Direct-Torque Neuro-Fuzzy Control of Induction Motor
徐君鹏; CHEN Yan-feng; LI Guo-hou
2007-01-01
Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. In order to improve the system stability and raise the response speed, a new control scheme, direct-torque neuro-fuzzy control for induction motor drive, was put forward. The design and tuning procedure have been described. Also, the improved stator flux estimation algorithm, which guarantees eccentric estimated flux has been proposed.
New concept of direct torque neuro-fuzzy control for induction motor drives. Simulation study
Grabowski, P.Z. [Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw (Poland)
1997-12-31
This paper presents a new control strategy in the discrete Direct Torque Control (DTC) based on neuro-fuzzy structure. Two schemes are proposed: neuro-fuzzy switching times calculator and neuro-fuzzy incremental controller with space vector modulator. These control strategies guarantee very good dynamic and steady-states characteristics, with very low sampling time and constant switching frequency. The proposed techniques are verified by simulation study of the whole drive system and results are compared with conventional discrete Direct Torque Control method. (orig.) 18 refs.
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-10-01
The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications.
Neuro-Fuzzy Phasing of Segmented Mirrors
Olivier, Philip D.
1999-01-01
A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.
Neuro-fuzzy Control of Integrating Processes
Anna Vasičkaninová
2011-11-01
Full Text Available Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perceptionof natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plantswith time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control.Designed fuzzy controllers lead to better closed-loop control responses then classical PID controllers.
Ahmed, Hameed Kaleel; Zulquernain, Mallick
2009-01-01
Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task.
无
2000-01-01
The neuro-fuzzy network (NFN) is used to model the rules and experience of the process planner.NFN is to select the manufacturing operations sequences for the part features. A detailed description of the NFN system development is given. The rule structure utilizes sigmoid functions to fuzzify the inputs, multiplication to combine the if part of the rules and summation to integrate the fired rules. Expert knowledge from previous process plans is used in determining the initial network structure and parameters of the membership functions. A back-propagation (BP) training algorithm was developed to fine tune the knowledge to company standards using the input-output data from executions of previous plans. The method is illustrated by an industrial example.
CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY
Shumkov Y. A.
2014-11-01
Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning
Location Estimation and Mobility Prediction Using Neuro-fuzzy Networks In Cellular Networks
Maryam Borna; Mohammad Soleimani
2011-01-01
In this paper an approach is proposed for location estimation, tracking and mobility prediction in cellular networks in dense urban areas using neural and neuro-fuzzy networks. In urban areas with high buildings, due to the effects of multipath fading and Non-Line-of-Sight conditions, the accuracy of positioning methods based on direction finding and ranging degrades significantly. Also in these areas, due to high user traffic there's a need for network resources management. Knowing the next ...
Rule weights in a neuro-fuzzy system with a hierarchical domain partition
Krzysztof Siminski
2010-01-01
Rule weights in a neuro-fuzzy system with a hierarchical domain partition The paper discusses the problem of rule weight tuning in neuro-fuzzy systems with parameterized consequences in which rule...
Neuro-fuzzy modeling in bankruptcy prediction
Vlachos D.
2003-01-01
Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.
Adaptive neuro-fuzzy controller of switched reluctance motor
Tahour Ahmed
2007-01-01
Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....
Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation
Auday Al-Mayyahi
2014-11-01
Full Text Available This article proposes an adaptive neuro-fuzzy inference system (ANFIS for solving navigation problems of an autonomous ground vehicle (AGV. The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD; right distance (RD and left distance (LD for the low-level motion control. Two heading controllers deploy the angle difference (AD between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.
Hybrid Neuro-Fuzzy Systems for Software Development Effort Estimation
Rama Sree P
2012-12-01
Full Text Available The major prevailing challenges for Software Projects are Software Estimations like cost estimation, effort estimation, quality estimation and risk analysis. Though there are several algorithmiccost estimation models in practice, each model has its own pros and cons for estimation. There is still a need to find a model that gives accurate estimates. This paper is an attempt to experiment different types of Neuro-Fuzzy Models. Using the types of Neuro-Fuzzy Models for software effort prediction is a relatively unexplored area. Two case studies are used for this purpose. The first is based on NASA-93dataset and the other is based on Maxwell-62 dataset. The case studies were analyzed using six different criterions like Variance Accounted For (VAF, Mean Absolute Relative Error (MARE, VarianceAbsolute Relative Error (VARE, Mean Balance Relative Error (Mean BRE, Mean Magnitude Relative Error (MMRE and Prediction. From the results and from reasoning, it is concluded that Type BCompensationNeuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are more linear. Type J Neuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are not linear.
MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
2015-08-02
16. SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6...Instance AdaptiveNeuro-Fuzzy Inference System We introduce a novel adaptive neuro -fuzzy architecture based on the framework of Multiple Instance Fuzzy...Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS
A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods
Giovanis, eleftheios
2008-01-01
The purpose of this paper is to present a neuro-fuzzy approach of financial distress pre-warning model appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) from 2002 through 2008. We present an adaptive neuro-fuzzy system with triangle and Gaussian membership functions. We conclude that neuro-fuzzy model presents almost perfect forecasts for financial distress periods as also...
A New Neuro-Fuzzy Adaptive Genetic Algorithm
ZHU Lili; ZHANG Huanchun; JING Yazhi
2003-01-01
Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model
Bogdan Gliwa
2011-01-01
Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.
SECURE ADHOC ROUTING FOR DATA TRANSFER USING NEURO FUZZY
Suganya; Nagarajan Srinivasan
2013-01-01
In the present world the security vulnerabilities are highly challenging in MANET. To get the maximum security and minimum threat there is lots of work going on. To effectively isolate the malicious node this paper proposes a Neuro fuzzy algorithm. By using fuzzy logic we can further improve the security level by identifying the malicious node more accurately. The concept behind the paper is as inreal life scenario, trust and sharing. Here in this paper we use the concept of trusting supporte...
Adaptive Neuro-fuzzy Controller Design for Non-affine Nonlinear Systems
JIA Li; GE Shu-zhi; QIU Ming-sen
2008-01-01
An adaptive neuro-fuzzy control is investigated for a class of noa-affine nonlinear systems.To do so,rigorous description and quantification of the approximation error of the neuro-fuzzy controller are firstly discussed.Applying this result and Lyapunov stability theory,a novel updating algorithm to adapt the weights,centers,and widths of the neuro-fuzzy controller is presented.Consequently,the proposed design method is able to guaranteg the stability of the closed-loop system and the convergence of the tracking error.Simulation results illustrate the effectiveness of the proposed adaptive neuro-fuzzy control scheme.
Zahra Mohammadi
2011-07-01
Full Text Available This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).
Estimating the crowding level with a neuro-fuzzy classifier
Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo
1997-07-01
This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.
A novel Neuro-fuzzy classification technique for data mining
Soumadip Ghosh
2014-11-01
Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.
Securing jammed network using reliability behavior value through neuro-fuzzy analysis
S Raja Ratna; R Ravi
2015-06-01
Wireless multi-hop networks are often exposed to serious physical layer jamming attack. In this attack, the jammer node corrupts the packet by injecting high level of noise and keeps the channel busy and thus blocks the legitimate communication. If multiple jammers collude together, this attack will become very severe. To prevent this attack, a simple yet effective Reliability Behavior Neuro-Fuzzy system has been proposed and it operates in three modules. In module one, each route node obtains its behavior value from the route path and neighboring paths using direct and indirect behavior observations. In module two, based on the behavior value, three factor identification methods have been presented to identify the reliability value of nodes. In module three, using the reliability value the route nodes are level positioned and classified into groups by a neuro-fuzzy classifier. By simulation studies, it is observed that the proposed scheme significantly not only identifies misbehaving nodes with higher detection rate and lower false positive and but also achieves higher network throughput and lower jamming throughput.
Application of adaptive neuro-fuzzy inference system in motor soft start%自适应神经模糊推理系统在电动机软启动中的应用
李冬辉; 王莹莹; 马禹新
2012-01-01
Aimed at addressing serious grid impact entirely due to the impact of electricity resulting from direct start of induction motor,this paper introduces the application of the adaptive neuro-fuzzy inference system to the control of motor soft start.The method renders it possible to give a fuller play to the ability of adaptive learning of neural networks and fuzzy inference without the need to master the exact model of the object,and finally achieve the intelligent control of motor.The method consists of using the relationship of motor speed,load torque and the firing angle as training samples,and applying the hybrid learning algorithm to adjust the premise parameters and conclusion parameters,generating the fuzzy rules automatically and building the adaptive neuro-fuzzy inference system,and generating the appropriate thyristor trigger angle according to the given motor speed and torque.The simulation analysis shows that,the adaptive neuro-fuzzy inference system after training can afford a better control of motor speed,and thus promises to make possible the soft start of fan or pump load motor.%异步电动机直接启动产生的冲击电流会造成严重的电网冲击,因此提出将自适应神经模糊推理系统应用到电动机软启动控制中,充分发挥神经网络自适应学习和模糊推理不要求掌握被控对象精确模型处理结构化知识的能力,实现电动机软启动的智能控制。利用电机转速、负载转矩、触发角的对应关系作为训练样本,采用混合学习算法调整前提参数和结论参数,自动产生模糊规则,构建自适应神经模糊推理系统,根据给定的电机转速和转矩产生合适的晶闸管触发角。经仿真分析,结果表明：训练构建的自适应神经模糊推理系统能够很好地进行电机的速度控制,可以实现风机或泵类负载电动机的软启动。
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects
Spek, van der Jaap H.; Velthuis, Wubbe J.R.; Veltink, Peter H.; Vries, de Theo J.A.
1996-01-01
The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects
van der Spek, J.H.; Velthuis, W.J.R.; Veltink, Petrus H.; de Vries, Theodorus J.A.
1996-01-01
The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller
Neuro-fuzzy generalized predictive control of boiler steam temperature
Xiangjie LIU; Jizhen LIU; Ping GUAN
2007-01-01
Power plants are nonlinear and uncertain complex systems.Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant.A nonlinear generalized predictive controller based on neuro-fuzzy network(NFGPC)is proposed in this paper.The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant.From the experiments on the plant and the simulation of the plant,much better performance than the traditional controller is obtained.
NEURO-FUZZY MODELLING OF BLENDING PROCESS IN CEMENT PLANT
Dauda Olarotimi Araromi
2015-11-01
Full Text Available The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX model was developed for comparison purpose. ARX model gave high root mean square error (RMSE of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.
Neuro-fuzzy predictive control for nonlinear application
CHEN Dong-xiang; WANG Gang; LV Shi-xia
2008-01-01
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.
Vaganova, E. V.; Syryamkin, M. V.
2015-11-01
The purpose of the research is the development of evolutionary algorithms for assessments of promising scientific directions. The main attention of the present study is paid to the evaluation of the foresight possibilities for identification of technological peaks and emerging technologies in professional medical equipment engineering in Russia and worldwide on the basis of intellectual property items and neural network modeling. An automated information system consisting of modules implementing various classification methods for accuracy of the forecast improvement and the algorithm of construction of neuro-fuzzy decision tree have been developed. According to the study result, modern trends in this field will focus on personalized smart devices, telemedicine, bio monitoring, «e-Health» and «m-Health» technologies.
Adaptive Neuro-Fuzzy Controller Experimental Design for DC Motor Connected to Unbalanced Load
Reza Nejati
2007-09-01
Full Text Available In two recent decades, fuzzy controllers have been used in controlling different systems successfully. In this article, a new method is given for controlling of permanent magnetic DC motor connected to unbalanced load. Imbalance of load leads to machine vibrations, fluctuation of power, making exhaustion in machine shaft, and equipment depreciation. In this article neuro-fuzzy controllers are used for controlling unbalanced load. Because of non-linear nature of load and machine, machine fluctuations are different in various speeds. For making controller adaptive with machine, using an artificial neural network, the input-output coefficients are be updated in any speed. Optimized coefficients obtained by using of direct search method, and with these coefficients, artificial neural network trained with Lauvenberg-Marcoardet method. Operational results obtained from developed system, shows the efficiency of given method.
A Neuro-Fuzzy System for Characterization of Arm Movements
Alexandre Balbinot
2013-02-01
Full Text Available The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours.
Tuning of a neuro-fuzzy controller by genetic algorithm.
Seng, T L; Bin Khalid, M; Yusof, R
1999-01-01
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.
Adaptive Neuro-fuzzy approach in friction identification
Zaiyad Muda @ Ismail, Muhammad
2016-05-01
Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.
Extraction of rules for faulty bearing classification by a Neuro-Fuzzy approach
Marichal, G. N.; Artés, Mariano; García Prada, J. C.; Casanova, O.
2011-08-01
In this paper, a classification system of faulty bearings based on a Neuro-Fuzzy approach is presented. The vibration signals in the frequency domain produced by the faulty bearings will be taken as the inputs to the classification system. In this sense, it is an essential characteristic for the used Neuro-Fuzzy approach, the possibility of taking a great number of inputs. The system consists of several Neuro-Fuzzy systems for determining different bearing status, along with a measurement equipment of the vibration spectral data. In this paper, a special attention is focused on the analysis of the rules obtained by the final Neuro-Fuzzy system. In fact, a rule extraction process and an interpretation rule process is discussed. Several trials have been carried out, taking into account the vibration spectral data collected by the measurement equipment, where satisfactory results have been achieved.
Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla
2014-12-01
This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.
High Torque, Direct Drive Electric Motor Project
National Aeronautics and Space Administration — Bear Engineering proposes to advance the development of an innovative high torque, low speed, direct drive motor in order to meet NASA's requirements for such...
High Torque, Direct Drive Electric Motor Project
National Aeronautics and Space Administration — Bear Engineering proposes to develop an innovative high torque, low speed, direct drive motor in order to meet NASA's requirements for such devices. Fundamentally,...
A.K. Parida
2016-09-01
Full Text Available In this paper Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system is presented for the prediction and analysis of financial and electrical energy market data. The normally used TSK-type feedforward fuzzy neural network is unable to take the full advantage of the use of the linear fuzzy rule base in accurate input–output mapping and hence the consequent part of the rule base is made nonlinear using polynomial or arithmetic basis functions. Further the Chebyshev polynomial functions provide an expanded nonlinear transformation to the input space thereby increasing its dimension for capturing the nonlinearities and chaotic variations in financial or energy market data streams. Also the locally recurrent neuro-fuzzy information system (LRNFIS includes feedback loops both at the firing strength layer and the output layer to allow signal flow both in forward and backward directions, thereby making the LRNFIS mimic a dynamic system that provides fast convergence and accuracy in predicting time series fluctuations. Instead of using forward and backward least mean square (FBLMS learning algorithm, an improved Firefly-Harmony search (IFFHS learning algorithm is used to estimate the parameters of the consequent part and feedback loop parameters for better stability and convergence. Several real world financial and energy market time series databases are used for performance validation of the proposed LRNFIS model.
Setia, Ronald; May, Gary S.
2006-02-01
Excimer laser ablation is used for microvia formation in the microelectronics packaging industry. With continuing advancement of laser systems, there is an increasing need to offset capital equipment investment and lower equipment downtime. This paper presents a neuro-fuzzy methodology for in-line failure detection and diagnosis of the excimer laser ablation process. Response data originating directly from laser tool sensors and the characterization of microvias were used as failure symptoms for potential deviations in four laser system parameters from their corresponding baseline values. The response characteristics consist of via diameter, via wall angle, and via resistance. Resistance measurements on copper deposited in the ablated vias were performed to characterize the degree to which debris remaining inside the vias affected quality. The laser system parameters include laser fluence, shot frequency, number of pulses, and helium pressure flow. The adaptive neuro-fuzzy inference system (ANFIS) was trained and subsequently validated for its capability in evidential reasoning using the data collected. Results indicated only a single false alarm occurred in 19 possible failure detection scenarios. In failure diagnosis, a single false alarm and a single missed alarm occurred.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Development of quantum-based adaptive neuro-fuzzy networks.
Kim, Sung-Suk; Kwak, Keun-Chang
2010-02-01
In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.
Estimation and Approximation Using Neuro-Fuzzy Systems
Nidhi Arora
2016-06-01
Full Text Available Estimation and Approximation plays an important role in planning for future. People especially the business leaders, who understand the significance of estimation, practice it very often. The act of estimation or approximation involves analyzing historical data pertaining to domain, current trends and expectations of people connected to it. Exercising estimation is not only complicated due to technological change in the world around, but also due to complexity of the problems. Traditional numerical based techniques for solution of ill-defined non-linear real world problems are not sufficient. Hence, there is a need of some robust methodologies which can deal with dynamic environment, imprecise facts and uncertainty in the available data to achieve practical applicability at low cost. Soft computing seeks to solve class of problems not suited for traditional algorithmic approaches. To address the common problems in business of inexactness, some models are put forward for servicing, support and monitoring by approximating and estimating important outcomes. This work illustrates some very general yet widespread problems which are of interest to common people. The suggested approaches can overcome the fuzziness in traditional methods by predicting some future events and getting better control on business. This includes study of various neuro-fuzzy architectures and their possible applications in various areas, where decision-making using classical methods fail.
Sensorless vector and direct torque control
Vas, Peter
1998-01-01
This is the first comprehensive book on sensorless high performance a.c. drives. It is essential reading for anyone interested in acquiring a solid background on sensorless torque-controlled drives. It presents a detailed and unified treatment of sensorless vector-controlled and direct-torque controlled drive systems. It also discusses the applications of artificial intelligence to drives. Where possible, space vector theory is used and emphasis is laid on detailed mathematical and physical analysis. Sensorless drive schemes for different types of permanent magnet synchronous motors, synchronous reluctance motors, and induction motors are also presented. These include more than twenty vector drives e.g. five types of MRAS-based vector drives, and eleven types of direct-torque-controlled (DTC) drives, e.g. the ABB DTC drive. However, torque-controlled switched reluctance motor drives are also discussed due to their emerging importance. The book also covers various drive applications using artificial intellige...
Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration
Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza
2003-01-01
Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
Merrikh-Bayat, Farnood
2011-01-01
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault-tolerant, all synaptic weights in our proposed method are always non-negative and there is no need to precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
Kim, J; Kasabov, N
1999-11-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.
A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance
Quang Hung Do
2013-01-01
Full Text Available Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.
A neuro-fuzzy approach in the classification of students' academic performance.
Do, Quang Hung; Chen, Jeng-Fung
2013-01-01
Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.
A Synergistic Effect in the Measurement of Neuro-Fuzzy System
Gorbachev Sergey
2016-01-01
Full Text Available We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.
Vipan K Sohpal
2014-06-01
Full Text Available Transesterification of Jatropha curcus for biodiesel production is a kinetic control process, which is complex in nature and controlled by temperature, the molar ratio, mixing intensity and catalyst process parameters. A precise choice of catalyst is required to improve the rate of transesterification and to simulate the kinetic study in a batch reactor. The present paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS approach to model and simulate the butyl ester production using alkaline catalyst (NaOH. The amounts of catalyst and time for reaction have been used as the model’s input parameters. The model is a combination of fuzzy inference and artificial neural network, including a set of fuzzy rules which have been developed directly from experimental data. The proposed modeling approach has been verified by comparing the expected results with the practical results which were observed and obtained through a batch reactor operation. The application of the ANFIS test shows which amount of catalyst predicted by the proposed model is suitable and in compliance with the experimental values at 0.5% level of significance.
NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR
Mohamed Ben Halima
2012-10-01
Full Text Available In this paper we propose a robust approach for text extraction and recognition from video clips which is called Neuro-Fuzzy system for Arabic Video OCR. In Arabic video text recognition, a number of noise components provide the text relatively more complicated to separate from the background. Further, the characters can be moving or presented in a diversity of colors, sizes and fonts that are not uniform. Added to this, is the fact that the background is usually moving making text extraction a more intricate process. Video include two kinds of text, scene text and artificial text. Scene text is usually text that becomes part of the scene itself as it is recorded at the time of filming the scene. But artificial text is produced separately and away from the scene and is laid over it at a later stage or during the post processing time. The emergence of artificial text is consequently vigilantly directed. This type of text carries with it important information that helps in video referencing, indexing and retrieval.
Ashwani Kharola
2016-07-01
Full Text Available This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulum (DIP on cart. A Matlab-Simulink model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vertical position while cart moves in horizontal direction. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec, maximum overshoot (degree and steady state error. The regression (R and mean square error (MSE values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.
Karami Alireza; Afiuni-Zadeh Somaieh
2013-01-01
One of the most important characters of blasting, a basic step of surface mining, is rock fragmentation because it directly effects on the costs of drilling and economics of the subsequent operations of loading, hauling and crushing in mines. Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks. We developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80%passing size (K80) of Golgohar iron mine of Sirjan, Iran. Comparing the results of ANFIS and RBF models shows that although the statistical parame-ters RBF model is acceptable but ANFIS proposed model is superior and also simpler because ANFIS model is constructed using only two input parameters while seven input parameters used for construction of RBF model.
Jie Zhang
2006-01-01
In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
Short-Term Electrical Load Forecasting using Neuro-Fuzzy Models
Park, Young Jin; Shim, Hyun Jeong; Wang, Bo Hyeun [Kang Nung National University (Korea)
2000-03-01
This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models, The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptions, radial basis function networks, and neuro-fuzzy models without the structure learning. (author). 23 refs., 11 figs., 8 tabs.
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.
Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
2017-07-01
The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.
Seied Yasser Nikoo
2016-11-01
Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.
Modeling of a HTPEM fuel cell using Adaptive Neuro-Fuzzy Inference Systems
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Sahlin, Simon Lennart
2015-01-01
In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) model of the voltage of a fuel cell is developed. The inputs of this model are the fuel cell temperature, current density and the carbon monoxide concentration of the anode supply gas. First an identification experiment which spans...
Achiche, S.; Shlechtingen, M.; Raison, M.
2016-01-01
This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped with ...
Petchinathan,G.; K. Valarmathi; Devaraj,D.; T. K. Radhakrishnan
2014-01-01
This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a G...
Machining process influence on the chip form and surface roughness by neuro-fuzzy technique
Anicic, Obrad; Jović, Srđan; Aksić, Danilo; Skulić, Aleksandar; Nedić, Bogdan
2017-04-01
The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.
Evaluation of Regression and Neuro_Fuzzy Models in Estimating Saturated Hydraulic Conductivity
J. Behmanesh
2015-06-01
Full Text Available Study of soil hydraulic properties such as saturated and unsaturated hydraulic conductivity is required in the environmental investigations. Despite numerous research, measuring saturated hydraulic conductivity using by direct methods are still costly, time consuming and professional. Therefore estimating saturated hydraulic conductivity using rapid and low cost methods such as pedo-transfer functions with acceptable accuracy was developed. The purpose of this research was to compare and evaluate 11 pedo-transfer functions and Adaptive Neuro-Fuzzy Inference System (ANFIS to estimate saturated hydraulic conductivity of soil. In this direct, saturated hydraulic conductivity and physical properties in 40 points of Urmia were calculated. The soil excavated was used in the lab to determine its easily accessible parameters. The results showed that among existing models, Aimrun et al model had the best estimation for soil saturated hydraulic conductivity. For mentioned model, the Root Mean Square Error and Mean Absolute Error parameters were 0.174 and 0.028 m/day respectively. The results of the present research, emphasises the importance of effective porosity application as an important accessible parameter in accuracy of pedo-transfer functions. sand and silt percent, bulk density and soil particle density were selected to apply in 561 ANFIS models. In training phase of best ANFIS model, the R2 and RMSE were calculated 1 and 1.2×10-7 respectively. These amounts in the test phase were 0.98 and 0.0006 respectively. Comparison of regression and ANFIS models showed that the ANFIS model had better results than regression functions. Also Nuro-Fuzzy Inference System had capability to estimatae with high accuracy in various soil textures.
Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety
Beinarovica, Anna; Gorobetz, Mikhail; Levchenkov, Anatoly
2017-09-01
The proposed study describes applying of neural network and fuzzy logic in transport control for safety improvement by evaluation of accidents’ risk by intelligent infrastructure devices. Risk evaluation is made by following multiple-criteria: danger, changeability and influence of changes for risk increasing. Neuro-fuzzy algorithms are described and proposed for task solution. The novelty of the proposed system is proved by deep analysis of known studies in the field. The structure of neuro-fuzzy system for risk evaluation and mathematical model is described in the paper. The simulation model of the intelligent devices for transport infrastructure is proposed to simulate different situations, assess the risks and propose the possible actions for infrastructure or vehicles to minimize the risk of possible accidents.
Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow
Hasiloglu, Abdulsamet [Department of Electronics and Telecommunications Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Yilmaz, Mehmet; Comakli, Omer [Department of Mechanical Engineering, Engineering Faculty, Ataturk University, Erzurum (Turkey); Ekmekci, Ismail [Department of Mechanical Engineering, Engineering Faculty, Sakarya University, Sakarya (Turkey)
2004-11-01
The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of transient heat transfer. An ANFIS has been applied for the transient heat transfer in thermally and simultaneously developing circular duct flow, subjected to a sinusoidally varying inlet temperature. The experiments covered Reynolds numbers in the 2528{<=}Re{<=}4265 range and inlet heat input in the 0.01{<=}{beta}{<=}0.96 Hz frequency range. The accuracy of predictions and the adaptability of the ANFIS were examined, and good predictions were achieved for the temperature amplitudes of the transient heat transfer in thermally and simultaneously developing circular duct flow. The results show that the neuro-fuzzy can be used for modeling transient heat transfer in ducts. The results obtained with the ANFIS are also compared to those of a multiple linear regression and a neural network with a multi-layered feed-forward back-propagation algorithm. (authors)
Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.
Reyneri, L M; Chiaberge, M; Lavagno, L
2000-06-01
We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures.
Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization
Boumediene ALLAOUA
2009-12-01
Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.
A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
Abraham, Ajith; Nath, Baikunth
2004-01-01
Neuro-fuzzy systems have attracted growing interest of researchers in various scientific and engineering areas due to the increasing need of intelligent systems. This paper evaluates the use of two popular soft computing techniques and conventional statistical approach based on Box--Jenkins autoregressive integrated moving average (ARIMA) model to predict electricity demand in the State of Victoria, Australia. The soft computing methods considered are an evolving fuzzy neural network (EFuNN) ...
ON THE DESIGN OF A NEURO-FUZZY CONTROLLER - APPLICATION TO THE CONTROL OF A BIOREACTOR
Joseph HAGGEGE; Mohamed BENREJEB; Pierre BORNE
2005-01-01
This paper presents a new methodological approach for the synthesis of a neuro-fuzzy controller,using an on-line learning procedure. A simple algebraic formulation of a Sugeno fuzzy inference system that ensures a coherent universe of discourse, making easy its interpretation by a human being,is proposed and implemented in the case of the control of a bioreactor, which is considered as a complex non linear process.
Edge Detection with Neuro-Fuzzy Approach in Digital Synthesis Images
Fatma ZRIBI
2016-04-01
Full Text Available This paper presents an enhanced Neuro-Fuzzy (NF Approach of edge detection with an analysis of the characteristic of the method. The specificity of our method is an enhancement of the learning database of the diagonal edges compared to the original learning database. The original inspired NF edge detection model uses just one image learning database realized by Emin Yuksel. The tests are accomplished in synthesis images with a noised one of 20% of Gaussian noise.
Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System
Yao, Feng; Yerima, Suleiman Y.; Kang, BooJoong; Sezer, Sakir
2017-01-01
As mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner.To illustrate the applic...
Ravi Samikannu
2011-01-01
Full Text Available Problem statement: The temperature control in plastic extrusion machine is an important factor to produce high quality plastic products. The first order temperature control system in plastic extrusion comprises of coupling effects, long delay time and large time constants. Controlling temperature is very difficult as the process is multistage process and the system coupled with each other. In order to conquer this problem the system is premeditated with neuro fuzzy controller using LabVIEW. Approach: The existing technique involved is conventional PID controller, Neural controller, mamdani type Fuzzy Logic Controller and the proposed method is neuro fuzzy controller. Results: Manifest feature of the proposed method is smoothing of undesired control signal of conventional PID, neural controller and mamdani type FLC controller. The software incorporated the LabVIEW graphical programming language and MATLAB toolbox were used to design temperature control in plastic extrusion system. Hence neuro fuzzy controller is most powerful approach to retrieve the adaptiveness in the case of nonlinear system. Conclusion: The tuning of the controller was synchronized with the controlled variable and allowing the process at its desired operating condition. The results indicated that the use of proposed controller improve the process in terms of time domain specification, set point tracking and also reject disturbances with optimum stability.
Neeraj Kumar Goyal
2007-01-01
Full Text Available Objective: To predict biochemical failure in localized prostate cancer after radical prostatectomy using preoperative variables. Materials and Methods: Twenty-six patients of early carcinoma of prostate underwent open retropubic radical prostatectomy from June 2002 to June 2006. Preoperative variables included age, family history, digital rectal examination, serum prostatic specific antigen (S. PSA, prostate biopsy Gleason score, MRI of pelvis variables like periprostatic extension, seminal vesical invasion, weight of gland and pathological stage. With application of neuro-fuzzy, these variables were fed into system as input and output, that is S. PSA at six months (predicted value was calculated. Neuro-fuzzy system is a system to combine fuzzy system with learning techniques derived from neural networks. Here, we applied Takagi Sugeno Kang model (TSK due to its close solution to our aim. All the patients were followed up for a minimum of six months. At six month S. PSA of all patients was done (observed value. Predicted and observed values were compared. Result: Predicted and observed values were plotted on 1:1 slop line. Coefficient of correlation was 0.9935. Conclusion: Coefficient of correlation is close to one. It indicates that the neuro-fuzzy is accurate in predicting biochemical failure in localized carcinoma of prostate after radical prostatectomy.
NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS
Indira Priya Ponnuvel
2014-01-01
Full Text Available In this study, a new link based classifier using neuro fuzzy logic has been proposed for analyzing the social behavior based on Weblog dataset. In this system, data are processed using a multistage structure. This system provides a diagnosis using a neuro fuzzy link based classifier that analyses the user’s behavior to specific diagnostic categories based on their cluster category in social networks. It uses random walks method to organize the labels. Since the links present in the social network graph frequently represent relationships among the users with respect to social contacts and behaviours, this work observes the links of the graph in order to identify the relationships represented in the graph between the users of the social network based on some new social network metrics and the past behaviour of the users. This work is useful to provide connection between consolidated features of users based on network data and also using the traditional metrics used in the analysis of social network users. From the experiments conducted in this research work, it is observed that the proposed work provides better classification accuracy due to the application of neuro fuzzy classification method in link analysis.
Evaluating Loans Using a Combination of Data Envelopment and Neuro-Fuzzy Systems
Rashmi Malhotra
2015-02-01
Full Text Available A business organization's objective is to make better decisions at all levels of the firm to improve performance. Typically organizations are multi-faceted and complex systems that use uncertain information. Therefore, making quality decisions to improve organizational performance is a daunting task. Organizations use decision support systems that apply different business intelligence techniques such as statistical models, scoring models, neural networks, expert systems, neuro-fuzzy systems, case-based systems, or simply rules that have been developed through experience. Managers need a decision-making approach that is robust, competent, effective, efficient, and integrative to handle the multi-dimensional organizational entities. The decision maker deals with multiple players in an organization such as products, customers, competitors, location, geographic structure, scope, internal organization, and cultural dimension [46]. Sound decisions include two important concepts: efficiency (return on invested resources and effectiveness (reaching predetermined goals. However, quite frequently, the decision maker cannot simultaneously handle data from different sources. Hence, we recommend that managers analyze different aspects of data from multiple sources separately and integrate the results of the analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA and fuzzy logic. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand fuzzy logic's main strength lies in handling imprecise data. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies. A major advantage of the DEA approach is that it clearly identifies the
Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna
1998-10-01
Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.
DIRECT TORQUE CONTROL FOR INDUCTION MOTOR USING INTELLIGENT TECHNIQUES
R.Toufouti
2007-09-01
Full Text Available In this paper, we propose two approach intelligent techniques of improvement of Direct Torque Control (DTC of Induction motor such as fuzzy logic (FL and artificial neural network (ANN, applied in switching select voltage vector .The comparison with conventional direct torque control (DTC, show that the use of the DTC_FL and DTC_ANN, reduced the torque, stator flux, and current ripples. The validity of the proposed methods is confirmed by the simulative results.
Alves, Antonio Carlos Pinto Dias
2000-09-01
A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)
Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model.
Georgina Cosma
Full Text Available The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA level, the biopsy most common tumor pattern (Primary Gleason pattern and the second most common tumor pattern (Secondary Gleason pattern in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD or Extra-Prostatic Disease (ED using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC, with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812. The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR
Adaptive Neuro-Fuzzy Modeling of Mechanical Behavior for Vertically Aligned Carbon Nanotube Turfs
Mohammad A1-Khedher; Charles Pezeshki; Jeanne McHale; GFritz Knorr
2011-01-01
Several characterization methods have been developed to investigate the mechanical and structural properties of vertically aligned carbon nanotubes (VACNTs). Establishing analytical models at nanoscale to interpret these properties is complicated due to the nonuniformity and irregularity in quality of as-grown samples.In this paper, we propose a new methodology to investigate the correlation between indentation resistance of multi-wall carbon nanotube (MWCNT) turfs, Raman spectra and the geometrical properties of the turf structure using adaptive neuro-fuzzy phenomenological modeling. This methodology yields a novel approach for modeling at the nanoscale by evaluating the effect of structural morphologies on nanomaterial properties using Raman spectroscopy.
Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models
Schlechtingen, Meik; Santos, Ilmar
2012-01-01
in graphical and text format. Within the paper examples of real faults are provided, showing the capabilities of the method proposed. The method can be applied both to existing and new built turbines without the need of any additional hardware installation or manufacturers input.......This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference...
A.A. Fahmy
2013-12-01
Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.
Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems
Zahra Atashbar Orang
2012-10-01
Full Text Available By ever increase in using computer network and internet, using Intrusion Detection Systems (IDS has been more important. Main problems of IDS are the number of generated alerts, alert failure as well as identifying the attack type of alerts. In this paper a system is proposed that uses Adaptive Neuro-Fuzzy Inference System to classify IDS alerts reducing false positive alerts and also identifying attack types of true positive ones. By the experimental results on DARPA KDD cup 98, the system can classify alerts, leading a reduction of false positive alerts considerably and identifying attack types of alerts in low slice of time.
Paulchamy Balaiah
2012-01-01
Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors
A Neuro-Fuzzy based System for Classification of Natural Textures
Jiji, G. Wiselin
2016-12-01
A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.
MITRAKIS NIKOLAOS; Mallinis, Giorgos; Koutsias, Nikos; Theocharis, J. B.
2012-01-01
In this study, we assess the performance of a self-organising neuro-fuzzy classifier for burned area mapping using multi-spectral satellite data. The proposed neuro-fuzzy model incorporates a multi-layered structure consisting of two types of nodes. The first type is a generic fuzzy neuron classifier (FNCs), whereas the second is solely a decision fusion operator. The Group Method of Data Handling algorithm is used for structure learning providing the model with self-organising attributes and...
Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.
2015-01-01
Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.
Karami Alireza; Afiuni-Zadeh Somaieh
2012-01-01
One of the most important characters of blasting,a basic step of surface mining,is rock fragmentation.It directly effects on the costs of drilling and economics of the subsequent operations of loading,hauling and crushing in mines.Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF)show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks.In this paper we developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80％ passing size (K80) of Golgohar iron ore mine of Sir jan,Iran.Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but the ANFIS proposed model is superior and also simpler because the ANFIS model is constructed using only two input parameters while seven input parameters used for construction of the RBF model.
Improved Torque Control Performance of Direct Torque Control for 5-Phase Induction Machine
Logan Raj Lourdes Victor Raj
2013-12-01
Full Text Available In this paper, the control of five-phase induction machine using Direct Torque Control (DTC is presented. The general D-Q model of five-phase induction machine is discussed. The de-coupled control of stator flux and electromagnetic torque based on hysteresis controller similar to conventional DTC is applied to maintain the simplicity of the system. Three sets of look-up tables consist of voltage vectors with different amplitude that selects the most optimal voltage vectors according motor operation condition is proposed. This provides excellent torque dynamic control, reduces torque ripple, lower switching frequency (high efficiency and extension of constant torque. Simulation results validate the improvement achieved.
G. Petchinathan
2014-06-01
Full Text Available This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT and an adaptive neuro-fuzzy inference system (ANFIS. The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC strategy are also presented.
UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID
Ali Moltajaei Farid
2013-01-01
Full Text Available ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper, an adaptive neuro-fuzzy inference system (ANFIS is employed to control an unmanned aircraft vehicle (UAV. First, autopilots structure is defined, and then ANFIS controller is applied, to control UAVs lateral position. The results of ANFIS and PID lateral controllers are compared, where it shows the two controllers have similar results. ANFIS controller is capable to adaptation in nonlinear conditions, while PID has to be tuned to preserves proper control in some conditions. The simulation results generated by Matlab using Aerosim Aeronautical Simulation Block Set, which provides a complete set of tools for development of six degree-of-freedom. Nonlinear Aerosonde unmanned aerial vehicle model with ANFIS controller is simulated to verify the capability of the system. Moreover, the results are validated by FlightGear flight simulator.
Mohammad Subhi Al-batah
2014-01-01
Full Text Available To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL and high-grade squamous intraepithelial lesion (HSIL. The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.
Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator
Srinivasan Alavandar
2008-01-01
Full Text Available The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy. This paper presents the control of six degrees of freedom robot arm (PUMA Robot using Adaptive Neuro Fuzzy Inference System (ANFIS based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers
Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network
Vitor Badiale Furlong
2013-02-01
Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.
Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set.
Diago, Luis; Kitaoka, Tetsuko; Hagiwara, Ichiro; Kambayashi, Toshiki
2011-12-01
Artificial neural networks are nonlinear techniques which typically provide one of the most accurate predictive models perceiving faces in terms of the social impressions they make on people. However, they are often not suitable to be used in many practical application domains because of their lack of transparency and comprehensibility. This paper proposes a new neuro-fuzzy method to investigate the characteristics of the facial images perceived as Iyashi by one hundred and fourteen subjects. Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined. In order to gain a clear insight into the reasoning made by the nonlinear prediction models such as holographic neural networks (HNN) in the classification of Iyashi expressions, the interpretability of the proposed fuzzy-quantized HNN (FQHNN) is improved by reducing the number of input parameters, creating membership functions and extracting fuzzy rules from the responses provided by the subjects about a limited dataset of 20 facial images. The experimental results show that the proposed FQHNN achieves 2-8% increase in the prediction accuracy compared with traditional neuro-fuzzy classifiers while it extracts 35 fuzzy rules explaining what characteristics a facial image should have in order to be classified as Iyashi-stimulus for 87 subjects.
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou
2011-01-01
Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.
A Neuro Fuzzy Technique for Process Grain Scheduling of Parallel Jobs
S. V. Sudha
2011-01-01
Full Text Available Problem statement: We present development of neural network based fuzzy inference system for scheduling of parallel Jobs with the help of a real life workload data. The performance evaluation of a parallel system mainly depends on how the processes are co scheduled? Various co scheduling techniques available are First Come First Served, Gang Scheduling, Flexible Co Scheduling and Agile Algorithm Approach: In order to use a wide range of objective functions, we used a rule bases scheduling strategy. The rule system depends on scheduling results of the agile algorithm and classifies all possible scheduling states and assigns an appropriate scheduling strategy based on actual state. The rule bases were developed with the help of a real workload data. Results: With the help of rule base results, scheduling was done again, which is compared with the first come first served, gang scheduling, flexible co scheduling and agile algorithm. The results of scheduling showed the optimized results of agile algorithm with the help of neuro fuzzy optimization technique. Conclusion: The study confirmed that the Neuro Fuzzy Technique can be used as a better optimization tool for optimizing any scheduling algorithm, This optimization tool is used for agile algorithm which is further used for process grain scheduling of parallel jobs.
NEURO FUZZY MODEL FOR FACE RECOGNITION WITH CURVELET BASED FEATURE IMAGE
SHREEJA R,
2011-06-01
Full Text Available A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometric techniques such as fingerprint or iris recognition systems. Every face has approximately 80 nodal points like (Distance between the eyes, Width of the nose etc.The basic face recognition system capture the sample, extract feature, compare template and perform matching. In this paper two methods of face recognition are compared- neural networks and neuro fuzzy method. For this curvelet transform is used for feature extraction. Feature vector is formed by extracting statistical quantities of curve coefficients. From the statistical results it is concluded that neuro fuzzy method is the better technique for face recognition as compared to neural network.
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2013-01-01
This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS......, or fuel cell diagnostics systems....
Heddam, Salim
2014-01-01
This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2013-01-01
This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H2, CO2, CO and CH3OH mass flows of the reformed gas. The ANFIS...
Novel torque ripple minimization algorithm for direct torque control of induction motor drive
LONG Bo; GUO Gui-fang; HAO Xiao-hong; LI Xiao-ning
2009-01-01
To elucidate the principles of notable torque and flux ripple during the steady state of the conventional direct torque control (DTC) of induction machines, the factors of influence torque variation are examined. A new torque ripple minimization algorithm is proposed. The novel method eradicated the torque ripple by imposing the required stator voltage vector in each control cycle. The M and T axial components of the stator voltage are accomplished by measuring the stator flux error and the expected incremental value of the torque at every sampling time. The maximum angle rotation allowed is obtained. Experimental results showed that the proposed method combined with the space vector pulse width modulation(SVPWM) could be implemented in most existing digital drive controllers, offering high performance in both steady and transient states of the induction drives at full speed range. The result of the present work imphes that torque fluctuation could be eliminated by imposing proper stator voltage, and the proposed scheme could not only maintain constant switching frequency for the inverter, but also solve the heating problem and current harmonics in traditional induction motor drives.
A neuro-fuzzy controlling algorithm for wind turbine
Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)
1995-12-31
The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)
Direct Torque Control of IPMSM to Improve Torque ripple and Efficiency based on Fuzzy Controller
B. Mirzaeian Dehkordi
2012-09-01
Full Text Available In this paper, a stator-flux-reference frame control method is proposed in order to control the speed and torque of an Interior Permanent Magnet Synchronous Machine (IPMSM in different loads condition. Direct Torque Control method (DTC based on Space Vector Modulation (SVM is used for control of IPMSM. In the proposed control method, conventional PI controller is used for controlling the stator flux, and torque of the motor. Also, a fuzzy controller is considered to improve the dynamic performance of DTC technique for speed control. In comparison to the conventional reference flux controller methods, this method, in addition, improves the torque profile of the motor drive. Moreover, it reduces copper losses. Simulation results for a 240V, 120A, 2500rpm, IPMSM confirm the appropriate performances of the method.
Abdelli, R; Rekioua, D; Rekioua, T
2011-04-01
This paper describes a torque ripple reduction technique with constant switching frequency for direct torque control (DTC) of an induction motor (IM). This method enables a minimum torque ripple control. In order to obtain a constant switching frequency and hence a torque ripple reduction, we propose a control technique for IM. It consists of controlling directly the electromagnetic torque by using a modulated hysteresis controller. The design methodology is based on space vector modulation (SVM) of electrical machines with digital vector control. MATLAB simulations supported with experimental study are used. The simulation and experimental results of this proposed algorithm show an adequate dynamic to IM; however, the research can be extended to include synchronous motors as well. The implementation of the proposed algorithm is described. It doesn't require any PI controller in the torque control loop. The hardware inverter is controlled digitally using a Texas Instruments TMS320F240 digital signal processor (DSP) with composed C codes for generating the required references. The results obtained from simulation and experiments confirmed the feasibility of the proposed strategy compared to the conventional one.
Simulation of neuro-fuzzy model for optimization of combine header setting
S Zareei
2016-09-01
Full Text Available Introduction The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses. Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses. On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester. In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss. Materials and Methods The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI (forward speed of combine harvester divided by peripheral speed of reel (1, 1.2, 1.5, three levels of cutting height (CH(25, 30, 35 cm, three
Direct Torque Control With Feedback Linearization for Induction Motor Drives
Lascu, Cristian Vaslie; Jafarzadeh, Saeed; Fadali, Sami M.
2017-01-01
This paper describes a direct-torque-controlled (DTC) induction motor (IM) drive that employs feedback linearization and sliding-mode control (SMC). A new feedback linearization approach is proposed, which yields a decoupled linear IM model with two state variables: torque and stator flux magnitude....... This intuitive linear model is used to implement a DTC-type controller that preserves all DTC advantages and eliminates its main drawback, the flux and torque ripple. Robust, fast, and ripple-free control is achieved by using SMC with proportional control in the vicinity of the sliding surface. SMC assures...... robustness as in DTC, while the proportional component eliminates the torque and flux ripple. The torque time response is similar to conventional DTC and the proposed solution is flexible and highly tunable due to the P component. The controller design is presented, and its robust stability is analyzed...
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata
2014-12-01
In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root
Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.
Ahmadieh, Hajar; Asl, Babak Mohammadzadeh
2017-04-01
We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its
Luis D Lledó
Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
Exploration of the Adaptive Neuro - Fuzzy Inference System Architecture and its Applications
Okereke Eze Aru
2016-09-01
Full Text Available In this paper we exhibited an architecture and essential learning process basic in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In genuine figuring environment, soft computing techniques including neural network, fuzzy logic algorithms have been generally used to infer a real choice utilizing given input or output information traits, ANFIS can build mapping taking into account both human learning and hybrid algorithms. This study includes investigation of ANFIS methodology. ANFIS procedure is utilized to display nonlinear functions, to control a standout amongst the most essential parameters of the impelling machine and anticipate a turbulent time arrangement, all yielding more viable, quicker result.
APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS
ELEFTHERIOS GIOVANIS
2011-02-01
Full Text Available In the current study we examine the effects of interest rate changes on common stock returns of Greek banking sector. We examine theGeneralized Autoregressive Heteroskedasticity (GARCH process and an Adaptive Neuro-Fuzzy Inference System (ANFIS. The conclusions of our findings are that the changes of interest rates, based on GARCH model, are insignificant on common stock returns during the period we examine. On the other hand, with ANFIS we can get the rules and in each case we can have positive or negative effects depending on the conditions and the firing rules of inputs, which information is not possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both models and we conclude that ANFIS outperforms GARCH model in both in-sample and out-of-sample periods.
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.
Ahmed, Sk Saddam; Dey, Nilanjan; Ashour, Amira S; Sifaki-Pistolla, Dimitra; Bălas-Timar, Dana; Balas, Valentina E; Tavares, João Manuel R S
2017-01-01
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
A neuro-fuzzy controller for xenon spatial oscillations in load-following operation
Na, Man Gyun [Chosun University, Kwangju (Korea, Republic of); Upadhyaya, Belle R. [The University of Tennessee, Knoxville (United States)
1997-12-31
A neuro-fuzzy control algorithm is applied for xenon spatial oscillations in a pressurized water reactor. The consequent and antecedent parameters of the fuzzy rules are tuned by the gradient descent method. The reactor model used for computer simulations is a two-point xenon oscillation model. The reactor core is axially divided into two regions and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very responses to a step or a ramp change of target axial offest without any residual flux oscillations. 9 refs., 5 figs. (Author)
Active Head Motion Compensation of TMS Robotic System Using Neuro-Fuzzy Estimation
Wan Zakaria W.N.
2016-01-01
Full Text Available Transcranial Magnetic Stimulation (TMS allows neuroscientist to study human brain behaviour and also become an important technique for changing the activity of brain neurons and the functions they sub serve. However, conventional manual procedure and robotized TMS are currently unable to precisely position the TMS coil because of unconstrained subject’s head movement and excessive contact force between the coil and subject’s head. This paper addressed this challenge by proposing an adaptive neuro-fuzzy force control to enable low contact force with a moving target surface. A learning and adaption mechanism is included in the control scheme to improve position disturbance estimation. The results show the ability of the proposed force control scheme to compensate subject’s head motions while maintaining desired contact force, thus allowing for more accurate and repeatable TMS procedures.
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
无
2007-01-01
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.
Decision Support System for the Intelligient Identification of Alzheimer using Neuro Fuzzy logic
Obi J.C
2011-05-01
Full Text Available Alzheimer Disease (AD is a form of dementia; it is a progressive, degenerative disease. Alzheimer is abrain disease that causes problems with memory, thinking and behavior. It is severe enough to interferewith daily activities. Alzheimer symptoms are characterized by memory loss that affects day-to-dayfunction, difficulty performing familiar tasks, problems with language, disorientation of time and place,poor or decreased judgment, problems with abstract thinking, misplacing things, changes in mood andbehavior, changes in personality and loss of initiative. Neuro-Fuzzy Logic explores approximationtechniques from neural networks to find the parameter of a fuzzy system. In this paper, the traditionalprocedure for the medical diagnosis of Alzheimer employed by physician is analyzed using neuro-fuzzyinference procedure. The proposed system is a useful decision support approach for the diagnosis ofAlzheimer.
FPGA implementation of neuro-fuzzy system with improved PSO learning.
Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali
2016-07-01
This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.
Neuro fuzzy force control for soft dry contact Hertzian ultrasonic probe
Gallegos, E.; Baltazar, A.; Treesatayapun, C.
2016-02-01
In this work the use of a cartesian robotic manipulator as scanner for the automated identification of hidden defects in an aluminum test plate is proposed. The robotic manipulator includes a custom made soft deformable ultrasonic probe and a force sensor for the recollection of the ultrasonic signals and force feedback. The contact between the soft probe and the test plate is regulated using a Neuro Fuzzy controller in order to avoid the complex mathematical model produced by the interaction. Finally the use of the correlation coefficient is proposed for the post processing of the obtained ultrasonic signals and identification of hidden defects inside the test plate. Experimental studies demonstrated the efficiency of the method.
Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system
Pourmahyabadi, M.; Mohammad Nejad, S.
2009-10-01
The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.
Favieiro, Gabriela W; Balbinot, Alexandre
2011-01-01
The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.
Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T; Kiaei, Ali A
2010-01-01
In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation of the facial expressions, and 3) by continuous values of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vect...
A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis
Mumini Olatunji Omisore
2017-01-01
Full Text Available Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria, shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.
Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela
Hurtado, Nuri; Torres, Julio
2014-01-01
The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.
PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
Ria Chaniago
2014-01-01
Full Text Available Weather is one of the nature elements that can influence decision making in human's life. Based on that issue, the author wants to make an application that is able to predict weather with good accuracy. The application is a weather forecasting system, using computer technology that implements expert system. The methods used are Adaptive Neuro Fuzzy Inference System (ANFIS and Case Based Reasoning (CBR, and a combination of both methods will applied to the system. The system also has learning methods like Backpropagation Error (BPE and Recursive Least Error (RLSE, to increase its accuracy. Clustering and data cleaning also done inside the system, as it needed by forecasting process to achieve a good result. K-Means is the clustering algorithm, while Box and Whisker Plot is the algorithm for data cleaning. The result from this project is to create a weather forecasting system with high accuracy.
Recognition of Handwritten Arabic words using a neuro-fuzzy network
Boukharouba, Abdelhak; Bennia, Abdelhak
2008-06-01
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure
Ashraf Ahmed Fahmy
2014-03-01
Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.
Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images
M. H. Olyaee
2013-10-01
Full Text Available One of the most important topics in image processing is edge detection. Many methods have been proposed for this end but most of them have weak performance in noisy images because noise pixels are determined as edge. In this paper, two new methods are represented based on Hierarchical Adaptive Neuro Fuzzy Systems (HANFIS. Each method consists of desired number of HANFIS operators that receive the value of some neighbouring pixels and decide central pixel is edge or not. Simple train images are used in order to set internal parameters of each HANFIS operator. The presented methods are evaluated by some test images and compared with several popular edge detectors. The experimental results show that these methods are robust against impulse noise and extract edge pixels exactly.
Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields
Geraldo A. R. Ramos
2017-06-01
Full Text Available In this work, a neuro-fuzzy (NF simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL and the learning capability of neural network (NN to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.
C.F. Wu
2013-04-01
Full Text Available The aim of this article attempts to propose an advanced design of driver assistance system which can provide the driver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehicle has a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detection of lane lines is implemented with methods based on image processing techniques. The candidates for lateral vehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length, width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trained recurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when the vehicle is driving on Taiwan’s highway, and the experimental results show it works well for different road conditions and for multiple vehicles.
C. F. Wu
2013-03-01
Full Text Available The aim of this article attempts to propose an advanced design of driver assistance system which can provide thedriver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehiclehas a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detectionof lane lines is implemented with methods based on image processing techniques. The candidates for lateralvehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length,width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trainedrecurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when thevehicle is driving on Taiwan’s highway, and the experimental results show it works well for different road conditionsand for multiple vehicles.
Neuro-Fuzzy based Controller for a Three- Phase Four-Wire Shunt Active Power Filter
Mridul Jha
2011-10-01
Full Text Available This paper describes the application of a novel neuro-fuzzy based control strategy which is used in order to improve the Active Power Filter (APF dynamics to minimize the harmonics for wide range of variations of load current under various conditions. To improve dynamic behavior of a three phase four-wire shunt active power filter and its robustness under range of load variations, adaptive hysteresis band with instantaneous p-q theory is used with the inclusion of neural network filter for reference current generation and fuzzy logic controller for DC voltage control. The proposed control scheme for “split-capacitor” converter topology is simple and also capable of maintaining the compensated line currents balanced, irrespective of unbalancing in the source voltages & deviation in the capacitor voltages. The results presented in MATLAB-SIMULINK software in this paper clearly reflect the effectiveness of the proposed APF to meet the IEEE-519 standard recommendations on harmonic levels.
A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors
Evelio José González
2009-12-01
Full Text Available In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case.
Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
Potential of neuro-fuzzy methodology to estimate noise level of wind turbines
Nikolić, Vlastimir; Petković, Dalibor; Por, Lip Yee; Shamshirband, Shahaboddin; Zamani, Mazdak; Ćojbašić, Žarko; Motamedi, Shervin
2016-01-01
Wind turbines noise effect became large problem because of increasing of wind farms numbers since renewable energy becomes the most influential energy sources. However, wind turbine noise generation and propagation is not understandable in all aspects. Mechanical noise of wind turbines can be ignored since aerodynamic noise of wind turbine blades is the main source of the noise generation. Numerical simulations of the noise effects of the wind turbine can be very challenging task. Therefore in this article soft computing method is used to evaluate noise level of wind turbines. The main goal of the study is to estimate wind turbine noise in regard of wind speed at different heights and for different sound frequency. Adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the wind turbine noise levels.
Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system
Doaa M. Atia
2017-05-01
Full Text Available The greenhouse is a complicated nonlinear system, which provides the plants with appropriate environmental conditions for growing. This paper presents a design of a control system for a greenhouse using geothermal energy as a power source for heating system. The greenhouse climate control problem is to create a favourable environment for the crop in order to reach predetermined results for high yield, high quality and low costs. Four controller techniques; PI control, fuzzy logic control, artificial neural network control and adaptive neuro-fuzzy control are used to adjust the greenhouse indoor temperature at the required value. MATLAB/SIMULINK is used to simulate the different types of controller techniques. Finally a comparative study between different control strategies is carried out.
An intelligent load shedding scheme using neural networks and neuro-fuzzy.
Haidar, Ahmed M A; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini; Masoum, Mohammad
2009-12-01
Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper.
Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems
Syed Zulqadar Hassan
2017-03-01
Full Text Available An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.
REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
Hartono Hartono
2016-01-01
Full Text Available Abstract The amount of inventory is determined on the basis of the demand. So that users can know the demand forecasts need to be done on the request. This study uses the data to implement a replacement parts on the electronic module production equipment in the telecommunications transmission systems, switching, access and power, ie by replacing the electronic module in the system is trouble or damaged parts of a good electronic module spare parts inventory, while the faulty electronic modules shipped to the Repair Center for repaired again, so that the results of these improvements can replenish spare part inventory. Parameters speed on improvement process of electronic module broken (repaired, in the form of an average repair time at the repair centers, in order to get back into the electronic module that is ready for used as spare parts in compliance with the safe supply inventory warehouse. This research using the method of Adaptive Neuro Fuzzy Inference System (ANFIS in developing a decision support system for inventory control of spare parts available in Warehouse Inventory taking into account several parameters supporters, namely demand, improvement and fulfillment of spare parts and repair time. This study uses a recycling input parameter repair faulty electronic module of the customer to immediately replace the module in inventory warehouse, do improvements in the Repair Center. So the acceleration restoration factor is very influential as the input spare parts inventory supply in the warehouse and using the Adaptive Neuro-Fuzzy Inference System (ANFIS method. Keywords: ANFIS, inventory control, replacement
Torque Ripple Reduction in Direct Torque Control Based Induction Motor using Intelligent Controllers
Sudhakar, Ambarapu; Vijaya Kumar, M.
2015-09-01
This paper presents intelligent control scheme together with conventional control scheme to overcome the problems with uncertainties in the structure encountered with classical model based design of induction motor drive based on direct torque control (DTC). It allows high dynamic performance to be obtained with very simple hysteresis control scheme. Direct control of the torque and flux is achieved by proper selection of inverter voltage space vector through a lookup table. This paper also presents the application of intelligent controllers like neural network and fuzzy logic controllers to control induction machines with DTC. Intelligent controllers are used to emulate the state selector of the DTC. With implementation of intelligent controllers the system is also verified and proved to be operated stably with reduced torque ripple. The proposed method validity and effectiveness has been verified by computer simulations using Matlab/Simulink®. These results are compared with the ones obtained with a classical DTC using proportional integral speed controller.
Direct torque control with feedback linearization for induction motor drives
Lascu, Cristian; Jafarzadeh, Saeed; Fadali, Sami M.
2015-01-01
This paper describes a Direct Torque Controlled (DTC) Induction Machine (IM) drive that employs feedback linearization and sliding-mode control. A feedback linearization approach is investigated, which yields a decoupled linear IM model with two state variables: torque and stator flux magnitude...... of the sliding surface. The VSC component assures robustness as in DTC, while the proportional component eliminates the torque and flux ripple. The torque time response is similar to DTC and the proposed solution is flexible and highly tunable due to the proportional controller. The controller design and its...... robust stability analysis are presented. The sliding controller is compared with a linear DTC scheme, and experimental results for a sensorless IM drive validate the proposed solution....
Improved direct torque control of induction motor with dither injection
R K Behera; S P Das
2008-10-01
In this paper, a three-level inverter-fed induction motor drive operating under Direct Torque Control (DTC) is presented. A triangular wave is used as dither signal of minute amplitude (for torque hysteresis band and ﬂux hysteresis band respectively) in the error block. This method minimizes ﬂux and torque ripple in a three-level inverter fed induction motor drive while the dynamic performance is not affected. The optimal value of dither frequency and magnitude is found out under free running condition. The proposed technique reduces torque ripple by 60% (peak to peak) compared to the case without dither injection, results in low acoustic noise and increases the switching frequency of the inverter. A laboratory prototype of the drive system has been developed and the simulation and experimental results are reported.
Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2004-07-01
The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)
Novel Design for Direct Torque Control System of PMSM
HUANG Xu-chao
2013-04-01
Full Text Available Nowadays, with the rapid development of high-performance servo system, The conventional permanent magnet synchronous motor (PMSM Direct Torque Control (DTC system has large torque ripple in low speed which cannot be well adapted to today`s development. The main reason is because the number of voltage vectors provided by the two-level inverter is only six and the relationship between voltage vector and torque is not clear[1-5.10-12]. In this paper, the basic concept of direct torque control of permanent magnet synchronous motor is investigated in order to emphasize the effects produced by a given voltage vector on stator and torque variations in this paper. Modified the voltage sector switching table, a novel DTC scheme for the permanent magnet synchronous motor is proposed which is using a novel three-level inverter. An improvement of the drive performance can be obtained by using the novel DTC scheme. The simulation results showed that the scheme could reduce the torque ripple in low speed and improved the stability of the motor under the condition of keeping the system dynamic performance.
Improved Torque Control Performance in Direct Torque Control using Optimal Switching Vectors
Muhd Zharif Rifqi Zuber Ahmadi
2015-02-01
Full Text Available This paper presents the significant improvement of Direct Torque Control (DTC of 3-phases induction machine using a Cascaded H-Bidge Multilevel Inverter (CHMI. The largest torque ripple and variable switching frequency are known as the major problem founded in DTC of induction motor. As a result, it can diminish the performance induction motor control. Therefore, the conventional 2-level inverter has been replaced with CHMI the in order to increase the performance of the motor either in dynamic or steady-state condition. By using the multilevel inverter, it can produce a more selection of the voltage vectors. Besides that, it can minimize the torque ripple output as well as increase the efficiency by reducing the switching frequency of the inverter. The simulation model of the proposed method has been developed and tested by using Matlab software. Its improvements were also verified via experimental results.
Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somaveh HEKMATI VAHED
2012-01-01
This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system.A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT),which is an incremental tree-based learning algorithm.The proposed NF models are compared with other known intelligent identifiers,namely multilayer perceptron (MLP) and radial basis function (RBF).Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system.Experimental results show the effectiveness of our proposed NF modelling approach.
Design and Comparison Direct Torque Control Techniques for Induction Motors
Blaabjerg, Frede; Kazmierkowski, Marian P.; Zelechowski, Marcin
2005-01-01
In this paper a comparison of two significant control methods of induction motor are presented. The first one is a classical Direct Torque and Flux Control (DTC) and is compared with a scheme, which uses Space Vector Modulator (DTC-SVM). A comparison in respect to dynamic and steady state...
IPMSM Motion-Sensorless Direct Torque and Flux Control
Pitict, Christian Ilie; Andreescu, Gheorghe-Daniel; Blaabjerg, Frede
2005-01-01
The paper presents a rather comprehensive implementation of a wide speed motion-sensorless control of IPMSM drives via direct torque and flux control (DTFC) with space vector modulation (SVM). Signal injection with only one D-module vector filter and phase-locked loop (PLL) observer is used at low...
IPMSM Motion-Sensorless Direct Torque and Flux Control
Pitict, Christian Ilie; Andreescu, Gheorghe-Daniel; Blaabjerg, Frede
2005-01-01
The paper presents a rather comprehensive implementation of a wide speed motion-sensorless control of IPMSM drives via direct torque and flux control (DTFC) with space vector modulation (SVM). Signal injection with only one D-module vector filter and phase-locked loop (PLL) observer is used at low...
Research on Direct Torque Control System Based on Induction Motor
康劲松; 陶生桂; 毛明平
2003-01-01
The mathematic model of direct torque control (DTC) was deduced. Two simulating models based on the MATLAB & SIMULINK were established. The emphasis is focused on study of the performance difference of the DTC system with stator flux hexagon and circle trajectories. The simulation waveforms of flux, torque and current characters with two flux trajectories were given. Experiments were carried out in an AC drive system based on induction motor and two-level inverter. A dual-CPU structure was used and the communication with two CPUs was obtained by a dual-port RAM in this system.
STATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC
Fatih Korkmaz
2012-07-01
Full Text Available The Direct Torque Control (DTC is well known as an effective control technique for high performance drives in a wide variety of industrial applications and conventional DTC technique uses two constant reference value: torque and stator flux. In this paper, fuzzy logic based stator flux optimization technique for DTC drives that has been proposed. The proposed fuzzy logic based stator flux optimizer self-regulates the stator flux reference using induction motor load situation without need of any motor parameters. Simulation studies have been carried out with Matlab/Simulink to compare the proposed system behaviors at vary load conditions. Simulation results show that the performance of the proposed DTC technique has been improved and especially at low-load conditions torque ripple are greatly reduced with respect to the conventional DTC.
Application of Space Vector Modulation in Direct Torque Control of PMSM
Michal Malek
2008-01-01
Full Text Available The paper deals with an improvement of direct torque control method for permanent magnet synchronous motor drives. Electrical torque distortion of the machine under original direct torque control is relatively high and if proper measures are taken it can be substantially decreased. The proposed solution here is to combine direct torque control with the space vector modulation technique. Such approach can eliminate torque distortion while preserving the simplicity of the original method.
An adaptive neuro fuzzy model for estimating the reliability of component-based software systems
Kirti Tyagi
2014-01-01
Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.
Prediksi Penjualan Barang Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS
Allyna Virrayyani
2016-12-01
Full Text Available Prediksi penjualan barang merupakan salah satu cara untuk menjaga stabilitas penjualan barang. Hasil prediksi yang diperoleh dapat dijadikan sebagai pertimbangan untuk mengambil keputusan dalam perencanaan manajemen bisnis. Salah satu metode yang dapat digunakan untuk prediksi adalah Adaptive Neuro-Fuzzy Inference System (ANFIS. Di dalam penelitian ini, ANFIS diimplementasikan dalam sebuah aplikasi sistem prediksi penjualan barang. Prosedur prediksi menggunakan analisis runtun waktu. Aturan ANFIS menggunakan model fuzzy Takagi-Sugeno dan fungsi keanggotaan tipe Generalized bell dengan 2 data masukan untuk 1 data target. Dari hasil pelatihan dan pengujian ANFIS untuk penjualan Beras Delanggu Raja, diperoleh nilai Mean Absolute Persentage (MAPE pelatihan sebesar 9.4180332828% dan diperoleh nilai MAPE pengujian sebesar 7.5343642644%. Hasil MAPE pengujian tersebut kurang dari batas toleransi error, yaitu 20 %. Batas toleransi tersebut berdasarkan penafsiran Batey dan Friedrich di mana MAPE < 10% merupakan perkiraan yang sangat baik dan 10% < MAPE < 20% merupakan perkiraan yang baik. ANFIS berhasil memprediksi penjualan Beras Delanggu Raja pada bulan yang akan datang dengan total 4944. Aplikasi sistem telah diuji menggunakan pengujian black-box. Seluruh prosedur pengujian dinyatakan berhasil.
Manjunatha K.C.
2015-03-01
Full Text Available A computer vision-based automated fire detection and suppression system for manufacturing industries is presented in this paper. Automated fire suppression system plays a very significant role in Onsite Emergency System (OES as it can prevent accidents and losses to the industry. A rule based generic collective model for fire pixel classification is proposed for a single camera with multiple fire suppression chemical control valves. Neuro-Fuzzy algorithm is used to identify the exact location of fire pixels in the image frame. Again the fuzzy logic is proposed to identify the valve to be controlled based on the area of the fire and intensity values of the fire pixels. The fuzzy output is given to supervisory control and data acquisition (SCADA system to generate suitable analog values for the control valve operation based on fire characteristics. Results with both fire identification and suppression systems have been presented. The proposed method achieves up to 99% of accuracy in fire detection and automated suppression.
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin
2014-01-01
Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems
A. Aldair
2010-12-01
Full Text Available The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλ Dμ (FOPID controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.
Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.
Cakmakci, Mehmet
2007-09-01
Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield.
An Improvement of Empirical Risk Functional in Neuro-Fuzzy Classifier
Elham Zamani
2013-09-01
Full Text Available This paper suggests a new method to improve of Empirical Risk Functional . Empirical Risk Functional acts as cost function for training neuro-fuzzy classifiers. Empirical risk minimization seeks the function that best fits the training data and it is equivalent to maximum likelihood estimation. The name of this cost function is Approximate Differentiable Empirical Risk Functional (ADERF.This function enables us to use a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Statistical Learning Theory can be applied. Also there is a learning algorithm based on ADERF. With our new method,more component of output vector of fuzzy classifier map to 1.By evaluating the effects of the proposed method, we can see the convergence speed of the learning algorithm and the classification accuracy are improved,and causes improved ADERF. The effects of improved ADERF, was illustrated. Experimental results on a number of benchmark classification tasks and comparison between approaches are provided
Performance analysis of electronic power transformer based on neuro-fuzzy controller.
Acikgoz, Hakan; Kececioglu, O Fatih; Yildiz, Ceyhun; Gani, Ahmet; Sekkeli, Mustafa
2016-01-01
In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 Vrms AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
A transfer learning framework for traffic video using neuro-fuzzy approach
P M ASHOK KUMAR; V VAIDEHI
2017-09-01
One of the main challenges in the Traffic Anomaly Detection (TAD) system is the ability to deal with unknown target scenes. As a result, the TAD system performs less in detecting anomalies. This paper introduces a novelty in the form of Adaptive Neuro-Fuzzy Inference System-Lossy-Count-based Topic Extraction (ANFIS-LCTE) for classification of anomalies in source and target traffic scenes. The process of transforming the input variables, learning the semantic rules in source scene and transferring the model to target scene achieves the transfer learning property. The proposed ANFIS-LCTE transfer learning model consists offour steps. (1) Low level visual items are extracted only for motion regions using optical flow technique. (2)Temporal transactions are created using aggregation of visual items for each set of frames. (3) An LCTE is applied for each set of temporal transaction to extract latent sequential topics. (4) ANFIS training is done with the back-propagation gradient descent method. The proposed ANFIS model framework is tested on standard dataset and performance is evaluated in terms of training performance and classification accuracies. Experimental results confirm that the proposed ANFIS-LCTE approach performs well in both source and targetdatasets.
Designing a Battlefield Fire Support System Using Adaptive Neuro-Fuzzy Inference System Based Model
Kerim Goztepe
2014-07-01
Full Text Available Fire support of the maneuver operation is a continuous process. It begins with the receiving the task by the maneuver commander and continues until the mission is completed. Yet it is a key issue in combat in the way gain success. Therefore, a real-time mannered solution to fire support problem is a vital component of tactical warfare to the sequence that auxiliary forces or logistic support arrives at the theatre. A new method for deciding on combat fire support is proposed using adaptive neuro-fuzzy inference system (ANFIS in this paper. This study addresses the design of an ANFIS as an efficient tool for real-time decision-making in order to produce the best fire support plan in battlefield. Initially, criteria that are determined for the problem are formed by applying ANFIS method. Then, the ANFIS structure is built up by using the data related to selected criteria. The proposed method is illustrated by a sample fire support planning in combat. Results showed us that ANFIS is valid especially for small unit fire support planning and is useful to decrease the decision time in battlefield.
Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat
2016-05-01
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.
Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems
Samira Chabaa
2009-01-01
Full Text Available Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been reported by several researchers. The goal of the present contribution was to complement these previous researches by predicting network traffic data. Approach: The Adaptive Neuro-Fuzzy Inference System (ANFIS was realized by an appropriate combination of fuzzy systems and neural networks. It was applied in different applications which have been increased in recent years and have multidisciplinary in several domains with a high accuracy. For this reason, we used a set of input and output data of packet transmission over Internet Protocol (IP networks as input and output of ANFIS to develop a model for predicting data. Results: ANFIS was compared with some existing model based on Volterra system with Laguerre functions. The obtained results demonstrate that the sequences of generated values have the same statistical characteristics as those really observed. Furthermore, the relative error using ANFIS model was better than this obtained by Volterra system model. Conclusion: The developed model fits well real data and can be used for predicting purpose with a high accuracy.
Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers
YUCEL, M.
2017-02-01
Full Text Available Erbium-doped fiber amplifiers (EDFA must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM and dense WDM (DWDM applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC is designed based on the adaptive neuro-fuzzy inference system (ANFIS for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.
Development of Neuro-fuzzy System for Early Prediction of Heart Attack
Obanijesu Opeyemi
2012-08-01
Full Text Available This work is aimed at providing a neuro-fuzzy system for heart attack detection. Theneuro-fuzzy system was designed with eight input field and one output field. The input variables are heart rate, exercise, blood pressure, age, cholesterol, chest pain type, blood sugar and sex. The output detects the risk levels of patients which are classified into 4 different fields: very low, low, high and very high. The data set used was extracted from the database and modeled in order to make it appropriate for the training, then the initial FIS structure was generated, the network was trained with the set of training data after which it was tested and validated with the set of testing data. The output of the system was designed in a way that the patient can use it personally. The patient just need to supply some values which serve as input to the system and based on the values supplied the system will be able to predict the risk level of the patient.
Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow
Yaseen A. Hamaamin
2016-07-01
Full Text Available Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS, were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed and local (subbasin results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales.
Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat
2017-08-01
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.
Julie, E. Golden; Selvi, S. Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
Shahaboddin Shamshirband
Full Text Available Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.
GEMAN, O.
2014-02-01
Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.
Förner, K.; Polifke, W.
2017-10-01
The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems.
Julie, E Golden; Selvi, S Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System
Ammar A. Aldair
2016-12-01
Full Text Available Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional-integral (PI controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker.
Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A
2012-01-01
Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.
E. Golden Julie
2016-01-01
Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
Static security-based available transfer capability using adaptive neuro fuzzy inference system
Venkaiah, C.; Vinod Kumar, D.M.
2010-07-01
In a deregulated power system, power transactions between a seller and a buyer can only be scheduled when there is sufficient available transfer capability (ATC). Internet-based, open access same-time information systems (OASIS) provide market participants with ATC information that is continuously updated in real time. Static security-based ATC can be computed for the base case system as well as for the critical line outages of the system. Since critical line outages are based on static security analysis, the computation of static security based ATC using conventional methods is both tedious and time consuming. In this study, static security-based ATC was computed for real-time applications using 3 artificial intelligent methods notably the back propagation algorithm (BPA), the radial basis function (RBF) neural network, and the adaptive neuro fuzzy inference system (ANFIS). An IEEE 24-bus reliability test system (RTS) and 75-bus practical system were used to test these 3 different intelligent methods. The results were compared with the conventional full alternating current (AC) load flow method for different transactions.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
P. Akhavan
2014-10-01
Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.
2015-12-01
This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.
Souha Boukadida
2014-12-01
Full Text Available The conventional Direct Torque Control (DTC is known to produce a quick and robust response in AC drives. However, during steady state, stator flux and electromagnetic torque which results in incorrect speed estimations and acoustical noise. A modified Direct Torque Control (DTC by using Space Vector Modulation (DTC-SVM for induction machine is proposed in this paper. Using this control strategy, the ripples introduced in torque and flux are reduced. This paper presents a novel approach to design and implementation of a high perfromane torque control (DTC-SVM of induction machine using Field Programmable gate array (FPGA.The performance of the proposed control scheme is evaluated through digital simulation using Matlab\\Simulink and Xilinx System Generator. The simulation results are used to verify the effectiveness of the proposed control strategy.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not
Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng
2016-05-01
Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.
NURWAHA Deogratias; WANG Xin-hou
2008-01-01
This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.
KAMPOUROPOULOS, K.
2014-02-01
Full Text Available This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithms (GA. The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.
Otilia Elena Dragomir
2015-11-01
Full Text Available The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1 and the shape of membership functions (Scenario 2.
Tole Sutikno
2011-11-01
Full Text Available Direct Torque Control (DTC has gained popularity for development of advanced motor control due to its simplicity and offers fast instantaneous torque and flux controls. However, the conventional DTC which is based on hysteresis controller has major drawbacks, namely high torque ripple and variable inverter switching frequency. This paper presents an improved switching strategy for reducing flux and torque ripples in DTC of PMSM drives; wherein the torque hysteresis controller and the look-up table used in the conventional DTC are replaced with a constant frequency torque controller (CFTC and an optimized look-up table, respectively. It can be shown that a constant switching frequency is established due to the use of the CFTC while the reduction of torque and flux ripples is achieved mainly because of the selection of optimized voltage vector (i.e. with an optimized look-up table. This paper also will explain the construction of DTC schemes implemented using MATLAB-Simulink blocks. Simulation results were shown that a significant reduction of flux and torque ripples which is about 90% can be achieved through the proposed DTC scheme.
Ramanpreet Kaur
2017-02-01
Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.
Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques
Banadaki Hamed Dehghan
2015-01-01
Full Text Available The walking beam furnace (WBF is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead, developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.
Ulfatun Hani'ah
2016-06-01
Full Text Available Peramalan pemakaian air pada bulan januari 2015 sampai April 2015 dapat dilakukan menggunakan perhitungan matematika dengan bantuan ilmu komputer. Metode yang digunakan adalah Adaptive Neuro Fuzzy Inference System (ANFIS dengan bantuan software MATLAB. Untuk pengujian program, dilakukan percobaan dengan memasukkan variabel klas = 2, maksimum epoh = 100, error = 10-6, rentang nilai learning rate = 0.6 sampai 0.9, dan rentang nilai momentum = 0.6 sampai 0.9. Simpulan yang diperoleh adalah bahwa implementasi metode Adaptive Neuro-Fuzzy Inference System dalam peramalan pemakaian air yang pertama adalah membuat rancangan flowchart, melakukan clustering data menggunakan fuzzy C-Mean, menentukan neuron tiap-tiap lapisan, mencari nilai parameter dengan menggunakan LSE rekursif, lalu penentuan perhitungan error menggunakan sum square error (SSE dan membuat sistem peramalan pemakaian air dengan software MATLAB. Setelah dilakukan percobaan hasil yang menunjukkan SSE paling kecil adalah nilai learning rate 0.9 dan momentum 0.6 dengan SSE 0.0080107. Hasil peramalan pemakaian air pada bulan Januari adalah 3.836.138m3, bulan Februari adalah 3.595.188m3, bulan Maret adalah 3.596.416 m3, dan bulan April adalah 3.776.833 m3.
Direct Torque Control for Double Star Induction Motor
LEKHCHINE, SALIMA; BAHI, TAHAR; Soufi, Youcef
2016-01-01
This paper describes a direct torque control (DTC) of dual star induction motor (DSIM). This machine possesses several advantages over conventional three-phase machine and is also known as the six-phase induction machine. The research has been underway for the last two decades to investigate the various issues related to the use of six-phase machine as a potential alternative to the conventional three-phase machine. Though six-phase machines have existed for some time, in the literature very ...
A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data
Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng
2017-02-01
Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to
Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede
2015-01-01
for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...
ZHOU Yangzhong; HU Yuwen; HUANG Wenxin; ZHONG Tianyun
2007-01-01
The electrically excited synchronous motor (ESM)has typically small synchronous inductance values and quite low transient values because of the damper windings mounted on the rotor.Therefore,the torque and stator flux linkage ripples are high in the direct torque control(DTC)drive of the ESM with a torque and flux linkage hysteresis controller (basic DTC).A DTC scheme with space vector modulation(SVM)for the ESM was investigated in this paper.It is based on the compensation of the stator flux linkage vector error using the space vector modulation in order to decrease the torque and flux linkage ripples and produce fixed switching frequency under the principle that the torque is controlled by the torque angle in the ESM.Compared with the basic DTC,the results of the simulation and experiment show that the torque and flux linkage rippies are reduced,the maximum current value is decreased during the startup,and the current distortion is much smaller in the steady-state under the SVM-DTC.The field-weakening control is incorporated with the SVM-DTC successfully.
High Performance Direct Torque Control of Induction Motor Drives Using Space Vector Modulation
S. Allirani
2010-11-01
Full Text Available This paper presents a simple approach to design and implement Direct Torque Control technique for voltage source inverter fed induction motor drives. The direct torque control is one of the excellent strategies available for torque control of induction machine. It is considered as an alternative to field oriented control technique. The Direct Torque Control scheme is characterized by the absence of PI regulators, co-ordinate transformations, current regulators and pulse width modulated signal generators. Direct Torque Control allows a good torque control in steady state and transient operating conditions. The direct torque control technique based on space vector modulation and switching table has been developed and presented in this paper.
Vitor Badiale Furlong
2013-02-01
Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.Neste trabalho, foi construído um estimador neuro-fuzzy da concentração de biomassa da microalga Synechococcus nidulans a partir de concentrações iniciais da batelada, visando possibilitar a predição da produtividade. Nove experimentos em réplica foram realizados. O crescimento foi acompanhado diariamente pela transmitância do meio e mantido até o final da fase exponencial de crescimento. O treinamento das redes ocorreu segundo delineamento experimental 3³, os fatores foram o número de dias no vetor de entrada (3, 5 e 7 dias, o número de clusters (10, 30 e 50 clusters e o valor de abrandamento do filtro interno (Sigma (0,30, 0,45 e 0,60. A variável resposta foi o somatório do erro quadrático das validações. Estas possuíam 24 (A
Li-Ching Lin Hsien-Kuo Chang
2008-01-01
Full Text Available The paper presents an adaptive neuro fuzzy inference system for predicting sea level considering tide-generating forces and oceanic thermal expansion assuming a model of sea level dependence on sea surface temperature. The proposed model named TGFT-FN (Tide-Generating Forces considering sea surface Temperature and Fuzzy Neuro-network system is applied to predict tides at five tide gauge sites located in Taiwan and has the root mean square of error of about 7.3 - 15.0 cm. The capability of TGFT-FN model is superior in sea level prediction than the previous TGF-NN model developed by Chang and Lin (2006 that considers the tide-generating forces only. The TGFT-FN model is employed to train and predict the sea level of Hua-Lien station, and is also appropriate for the same prediction at the tide gauge sites next to Hua-Lien station.
Zhixian Yang
2014-01-01
Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
Mohammad Najafzadeh
2015-03-01
Full Text Available In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO. Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS. The testing results of performances were compared with the gene-expression programming (GEP, evolutionary polynomial regression (EPR model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.
A comparative study of ANN and Neuro-fuzzy for the prediction of dynamic constant of rockmass
T N Singh; R Kanchan; A K Verma; K Saigal
2005-02-01
Physico-mechanical properties of rocks have great significance in all operational parts in mining activities, from exploration to final dispatch of material. Compressional wave velocity (-wave velocity) and anisotropic behaviour of rocks are two such properties which help to understand the rock response under varying stress conditions. They also influence the breakage mechanism of rock. There are different methods to determine the -wave velocity and anisotropy in situ and in the laboratory. These methods are cumbersome and time consuming. Fuzzy set theory, Fuzzy logic and Neural Networks techniques seem very well suited for typical geotechnical problems. In conjunction with statistics and conventional mathematical methods, hybrid methods can be developed that may prove to be a step forward in modeling geotechnical problems. Here, we have developed and compared two di®erent models, Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and Artificial neural network systems, for the prediction of compressional wave velocity.
Al-Shammari, Eiman Tamah; Petković, Dalibor; Danesh, Amir Seyed; Shamshirband, Shahaboddin; Issa, Mirna; Zentner, Lena
2016-05-01
Robotic operations need to be safe for unpredictable contacts. Joints with passive compliance with springs can be used for soft robotic contacts. However the joints cannot measure external collision forces. In this investigation was developed one passive compliant joint which have soft contacts with external objects and measurement capabilities. To ensure it, conductive silicone rubber was used as material for modeling of the compliant segments of the robotic joint. These compliant segments represent embedded sensors. The conductive silicone rubber is electrically conductive by deformations. The main task was to obtain elastic absorbers for the external collision forces. These absorbers can be used for measurement in the same time. In other words, the joint has an internal measurement system. Adaptive neuro fuzzy inference system (ANFIS) was used to estimate the safety level of the robotic joint by head injury criteria (HIC).
Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep
2015-09-01
Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.
Zong, Lu-Hang; Gong, Xing-Long; Guo, Chao-Yang; Xuan, Shou-Hu
2012-07-01
In this paper, a magneto-rheological (MR) damper-based semi-active controller for vehicle suspension is developed. This system consists of a linear quadratic Gauss (LQG) controller as the system controller and an adaptive neuro-fuzzy inference system (ANFIS) inverse model as the damper controller. First, a modified Bouc-Wen model is proposed to characterise the forward dynamic characteristics of the MR damper based on the experimental data. Then, an inverse MR damper model is built using ANFIS technique to determine the input current so as to gain the desired damping force. Finally, a quarter-car suspension model together with the MR damper is set up, and a semi-active controller composed of the LQG controller and the ANFIS inverse model is designed. Simulation results demonstrate that the desired force can be accurately tracked using the ANFIS technique and the semi-active controller can achieve competitive performance as that of active suspension.
Modelos computacionais fuzzy e neuro-fuzzy para avaliarem os efeitos da poluição do ar
Chaves, Luciano Eustáquio
2013-01-01
O presente estudo teve por objetivo verificar a associação entre a exposição aos poluentes do ar e o número de internações hospitalares por asma e pneumonia. Para a verificação foi proposto desenvolver e validar modelos fuzzy (Mamdani) e neuro-fuzzy (Sugeno) e comparar qual dos modelos apresenta uma melhor eficácia para a predição de internações. A metodologia utilizada foi dividida em três módulos: limpeza e elaboração de dados, elaboração do modelo fuzzy (Mamdani) e elaboração do modelo neu...
Bilgehan, Mahmut
2011-03-01
In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the evaluation of relationships between concrete compressive strength and ultrasonic pulse velocity (UPV) values using the experimental data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. A comparative study is made using the neural nets and neuro-fuzzy (NF) techniques. Statistic measures were used to evaluate the performance of the models. Comparing of the results, it is found that the proposed ANFIS architecture with Gaussian membership function is found to perform better than the multilayer feed-forward ANN learning by backpropagation algorithm. The final results show that especially the ANFIS modelling may constitute an efficient tool for prediction of the concrete compressive strength. Architectures of the ANFIS and neural network established in the current study perform sufficiently in the estimation of concrete compressive strength, and particularly ANFIS model estimates closely follow the desired values. Both ANFIS and ANN techniques can be used in conditions where too many structures are to be examined in a restricted time. The presented approaches enable to practically find concrete strengths in the existing reinforced concrete structures, whose records of concrete mixture ratios are not available or present. Thus, researchers can easily evaluate the compressive strength of concrete specimens using UPV and density values. These methods also contribute to a remarkable reduction in the computational time without any significant loss of accuracy. A comparison of the results clearly shows that particularly the NF approach can be used effectively to predict the compressive strength of concrete using UPV and density values. In addition, these model architectures can be used as a nondestructive procedure for health monitoring of
Hoell, Simon; Omenzetter, Piotr
2017-07-01
Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.
LI Jian; YANG Geng; WANG Huan'gang; XU Wenli
2005-01-01
A torque control scheme for high-performance induction machine drives was developed to over- come some disadvantages of direct torque control (DTC). In the improved DTC method, the stator flux and the torque controllers use variable-structure control theory which does not require information about the rotor speed. Space vector modulation is applied to the voltage source inverter to reduce the torque, stator flux, and current ripples. The digital signal processor-based implementation is described in detail. The experimental results show that the system has good torque and stator flux response with small ripples.
Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban
2017-01-01
Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is
A. S. Koval
2008-01-01
Full Text Available In the article problems of frequency properties research for electric drive system with direct torque control and pulse width modulator are described. The mathematical description of elevator is present. Simplified mathematical description of direct torque control - pulse width modulator electric drive system is shown. Transfer functions for torque and speed loops are determined. Logarithmic frequency characteristics are computed. Damping properties of elevator drive system are estimated.
Fatih Korkmaz
2013-11-01
Full Text Available The induction motors are indispensable motor types for industrial applications due to its wellknown advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme.
Taib, Nabil; Francois, Bruno
2010-01-01
A few papers have been interested by the fixed switching frequency direct torque control fed by direct matrix converters, where we can find just the use of direct torque controlled space vector modulated method. In this present paper, we present an improved method used for a fixed switching frequency direct torque control (DTC) using a direct matrix converter (DMC). This method is characterized by a simple structure, a fixed switching frequency which causes minimal torque ripple and a unity input power factor. Using this strategy, we combine the direct matrix converters advantages with those of direct torque control (DTC) schemes. The used technique for constant frequency is combined with the input current space vector to create the switching table of direct matrix converter (DMC). Simulation results clearly demonstrate a better dynamic and steady state performances of the proposed method.
Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS
H Izadi
2016-04-01
Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning
FPGA-Based Implementation Direct Torque Control of Induction Motor
Saber Krim
2015-02-01
Full Text Available This paper proposes a digital implementation of the direct torque control (DTC of an Induction Motor (IM with an observation strategy on the Field Programmable Gate Array (FPGA. The hardware solution based on the FPGA is caracterised by fast processing speed due to the parallel processing. In this study the FPGA is used to overcome the limitation of the software solutions (Digital Signal Processor (DSP and Microcontroller. Also, the DTC of IM has many drawbacks such as for example; The open loop pure integration has from the problems of integration especially at the low speed and the variation of the stator resistance due to the temperature. To tackle these problems we use the Sliding Mode Observer (SMO. This observer is used estimate the stator flux, the stator current and the stator resistance. The hardware implementation method is based on Xilinx System Generator (XSG which a modeling tool developed by Xilinx for the design of implemented systems on FPGA; from the design of the DTC with SMO from XSG we can automatically generate the VHDL code. The model of the DTC with SMO has been designed and simulated using XSG blocks, synthesized with Xilinx ISE 12.4 tool and implemented on Xilinx Virtex-V FPGA.
A Model of FPGA-based Direct Torque Controller
Auzani Jidin
2013-02-01
Full Text Available This paper presents a generic model of a fully FPGA-based direct torque controller. This model is developed using two’s-complement fixed-point format approaches, in register-transfer-level (RTL VHDL abstraction for minimizing calculation errors and consuming hardware resource usage. Therefore, the model is universal and can be implemented for all FPGA types. The model is prepared for fast computation, without using of CORDIC algorithm, a soft-core CPU, a transformation from Cartesian-to-polar coordinates, and without the help of third-party applications. To get simpler implementation and fast computation, several methods were introduced: i the backward-Euler approach to calculate the discrete-integration operation of stator flux, ii the modified non-restoring method to calculate complicated square-root operation of stator flux, iii a new sector analysis method. The design, which was coded in synthesizable VHDL in RTL abstraction for implementation on Altera DE2-board has produced very-precise calculations, with minimal error when being compared to MATLAB/Simulink double-precision calculation.
Vinay KUMAR
2011-06-01
Full Text Available This paper proposes an algorithm for direct flux and torque controlled three phase induction motor drive systems. This method is based on control of slip speed and decoupled between amplitude and angle of reference stator flux for determining required stator voltage vector. In this proposes model, integrator unit is not required to generate the reference stator flux angle for calculating required stator voltage vector, hence it eliminates the initial values problems in real time. Within the given sampling time, flux as well as torque errors are controlled by stator voltage vector which is evaluated from reference stator flux. The direct torque control is achieved by reference stator flux angle which is generates from instantaneous slip speed angular frequency and stator flux angular frequency. The amplitude of the reference stator flux is kept constant at rated value. This technique gives better performance in three-phase induction motor than conventional technique. Simulation results for 3hp induction motor drive, for both proposed and conventional techniques, are presented and compared. From the results it is found that the stator current, flux linkage and torque ripples are decreased with proposed technique.
Simulation of Brushless DC Motor using Direct Torque Control
Kusuma, G.; S. Rukhsana Begum
2014-01-01
This paper deals with modelling of three phases brushless dc motor with MATLAB/SIMULINK software BLDC motor have advantages according to brushless dc motor and induction motor’s. They have improve speed torque charactistics, high efficiency high transient response and small size. It approaches for reducing the torque ripples of BLDC motor using DTC, by using control technique’s ,but present work mainly concentrate on advanced method. The whole drive system is simulated based o...
Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS
H Izadi
2016-04-01
Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning
FENG Yi; HUANG Shu-huai; LI Jun-chao; XIONG Xiao-hong
2009-01-01
Fast response and stable torque output are crucial to the performance of electric screw presses. This paper describes the design of a direct torque control (DTC) system for speeding up torque response and reducing the starting current of electric screw presses and its application to the J58K series of numerical control electric screw presses with a dual-motor drive. The DTC drive system encompasses speed control, torque reference control, and switching frequency control. Comparison of the DTC duaI-AC induction motor drive with corresponding AC servo motor drive showed that for the J58K-315 electric screw press, the DTC drive system attains a higher maximum speed (786 r/min) within a shorter time (1.13 s) during a 250 mm stroke and undergoes smaller rise in temperature (42.0 ℃) in the motor after running for 2 h at a 12 min strike fi'equency than the AC servo motor drive does (751 r/min within 1.19 s, and 50.6 ℃ rise). Moreover, the DTC AC induction motor drive, with no need for a tachometer or position encoder to feed back the speed or position of the motor shaft, enjoys increased reliability in a strong-shock work environment.
A Novel Direct Torque Control Permanent Magnet Synchronous Motor Drive used in Electrical Vehicle
Yaohua Li
2011-10-01
Full Text Available In this paper, a modified direct torque control (DTC scheme for permanent magnet synchronous motor (PMSM is investigated, which enables low torque ripple by using an improved voltage vector selection strategy instead of switching table used in conventional DTC. Based on the control of stator flux, torque angle and torque, voltage vector selection strategy of PMSM DTC drive is proposed. In the proposed voltage vector selection strategy, the applied voltage vector is determined according to outputs of hysteresis comparators for stator flux and torque, angular position of stator flux and torque angle, which is finally synthesized by space vector modulation (SVM. Modeling and experimental results for an interior PMSM used in Honda Civic 06My Hybrid electrical vehicle are given. Simulation and experimental results show torque ripple is reduced and the total harmonics of stator current is decreased when compared those of conventional DTC. And a fixed switching frequency is obtained with the help of SVM. In addition, the proposed DTC doesn’t need any additional PI controller, which maintains the simplicity in conventional DTC. Keywords: direct torque control, permanent magnet synchronous motor, electrical vehicle, torque ripple, switching frequency
Winters Jack M
2005-06-01
Full Text Available Abstract Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs. Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS. The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes, showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain as a function of input event patterns (e.g., medications. The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating
Induction machine Direct Torque Control system based on fuzzy adaptive control
Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng
2009-07-01
Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.
Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede
2002-01-01
DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...
Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede
2002-01-01
DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...
Simulation of Brushless DC Motor using Direct Torque Control
Mrs.G. Kusuma
2014-04-01
Full Text Available This paper deals with modelling of three phases brushless dc motor with MATLAB/SIMULINK software BLDC motor have advantages according to brushless dc motor and induction motor’s. They have improve speed torque charactistics, high efficiency high transient response and small size. It approaches for reducing the torque ripples of BLDC motor using DTC, by using control technique’s ,but present work mainly concentrate on advanced method. The whole drive system is simulated based on the system devices, BLDC motor source inverter, space vector modulation.
Lin, J.; Zheng, Y. B.
2012-07-01
The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.
Banjanovic-Mehmedovic Lejla
2016-01-01
Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.
Ahcene Boubakir; Fares Boudjema; Salim Labiod
2009-01-01
The aim of this paper is to develop a neuro-fuzzy-sliding mode controller (NFSMC) with a nonlinear sliding surface for a coupled tank system.The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation.A first-order nonlinear sliding surface is presented,on which the developed sliding mode controller (SMC) is based.Mathematical proof for the stability and convergence of the system is presented.In order to reduce the chattering in SMC,a fixed boundary layer around the switch surface is used.Within the boundary layer,where the fuzzy logic control is applied,the chattering phenomenon,which is inherent in a sliding mode control,is avoided by smoothing the switch signal.Outside the boundary,the sliding mode control is applied to drive the system states into the boundary layer.Moreover,to compute the equivalent controller,a feed-forward neural network (NN) is used.The weights of the net are updated such that the corrective control term of the NFSMC goes to zero.Then,this NN also alleviates the chattering phenomenon because a big gain in the corrective control term produces a more serious chattering than a small gain.Experimental studies carried out on a coupled tank system indicate that the proposed approach is good for control applications.
Ja’fari A.
2014-01-01
Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.
Sagir, Abdu Masanawa; Sathasivam, Saratha
2017-08-01
Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.
Lee, Jae-Neung; Lee, Myung-Won; Byeon, Yeong-Hyeon; Lee, Won-Sik; Kwak, Keun-Chang
2016-01-01
In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. PMID:27171098
Changho Jhin
2014-08-01
Full Text Available Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A and electronegativity (χ of flavylium cation, and ionization potential (I of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively.
Mosbeh R. Kaloop
2015-10-01
Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.
Metin Ertunc, H. [Department of Mechatronics Engineering, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey); Hosoz, Murat [Department of Mechanical Education, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey)
2008-12-15
This study deals with predicting the performance of an evaporative condenser using both artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. For this aim, an experimental evaporative condenser consisting of a copper tube condensing coil along with air and water circuit elements was developed and equipped with instruments used for temperature, pressure and flow rate measurements. After the condenser was connected to an R134a vapour-compression refrigeration circuit, it was operated at steady state conditions, while varying both dry and wet bulb temperatures of the air stream entering the condenser, air and water flow rates as well as pressure, temperature and flow rate of the entering refrigerant. Using some of the experimental data for training, ANN and ANFIS models for the evaporative condenser were developed. These models were used for predicting the condenser heat rejection rate, refrigerant temperature leaving the condenser along with dry and wet bulb temperatures of the leaving air stream. Although it was observed that both ANN and ANFIS models yielded a good statistical prediction performance in terms of correlation coefficient, mean relative error, root mean square error and absolute fraction of variance, the accuracies of ANFIS predictions were usually slightly better than those of ANN predictions. This study reveals that, having an extended prediction capability compared to ANN, the ANFIS technique can also be used for predicting the performance of evaporative condensers. (author)
Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
S. Kavitha
2011-01-01
Full Text Available Problem statement: Diabetic retinopathy is one of the most significant factors contributing to blindness and so early diagnosis and timely treatment is particularly important to prevent visual loss. Approach: An integrated approach for extraction of blood vessels and exudates detection was proposed to screen diabetic retinopathy. An automated classifier was developed based on Adaptive Neuro-Fuzzy Inference System (ANFIS to differentiate between normal and nonproliferative eyes from the quantitative assessment of monocular fundus images. Feature extraction was performed on the preprocessed fundus images. Structure of Blood vessels was extracted using Multiscale analysis. Hard Exudates were detected using CIE Color channel transformation, Entropy Thresholding and Improved Connected Component Analysis from the fundus images. Features like Wall to Lumen ratio in blood vessels, Texture, Homogeneity properties and area occupied by Hard Exudates, were given as input to ANFIS.ANFIS was trained with Back propagation in combination with the least squares method. Proposed method was evaluated on 200 real time images comprising 70 normal and 130 retinopathic eyes. Results and Conclusion: All of the results were validated with ground truths obtained from expert ophthalmologists. Quantitative performance of the method, detected exudates with an accuracy of 99.5%. Receiver operating characteristic curve evaluated for real time images produced better results compared to the other state of the art methods. ANFIS provides best classification and can be used as a screening tool in the analysis and diagnosis of retinal images.
Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad
2012-05-01
Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.
Teimouri, Reza; Sohrabpoor, Hamed
2013-12-01
Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.
Lilik Sutiarso
2012-05-01
Full Text Available The control of an autonomous agricultural vehicle operating on unstructured changing terrain includes many objective diffi culties. One major diffi culty concerns the characteristics of the terrain condition that the vehicle should operate in. Problems ranged from the effects of varying terrain conditions on the autonomous vehicle sensors and traction performance through to the need to deal with the presence of unexpected situations. On unstructured changing terrain, many factors infl uence vehicle behavior such as terrain slope, lateral slippage, and so on. Therefore, it is necessary to develop a more suitable model for vehicle motion on these terrain conditions. In order to control the vehicle along a course on unstructured changing terrain, it was developed control software to enable more accurate control. The developed method to control the vehicle when operating on these conditions was Neuro-Fuzzy Controller. Result of the trained model could be described as follows; number of nodes was 193, number of fuzzy rules was 81, average testing error between simulation and ANFIS output was 0.76, while for experimental and ANFIS output was 1.61. It was concluded that the developed control system had a good accuracy to steer the vehicle.
Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim
2016-11-01
In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.
Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.
2017-01-01
People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.
Direct Instantaneous Torque Control of 4 Phase 8/6 Switched Reluctance Motor
Srinivas Pratapgiri
2011-10-01
Full Text Available The applications of Switched Reluctance Motor Drives has increased in the recent past because of advantages like simple structure, no rotor winding, high torque to weight ratio, adaptability to harsh environments like coal mining etc. But the main disadvantage is that torque ripple is high because of the double saliency. This paper presents a high dynamic control technique called Direct Instantaneous Torque Control (DITC where in the torque is maintained within a hysteresis band by changing the switching states of the phases between 1, 0 or -1.Thus torque ripple minimization is an inherent property of DITC. DITC based SRM drive is simulated in MATLAB/SIMULINK environment and results are discussed elaborately
An Observing Method for Flux and Speed with Direct Torque Control
祝龙记; 王汝琳
2004-01-01
An observing method for stator flux and rotor flux is presented. Based on the proposed flux observing method, a novel speed estimator has been designed. At last, the speed estimator combined with the flux observing is applied in the direct torque control system without speed sensor. The simulation results show that these methods can improve the accuracy of speed observing and the low speed performance of direct torque control system, and strengthen the robustness of system.
Nandakumar Sundararaju
2014-05-01
Full Text Available This paper proposes novel hybrid asymmetric space vector modulation technique for inverter operated direct torque control induction motor drive. The hybridization process is performed by the combination of continuous asymmetric space vector modulation pulse width technique (ASVPWM and fuzzy operated discontinuous ASVPWM technique. Combination process is based on pulse mismatching technique. Pulse mismatching technique helps to reduce the active region of the switch. Finally, optimal pulses are applied to control the inverter. The optimal hybrid pulse condense switching losses of the inverter and also improves the operating performance of the direct torque control (DTC based drive system like smooth dynamic response in speed reversal, minimum torque error, settling time of speed. Simulation results of proposed hybrid asymmetric space vector pulse width modulation technique to direct torque control (HASVPWM-DTC approach has been carried out by using Matlab-Simulink environment.
Sivachandran Paulsamy
2014-01-01
Full Text Available In wind energy systems employing permanent magnet generator, there is an imperative need to reduce the cogging torque for smooth and reliable cut in operation. In a permanent magnet generator, cogging torque is produced due to interaction of the rotor magnets with slots and teeth of the stator. This paper is a result of an ongoing research work that deals with various methods to reduce cogging torque in dual rotor radial flux permanent magnet generator (DRFPMG for direct coupled stand alone wind energy systems (SAWES. Three methods were applied to reduce the cogging torque in DRFPMG. The methods were changing slot opening width, changing magnet pole arc width and shifting of slot openings. A combination of these three methods was applied to reduce the cogging torque to a level suitable for direct coupled SAWES. Both determination and reduction of cogging torque were carried out by finite element analysis (FEA using MagNet Software. The cogging torque of DRFPMG has been reduced without major change in induced emf. A prototype of 1 kW, 120 rpm DRFPMG was fabricated and tested to validate the simulation results. The test results have good agreement with the simulation predictions.
Paulsamy, Sivachandran
2014-01-01
In wind energy systems employing permanent magnet generator, there is an imperative need to reduce the cogging torque for smooth and reliable cut in operation. In a permanent magnet generator, cogging torque is produced due to interaction of the rotor magnets with slots and teeth of the stator. This paper is a result of an ongoing research work that deals with various methods to reduce cogging torque in dual rotor radial flux permanent magnet generator (DRFPMG) for direct coupled stand alone wind energy systems (SAWES). Three methods were applied to reduce the cogging torque in DRFPMG. The methods were changing slot opening width, changing magnet pole arc width and shifting of slot openings. A combination of these three methods was applied to reduce the cogging torque to a level suitable for direct coupled SAWES. Both determination and reduction of cogging torque were carried out by finite element analysis (FEA) using MagNet Software. The cogging torque of DRFPMG has been reduced without major change in induced emf. A prototype of 1 kW, 120 rpm DRFPMG was fabricated and tested to validate the simulation results. The test results have good agreement with the simulation predictions.
Direct Torque Control of Sensorless Induction Motor Drives: A Sliding-Mode Approach
Lascu, Cristian; Boldea, Ion; Blaabjerg, Frede
2004-01-01
-vector pulsewidth modulation is proposed for induction motor sensorless drives. The DTC transient merits and robustness are preserved and the steady-state behaviour is improved by reducing the torque and flux pulsations. A sliding-mode observer using a dual reference frame motor model is introduced and tested......Direct torque control (DTC) is known to produce fast response and robust control in ac adjustable-speed drives. However, in the steady-state operation, notable torque, flux, and current pulsations occur. A new, direct torque and flux control strategy based on variable-structure control and space....... Simulations and comparative experimental results with the proposed control scheme, versus classic DTC, are presented. Very-low-speed sensorless operation (3 r/min) is demonstrated....
Improving the performance of hysteresis direct torque control of IPMSM using active ﬁlter topology
Kayhan Gulez; Ali Ahmed Adam; Halit Pastaci
2006-06-01
This paper describes an active ﬁlter topology to improve the performance of hysteresis direct torque control (HDTC) of interior permanent magnet synchronous motor (IPMSM). The ﬁlter topology consists of an active ﬁlter and two RLC ﬁlters, and is connected to the main power circuit through a 1:1 transformer. The active ﬁlter is characterized by detecting the harmonics in the motor phase voltages and injecting equivalent harmonic voltages to produce almost sinusoidal voltage waveform to the motor terminals. The active ﬁlter uses hysteresis voltage controller while the motor main circuit uses hysteresis direct torque control. The simulation results of this combined control structure show considerable torque ripple reduction in the steady state range and adequate dynamic torque performance as well as considerable harmonic voltage and EMI noise reduction.
Converting a commercial electric direct-drive robot to operate from joint torque commands
Muir, P.F.
1991-07-01
Many robot control algorithms for high performance in-contact operations including hybrid force/position, stiffness control and impedance control approaches require the command the joint torques. However, most commercially available robots do not provide joint torque command capabilities. The joint command at the user level is typically position or velocity and at the control developer level is voltage, current, or pulse-width, and the torque generated is a nonlinear function of the command and joint position. To enable the application of high performance in-contact control algorithms to commercially available robots, and thereby facilitate technology transfer from the robot control research community to commercial applications, an methodology has been developed to linearize the torque characteristics of electric motor-amplifier combinations. A four degree of freedom Adept 2 robot, having pulse-width modulation amplifiers and both variable reluctance and brushless DC motors, is converted to operate from joint torque commands to demonstrate the methodology. The commercial robot controller is replaced by a VME-based system incorporating special purpose hardware and firmware programmed from experimental data. The performance improvement is experimentally measured and graphically displayed using three-dimensional plots of torque vs command vs position. The average percentage torque deviation over the command and position ranges is reduced from as much as 76% to below 5% for the direct-drive joints 1, 2 and 4 and is cut by one half in the remaining ball-screw driven joint 3. Further, the torque deviation of the direct-drive joints drops below 2.5% if only the upper 90% of the torque range is considered. 23 refs., 20 figs., 2 tabs.
Inference of S-wave velocities from well logs using a Neuro-Fuzzy Logic (NFL) approach
Aldana, Milagrosa; Coronado, Ronal; Hurtado, Nuri
2010-05-01
The knowledge of S-wave velocity values is important for a complete characterization and understanding of reservoir rock properties. It could help in determining fracture propagation and also to improve porosity prediction (Cuddy and Glover, 2002). Nevertheless the acquisition of S-wave velocity data is rather expensive; hence, for most reservoirs usually this information is not available. In the present work we applied a hybrid system, that combines Neural Networks and Fuzzy Logic, in order to infer S-wave velocities from porosity (φ), water saturation (Sw) and shale content (Vsh) logs. The Neuro-Fuzzy Logic (NFL) technique was tested in two wells from the Guafita oil field, Apure Basin, Venezuela. We have trained the system using 50% of the data randomly taken from one of the wells, in order to obtain the inference equations (Takani-Sugeno-Kang (TSK) fuzzy model). Equations using just one of the parameters as input (i.e. φ, Sw or Vsh), combined by pairs and all together were obtained. These equations were tested in the whole well. The results indicate that the best inference (correlation between inferred and experimental data close to 80%) is obtained when all the parameters are considered as input data. An increase of the equation number of the TSK model, when one or just two parameters are used, does not improve the performance of the NFL. The best set of equations was tested in a nearby well. The results suggest that the large difference in the petrophysical and lithological characteristics between these two wells, avoid a good inference of S-wave velocities in the tested well and allowed us to analyze the limitations of the method.
Erhankana Ardiana Putra
2017-01-01
Full Text Available Pada sistem kelistrikan terutama pada sistem proteksi kelistrikan dewasa ini sangat dibutuhkan sistem yang handal, sehingga perkembangan pada sistem proteksi sudah semakin maju dengan adanya penggunaan rele digital. Rele digital digunakan dengan mempertimbangkan kecepatan, keakuratan dan serta flexible dalam sistem koordinasi. Flexibilitas ini dimaksudkan bahwa rele digital dapat digunakan menjadi rele arus lebih (overcurrent relay sesuai pembahasan tugas akhir ini dan dapat disetting menurut keinginan user sesuai karakteristik kurva OCR konvensional/standart (normal inverse, very inverse, long time inverse, extreme inverse yang akan digunakan dalam koordinasi. Jenis kurva pada rele digital juga dapat disetting diluar rumus kurva konvensional/standart yang seperti sudah disebutkan sebelumnya, kurva diluar rumusan standart disebut kurva rele non-standart. Kurva rele non-standart digunakan untuk memudahkan pengguna untuk menentukan waktu trip berdasarkan arus yang diinginkan dan sebagai solusi jika pada koordinasi proteksi mengalami kendala dalam koordinasi kurva rele. Pada tugas akhir ini akan dibahas bagaimana membuat atau memodelkan kurva karakteristik inverse overcurrent rele non-standart dengan menggunakan metode (Adaptive Neuro Fuzzy Inference System atau biasa disebut metode pembelajaran ANFIS. Kurva non-standart didapatkan dengan pengambilan titik-titik data baru berupa arus dan waktu trip sesuai keinginan user. Data baru tersebut akan digabungkan dengan data lama sehingga menghasilkan data non-standart yang nantinya akan dilakukan pembelajaran dengan metode ANFIS untuk mendapatkan desain kurva non-standart. Setelah didapatkan desain kurva non-standart akan dilakukan pengujian keakuratan dengan mengganti nilai MF (membership function didapatkan hasil rata-rata error terkecil 2,56% (MF=10 dan epoch=100. Pengujian selanjutnya dengan mengubah nilai epoch didapatkan nilai keakuratan dengan error terkecil pada epoch = 500. Simulasi pada
Implementation of Hybrid Neuro-fuzzy Classifier%混合神经模糊分类器的实现
刘淑英
2013-01-01
Artificial neural network and fuzzy system were considered the main components of computation intelligence,the hybrid system about them was one of study topics in recent years. Classification is a research focus in data analysis,as data is complicated and diversi-fied,the requirements for classification will be increasingly high,sometimes only by experience and professional knowledge not to accu-rately classify. In view of their powerful data analysis functions,using neuro-fuzzy algorithm for data analysis will be meaningful and useful. In this paper,fuzzy C-means clustering algorithm model and Gath-Geva clustering algorithm model are proposed for the parame-ter classification,which is simulated,and obtain good results.%人工神经网络与模糊系统是计算智能的核心内容，二者的混合系统是近年来的一个研究热点。分类是数据分析中的研究重点，随着数据的复杂化和多样化，对分类的要求越来越高，有时仅凭经验和专业知识难以确切地进行分类，因此研究如何运用神经模糊分类算法进行数据分析具有重要意义与实用价值。鉴于其强大的数据分析功能，研究中采用模糊C均值聚类算法和Gath-Geva聚类算法对数据进行分类，并对测试数据进行仿真试验，其测试结果良好。
Faezehossadat Khademi
2016-12-01
Full Text Available Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS, and Multiple Linear Regression (MLR were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC. Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.
P. Bhattacharya
2007-11-01
Full Text Available To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i casual or contextual feature, (ii contact feature, (iii contactless feature, and (iv performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA, is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue. We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.
Julie M. David
2013-11-01
Full Text Available Learning Disability (LD is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.
Radovanović Milan M.
2015-01-01
Full Text Available In this research we search for a functional dependence between the occurrence of forest fires in the USA and the factors which characterize the solar activity. For this purpose we used several methods (R/S analysis, Hurst index to establish potential links between the influx of some parameters from the sun and the occurrence of forest fires with lag of several days. We found evidence for a connection and developed a prognostic scenario based on the Adaptive neuro-fuzzy interference system (ANFIS technique. This scenario allows the prediction between 79-93% of forest fires. [Projekat Ministarstva nauke Republike Srbije, br. III47007
A DSP-based discrete space vector modulation direct torque control of sensorless induction machines
Khoucha, F.; Marouani, K.; Kheloui, A.; Aliouane, K.
2004-07-01
In this paper, we present a Direct Torque Control scheme of an induction motor operating without speed sensor. The estimation of the stator flux and the rotor speed is performed by an adaptive observer. In order to reduce the torque, flux, current and speed ripple a Discrete Space Vector Modulation (DSVM-DTC) strategy is implemented using a DSP-based hardware. To illustrate the performances of this control scheme, experimental results are presented. (author)
Effects of Direct Torque Control Switching Strategies on Common Voltage and Bearing Current
Mohammad Taghi Sadeghzadeh
2012-04-01
Full Text Available Bearing current sininduction motorsare considered a sone of the most damaging factors. Induced shaft voltage through the parasitic capacitors cause this type of current. Inthispaper,given the increasing importance of direct torque control of induction motorin industry, various switching tables are assessed in order to ensure the lowest common voltage while maintaining the performance characteristics of the drive. Finally best switching table based on the minimum CMV, less torque rippleand better quality out put reference tracking is proposed.
Direct torque control via feedback linearization for permanent magnet synchronous motor drives
Lascu, Cristian; Boldea, Ion; Blaabjerg, Frede
2012-01-01
The paper describes a direct torque controlled (DTC) permanent magnet synchronous motor (PMSM) drive that employs feedback linearization and uses sliding-mode and linear controllers. We introduce a new feedback linearization approach that yields a decoupled linear PMSM model with two state...... variables, the torque and the square of stator flux magnitude. This linear model is intuitive and allows the implementation of DTC-type controllers that preserve all DTC advantages and eliminate its main drawback, the flux and torque ripple. Next, we investigate two controllers for toque and flux....... A variable structure controller (VSC) which is robust, fast, and produces low-ripple control is compared with a linear-DTC scheme which is ripple free. The torque time response is similar to a conventional DTC drive and the proposed solutions are flexible and highly tunable. We present the controller design...
Liu, Cheng-Li
2009-05-01
Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE.
Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir
2015-01-11
This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous {sup 99m}Tc/{sup 201}Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of {sup 201}Tl (77±10% keV) and {sup 99m}Tc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.
Direct Torque Control of Induction Motor Drive Fed from a Photovoltaic Multilevel Inverter
Mahrous Ahmed
2014-09-01
Full Text Available This paper presents Direct Torque Control (DTC using Space Vector Modulation (SVM for an induction motor drive fed from a photovoltaic multilevel inverter (PV-MLI. The system consists of two main parts PV DC power supply (PVDC and MLI. The PVDC is used to generate DC isolated sources with certain ratios suitable for the adopted MLI. Beside the hardware system, the control system which uses the torque and speed estimation to control the load angle and to obtain the appropriate flux vector trajectory from which the voltage vector is directly derived based on direct torque control methods. The voltage vector is then generated by a hybrid multilevel inverter by employing space vector modulation (SVM. The inverter high quality output voltage which leads to a high quality IM performances. Besides, the MLI switching losses is very low due to most of the power cell switches are operating at nearly fundamental frequency. Some selected simulation results are presented for system validation.
Control of torque direction by spinal pathways at the cat ankle joint.
Nichols, T R; Lawrence, J H; Bonasera, S J
1993-01-01
To study the biomechanics of the calcaneal tendon's complex insertion onto the calcaneus, we measured torque-time trajectories exerted by the triceps surae and tibialis anterior muscles in eight unanesthetized decerebrate cats using a multi-axis force-moment sensor placed at the ankle joint. The ankle was constrained to an angle of 110 degrees plantarflexion. Muscles were activated using crossed-extension (XER), flexion (FWR), and caudal cutaneous sural nerve (SNR) reflexes. Torque contributions of other muscles activated by these reflexes were eliminated by denervation or tenotomy. In two animals, miniature pressure transducers were implanted among tendon fibers from the lateral gastrocnemius (LG) muscle that insert straight into the calcaneus or among tendon fibers from the medial gastrocnemius (MG) that cross over and insert on the lateral aspect of calcaneus. Reflexively evoked torques had the following directions: FWR, dorsiflexion and adduction; SNR, plantarflexion and abduction; and XER, plantarflexion and modest abduction or adduction. The proportion of abduction torque to plantarflexion torque was always greater for SNR than XER; this difference was about 50% of the magnitude of abduction torque generated by tetanic stimulation of the peronei. During SNR, pressures were higher in regions of the calcaneal tendon originating from MG than regions originating from LG. Similarly, pressures within the MG portion of the calcaneal tendon were higher during SNR than during XER, although these two reflexes produced matched ankle plantarflexion forces. Selective tenotomies and electromyographic recordings further demonstrated that MG generated most of the torque in response to SNR, while soleus, LG, and MG all generated torques in response to XER.(ABSTRACT TRUNCATED AT 250 WORDS)
Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller
Sri Lal Senanayaka, Jagath; Karimi, Hamid Reza; Robbersmyr, Kjell G.
2016-09-01
In this paper. the method of direct torque control in the presence of a sliding-mode speed controller is proposed for a small wind turbine being used in water heating applications. This concept and control system design can be expanded to grid connected or off-grid applications. Direct torque control of electrical machines has shown several advantages including very fast dynamics torque control over field-oriented control. Moreover. the torque and flux controllers in the direct torque control algorithms are based on hvsteretic controllers which are nonlinear. In the presence of a sliding-mode speed control. a nonlinear control system can be constructed which is matched for AC/DC conversion of the converter that gives fast responses with low overshoots. The main control objectives of the proposed small wind turbine can be maximum power point tracking and soft-stall power control. This small wind turbine consists of permanent magnet synchronous generator and external wind speed. and rotor speed measurements are not required for the system. However. a sensor is needed to detect the rated wind speed overpass events to activate proper speed references for the wind turbine. Based on the low-cost design requirement of small wind turbines. an available wind speed sensor can be modified. or a new sensor can be designed to get the required measurement. The simulation results will be provided to illustrate the excellent performance of the closed-loop control system in entire wind speed range (4-25 m/s).
Elhadj BOUNADJA
2016-07-01
Full Text Available The present work examines a direct torque control strategy using a high order sliding mode controllers of a doubly-fed induction generator (DFIG incorporated in a wind energy conversion system and working in saturated state. This research is carried out to reach two main objectives. Firstly, in order to introduce some accuracy for the calculation of DFIG performances, an accurate model considering magnetic saturation effect is developed. The second objective is to achieve a robust control of DFIG based wind turbine. For this purpose, a Direct Torque Control (DTC combined with a High Order Sliding Mode Control (HOSMC is applied to the DFIG rotor side converter. Conventionally, the direct torque control having hysteresis comparators possesses major flux and torque ripples at steady-state and moreover the switching frequency varies on a large range. The new DTC method gives a perfect decoupling between the flux and the torque. It also reduces ripples in these grandeurs. Finally, simulated results show, accurate dynamic performances, faster transient responses and more robust control are achieved.
S. Priya
2013-02-01
Full Text Available Direct torque control of a 3 phase squirrel cage Induction motor though offers a good dynamic response and is free from dynamic coordinate transformation has a major disadvantage of producing rippled torque which degrades the performance of entire drive system. A scheme is proposed in this study which employs seven levels Neutral point clamped inverter which helps in alleviating the torque disturbances. The control scheme for the proposed method is described in this study and the simulation results are reported to demonstrate its effectiveness.
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2016-08-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS
Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail
2013-01-01
Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in
Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.
2012-04-01
The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the
Tarkan Erdik; Zekai Şen
2008-12-01
Singh et al (2005)examined the potential of the ANN and neuro-fuzzy systems application for the prediction of dynamic constant of rockmass. However,the model proposed by them has some drawbacks according to fuzzy logic principles.This discussion will focus on the main fuzzy logic principles which authors and potential readers should take into consideration.
Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles
无
2003-01-01
This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.
Adaptive Critic Based Neuro-Fuzzy Tracker for Improving Conversion Efficiency in PV Solar Cells
Halimeh Rashidi
2012-08-01
Full Text Available The output power of photovoltaic systems is directly related to the amount of solar energy collected by the system and it is therefore necessary to track the sun’s position with high accuracy. This study proposes multi-agent adaptive critic based nero fuzzy solar tracking system dedicated to PV panels. The proposed tracker ensures the optimal conversion of solar energy into electricity by properly adjusting the PV panels according to the position of the sun. To evaluate the usefulness of the proposed method, some computer simulations are performed and compared with fuzzy PD controller. Obtained results show the proposed control strategy is very robust, flexible and could be used to get the desired performance levels. The response time is also very fast. Simulation results that have been compared with fuzzy PD controller show that our method has the better control performance than fuzzy PD controller.
Intelligent security system based on neuro-fuzzy multisensor data fusion
Chen, Judy; Kostrzewski, Andrew A.; Kim, Dai Hyun; Kuo, Yih-Shi; Savant, Gajendra D.; Roberts, Barney B.
1998-10-01
This paper presents a real-world application of neurofuzzy processing to a security system with multiple sensor. Integrating fuzzy logic with neural networks, the authors have automated the tasks of sensor data fusion and determination of false/true alarms, which currently rely solely on human monitoring operators, so that they operate in a way similar to human reasoning. This integrated security system includes a set of heterogeneous sensor. To take advantage of each sensor's strengths, they are positioned and integrated for side, accurate, economical coverage. The system includes real-time tracking cameras functioning as true digital motion detectors with the capability of approximating the size, direction, and number of intruders. The system is also capable of real-time image segmentation based on motion, and of image recognition based on neural networks.
Zelechowski, M.; Kazmierkowski, M.P.; Blaabjerg, Frede
2005-01-01
In this paper two different methods of PI controllers for direct torque controlled-space vector modulated induction motor drives have been studied. The first one is simple method based only on symmetric optimum criterion. The second approach takes into account the full model of induction motor in...
Estimation of Switching Overvoltages on Transmission Lines Using Neuro-Fuzzy Method
Reza Shariatinasab
2012-11-01
Full Text Available Insulation failure caused by switching overvoltages (SOVs is one of the main sources of transmission lines’ outage, specially, on voltage levels of 345 kV and above. Therefore, the estimation of SOVs is vital in order to control and/or to reduce the switching–related outages. Due to the stochastic behavior of some of the parameters affecting on SOVs, the study of this phenomenon should be carried out based on a statistical study of the switching. Also, in the case of surge arrester installation on the transmission lines, depending on the location of arrester, voltage profile on line is changed and all the simulation should be performed for each new location of arresters, separately. One can conclude that this procedure is complex and time consuming. In this paper, a fuzzy based meta-model is presented which is be able to estimate the switching surge flashover rate (SSFOR, the maximum value of SOVs on the network and the location where the maximum overvoltage takes place. In the proposed meta model, the effect of altitude on SSFOR and the magnitude of SOVs is considered. This meta-model can be used, directly, for planning the insulation level of transmission lines in order to meet a certain number of outages and locating arresters on the region/nodes of the network of weak operation against SOVs. It is also possible to utilize the proposed meta model, indirectly, for assigning the optimal location of any specified set of arresters on the network without simulating of real network by a transient software, e.g. EMTP/ATP draw. The presented meta model can also be used in the operating stage to decide on the sequence of energizing and re-energizing of different transmission lines connected to the substations with the aim of reducing of maximum SOVs.
Sliding mode pulse-width modulation technique for direct torque controlled induction motor drive
Bounadja, M.; Belarbi, A. W.; Belmadani, B.
2010-05-01
This paper presents a novel pulse-width modulation technique based sliding mode approach for direct torque control of an induction machine drive. Methodology begins with a sliding mode control of machine's torque and stator flux to generate the reference voltage vector and to reduce parameters sensitivity. Then, the switching control of the three-phase inverter is developed using sliding mode concept to make the system tracking reference voltage inputs. The main features of the proposed methodologies are the high tracking accuracy and the much easier implementation compared to the space vector modulation. Simulations are carried out to confirm the effectiveness of proposed control algorithms.
High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives
Vasudevan M.
2005-01-01
Full Text Available This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i Development of Neural network algorithm for state selection in DTC; ii Development of new algorithm for state selection using Genetic algorithm principle; and iii Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.
Direct Torque Control for Three-Level Neutral Point Clamped Inverter-Fed Induction Motor Drive
M. K. Sahu
2012-04-01
Full Text Available Direct torque control (DTC is a control technique in AC drive systems to obtain high performance torque control. The classical DTC drive contains a pair of hysteresis comparators and suffers from variable switching frequency and high torque ripple. These problems can be solved by using space vector depending on the reference torque and flux. In this paper the space vector modulation technique is applied to the three-level Neutral Point Clamped (NPC inverter control in the proposed DTC-based induction motor drive system, resulting to a significant reduce of torque ripple. Three-level neutral point clamped inverters have been widely used in medium voltage applications. This type of inverters have several advantages over standard two-level VSI, such as greater number of levels in the output voltage waveforms, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper emphasizes the derivation of switching states using the Space Vector Pulse Width Modulation (SVPWM technique. The control scheme is implemented using Matlab/Simulink. Experimental results using dSPACE validate the steady-state and the dynamic performance of the proposed control strategy.
Design and Modeling Improved Direct Torque Control of Induction Motor Drive
Omid Moradi
2014-07-01
Full Text Available This paper studies most commonly used electric driving method of induction motors (IM. Direct torque control (DTC have been widely commercialized in induction motor drives, with each being favored by its supporters. In this paper, the dynamic performance of these drives for an electric vehicle application is examined, and sensitivities to parameter variations affecting this dynamic performance are explored by jacobian matrix in which the sensitivities of torque, speed, and other desired variables or outputs are estimated relative to change in motor parameters. Key performance measures include torque and speed transients. The switching scheme of these drive is a switching table. Both the overshoot and the settling time of DTC are really small
Luis Daniel Lledó
2015-03-01
Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.
Petković, Dalibor; Nikolić, Vlastimir; Milovančević, Miloš; Lazov, Lyubomir
2016-07-01
Heat affected zone (HAZ) of the laser cutting process may be developed on the basis on combination of different factors. In this investigation was analyzed the HAZ forecasting based on the different laser cutting parameters. The main aim in this article was to analyze the influence of three inputs on the HAZ of the laser cutting process. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for HAZ forecasting. Three inputs are considered: laser power, cutting speed and gas pressure. According the results the cutting speed has the highest influence on the HAZ forecasting (RMSE: 0.0553). Gas pressure has the smallest influence on the HAZ forecasting (RMSE: 0.0801). The results can be used in order to simplify HAZ prediction and analyzing.
Chau, K T; Chan, C C; Shen, W X
2003-01-01
This paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents.
El-Zoghby, Helmy M.; Bendary, Ahmed F.
2016-10-01
Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.
S. S, Pathak
2012-10-01
Full Text Available Self-compacting concrete is an innovative concrete that does not require vibration for placing and compaction. It is able to flow under its own weight, completely filling formwork and achieving full compaction even in congested reinforcement without segregation and bleeding. In the present study self compacting concrete mixes were developed using blend of fly ash and rice husk ash. Fresh properties of theses mixes were tested by using standards recommended by EFNARC (European Federation for Specialist Construction Chemicals and Concrete system. Compressive strength at 28 days was obtained for these mixes. This paper presents development of Adaptive Neuro-fuzzy Inference System (ANFIS model for predicting compressive strength of self compacting concrete using fly ash and rice husk ash. The input parameters used for model are cement, fly ash, rice husk ash and water content. Output parameter is compressive strength at 28 days. The results show that the implemented model is good at predicting compressive strength.
Zhu, Xiaojue; Verzicco, Roberto; Lohse, Detlef
2015-01-01
We present direct numerical simulations of Taylor-Couette flow with grooved walls at a fixed radius ratio $\\eta=r_i/r_o=0.714$ with inner cylinder Reynolds number up to $Re_i=3.76\\times10^4$, corresponding to Taylor number up to $Ta=2.15\\times10^9$. The grooves are axisymmetric V-shaped obstacles attached to the wall with a tip angle of $90^\\circ$. Results are compared with the smooth wall case in order to investigate the effects of grooves on Taylor-Couette flow. We focus on the effective scaling laws for the torque, flow structures, and boundary layers. It is found that, when the groove height is smaller than the boundary layer thickness, the torque is the same as that of the smooth wall cases. With increasing $Ta$, the boundary layer thickness becomes smaller than the groove height. Plumes are ejected from tips of the grooves and a secondary circulation between the latter is formed. This is associated to a sharp increase of the torque and thus the effective scaling law for the torque vs. $Ta$ becomes much ...
Active Speed Compensation Method of Direct Torque Control System and Stability Analysis
Rui Li
2015-02-01
Full Text Available By analyzing characteristics of the DTC (direct torque control system in electrical driving system, a shortcoming of the classical DTC method is to point out that it is unable to decouple the mutual interference between torque and speed, so that when a running asynchronous motor subjected to an instantaneous impact load, rotor speed and its deviation appears excessive fluctuations that can not be quickly restored to the initial set value. In this research, under conditions that without sensors for measuring load torque and rotor speed, to an electrical drive systems contains DTC devices, a novel ASCC (active speed compensation control method is proposed based on ADRC (active disturbance rejection control theory, on account of DTC model of asynchronous motor, a multiobjective observer is designed to regulate both the speed and the torque, and a proof of asymptotic stability that related this new control systems with the observer is made by theoretical deduction. Finally stimulating results show that this method can overcome the shortcomings of classical DTC system and greatly enhance the ability of the high-speed driving system to deal with unexpected impact loads.
Jovanovic, Milutin; Levi, Emil; Yu, James
2006-01-01
Presents the experimental verification of a new sensor-less control algorithm for direct torque (and flux) control (DTC) of the BDFRM in low variable frequency applications (e.g. wind energy conversion systems) where the low cost potential of the machine can be best exploited by using partially-rated power electronics. Brings a significant contribution to knowledge in the subject field as the proposed scheme has many important advantages over its counterparts in the target applications. Repre...
Flux observer algorithms for direct torque control of brushless doubly-fed reluctance machines
Chaal, Hamza; Jovanovic, Milutin
2009-01-01
Direct Torque Control (DTC) has been extensively researched and applied to most AC machines during the last two decades. Its first application to the Brushless Doubly-Fed Reluctance Machine (BDFRM), a promising cost-effective candidate for drive and generator systems with limited variable speed ranges (such as large pumps or wind turbines), has only been reported a few years ago. However, the original DTC scheme has experienced flux estimation problems and compromised performance under the ma...
A novel speed sensor-less direct torque control system for mining locomotive haulage
马宪民
2002-01-01
A novel speed sensor-less direct torque control induction motor drive system for the mining locomotive haulage is presented in the paper. Rotor speed identification is based on the model reference adaptive control theory with neural network using back propagation algorithm. The system is implemented using a real-time TMS320F240 digital signal processor. The simulation study and experiment results indicate that the suggested system has good performance.
BELMADANI, B.
2009-06-01
Full Text Available This paper proposes the design and implementation of a novel direct torque controlled induction machine drive system. The control system enjoys the advantages of stator vector control and conventional direct torque control and avoids some of the implementation difficulties of either of the two control methods. The stator vector control principal is used to keep constant the amplitude of stator flux vector at rated value, and to develop the relationship between the machine torque and the rotating speed of the stator flux vector. Thus, the machine torque can be regulated to generate the stator angular speed, which becomes a command signal and permits to overcome the problem of its estimation. Furthermore, with the combined control methods, the reference stator voltage vector can be generated and proportional-integral controllers and space vector modulation technique can be used to obtain fixed switching frequency and low torque ripple. Simulation experiments results indicate that, with the proposed scheme, a precise control of the stator flux and machine torque can be achieved. Compared to conventional direct torque control, presented method is easily implemented, and the steady performances of ripples of both torque and flux are considerably improved.
Super-twisting sliding mode direct torque contol of induction machine drives
Lascu, Cristian; Blaabjerg, Frede
2014-01-01
This paper presents a new super-twisting sliding modes direct torque and flux controller (STSM-DTC) for induction motor (IM) drives. The STSM is a second-order (type two) variable-structure control which operates without high-frequency chattering. The proposed STSM scheme is a torque and stator......-gain sliding-mode-like behavior. The experimental tests show that the STSM-DTC controller displays very robust behavior, similar to a conventional sliding controller, and it works without notable steady-state chattering, like the PI controller. The paper presents theoretical aspects for the new STSM......-DTC control, design and implementation details, and relevant experimental results for a sensorless IM drive. The scheme is compared to a second-order sliding mode controller and a linear PI controller. A robustness assessment against the PI controller is also included....
Super-twisting sliding mode direct torque contol of induction machine drives
Lascu, Cristian; Blaabjerg, Frede
2014-01-01
This paper presents a new super-twisting sliding modes direct torque and flux controller (STSM-DTC) for induction motor (IM) drives. The STSM is a second-order (type two) variable-structure control which operates without high-frequency chattering. The proposed STSM scheme is a torque and stator...... flux magnitude controller implemented in the stator flux reference frame, and it does not employ current controllers as in conventional vector control. This controller contains a design parameter that allows the designer to balance its operation between a linear PI-like behavior and a constant......-DTC control, design and implementation details, and relevant experimental results for a sensorless IM drive. The scheme is compared to a second-order sliding mode controller and a linear PI controller. A robustness assessment against the PI controller is also included....
Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach
Jinliang Zhang
2015-01-01
Full Text Available Extended Kalman filter (EKF has been widely applied for sensorless direct torque control (DTC in induction machines (IMs. One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs.
Application of neural networks for permanent magnet synchronous motor direct torque control
Zhang Chunmei; Liu Heping; Chen Shujin; Wang Fangjun
2008-01-01
Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC) is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.
Design and analysis of a direct-drive wind power generator with ultra-high torque density
Jian, Linni; Shi, Yujun; Wei, Jin; Zheng, Yanchong
2015-05-01
In order to get rid of the nuisances caused by mechanical gearboxes, generators with low rated speed, which can be directly connected to wind turbines, are attracting increasing attention. The purpose of this paper is to propose a new direct-drive wind power generator (DWPG), which can offer ultra-high torque density. First, magnetic gear (MG) is integrated to achieve non-contact torque transmission and speed variation. Second, armature windings are engaged to achieve electromechanical energy conversion. Interior permanent magnet (PM) design on the inner rotor is adopted to boost the torque transmission capability of the integrated MG. Nevertheless, due to lack of back iron on the stator, the proposed generator does not exhibit prominent salient feature, which usually exists in traditional interior PM (IPM) machines. This makes it with good controllability and high power factor as the surface-mounted permanent magnet machines. The performance is analyzed using finite element method. Investigation on the magnetic field harmonics demonstrates that the permanent-magnetic torque offered by the MG can work together with the electromagnetic torque offered by the armature windings to balance the driving torque captured by the wind turbine. This allows the proposed generator having the potential to offer even higher torque density than its integrated MG.
Simulation study on the starting characteristics of a kind of improved direct torque control system
YE Jin-jiao; MENG Qing-chun; GUO Rui
2004-01-01
Analyzed working principle and starting process of asynchronous motor direct torque control system. In order to solve the problem of serious fluctuation of starting current in traditional control scheme, the author puts forward a new kind of starting method which can limit voltage value to achieve the goal of reducing fluctuation of the current by increasing zero voltage vectors during the stage of starting. The author has also carried on simulation study on the system, and the result of simulation study shows that this method is effectual.
Ajay Kumar, M.; Srikanth, N.
2014-03-01
In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.
Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika
2014-08-01
The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.
Miranian, A; Abdollahzade, M
2013-02-01
Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.
Blanes-Vidal, Victoria; Cantuaria, Manuella Lech; Nadimi, Esmaeil S
2017-04-01
Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.
P. N. Raghunath
2012-01-01
Full Text Available Problem statement: This study presents the results of ANFIS based model proposed for predicting the performance characteristics of reinforced HSC beams subjected to different levels of corrosion damage and strengthened with externally bonded glass fibre reinforced polymer laminates. Approach: A total of 21 beams specimens of size 150, 250×3000 mm were cast and tested. Results: Out of the 21 specimens, 7 specimens were tested without any corrosion damage (R-Series, 7 after inducing 10% corrosion damage (ASeries and another 7 after inducing 25% corrosion damage (B-Series. Out of the seven specimens in each series, one was tested without any laminate, three specimens were tested after applying 3 mm thick CSM, UDC and WR laminates and another three specimens after applying 5mm thick CSM, UDC and WR laminates. Conclusion/Recommendations: The test results show that the beams strengthened with externally bonded GFRP laminates exhibit increased strength, stiffness, ductility and composite action until failure. An Adaptive Neuro-Fuzzy Inference System (ANFIS model is developed for predicting the study parameters for input values lying within the range of this experimental study.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population.
Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency.
Ghanei, S., E-mail: Sadegh.Ghanei@yahoo.com [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Vafaeenezhad, H. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Kashefi, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of); Eivani, A.R. [Centre of Excellence for High Strength Alloys Technology (CEHSAT), School of Metallurgical and Materials Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran (Iran, Islamic Republic of); Mazinani, M. [Department of Materials Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square, Mashhad (Iran, Islamic Republic of)
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency. - Highlights: • New NDT system for microstructural evaluation based on MBN using ANFIS modeling. • Sensitivity of magnetic Barkhausen noise to microstructure changes of the DP steels. • Accurate prediction of martensite by feeding multiple MBN outputs simultaneously. • Obtaining the modeled output without knowing the amount of the used frequency.
Hoell, Simon; Omenzetter, Piotr
2016-04-01
Fueled by increasing demand for carbon neutral energy, erections of ever larger wind turbines (WTs), with WT blades (WTBs) with higher flexibilities and lower buckling capacities lead to increasing operation and maintenance costs. This can be counteracted with efficient structural health monitoring (SHM), which allows scheduling maintenance actions according to the structural state and preventing dramatic failures. The present study proposes a novel multi-step approach for vibration-based structural damage localization and severity estimation for application in operating WTs. First, partial autocorrelation coefficients (PACCs) are estimated from vibrational responses. Second, principal component analysis is applied to PACCs from the healthy structure in order to calculate scores. Then, the scores are ranked with respect to their ability to differentiate different damage scenarios. This ranking information is used for constructing hierarchical adaptive neuro-fuzzy inference systems (HANFISs), where cross-validation is used to identify optimal numbers of hierarchy levels. Different HANFISs are created for the purposes of structural damage localization and severity estimation. For demonstrating the applicability of the approach, experimental data are superimposed with signals from numerical simulations to account for characteristics of operational noise. For the physical experiments, a small scale WTB is excited with a domestic fan and damage scenarios are introduced non-destructively by attaching small masses. Numerical simulations are also performed for a representative fully functional small WT operating in turbulent wind. The obtained results are promising for future applications of vibration-based SHM to facilitate improved safety and reliability of WTs at lower costs.
Nikolić, Vlastimir; Petković, Dalibor; Lazov, Lyubomir; Milovančević, Miloš
2016-07-01
Water-jet assisted underwater laser cutting has shown some advantages as it produces much less turbulence, gas bubble and aerosols, resulting in a more gentle process. However, this process has relatively low efficiency due to different losses in water. It is important to determine which parameters are the most important for the process. In this investigation was analyzed the water-jet assisted underwater laser cutting parameters forecasting based on the different parameters. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for water-jet assisted underwater laser cutting parameters forecasting. Three inputs are considered: laser power, cutting speed and water-jet speed. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the water-jet assisted underwater laser cutting parameters. According to the results the combination of laser power cutting speed forms the most influential combination foe the prediction of water-jet assisted underwater laser cutting parameters. The best prediction was observed for the bottom kerf-width (R2 = 0.9653). The worst prediction was observed for dross area per unit length (R2 = 0.6804). According to the results, a greater improvement in estimation accuracy can be achieved by removing the unnecessary parameter.
Tao, Yang; Li, Yong; Zhou, Ruiyun; Chu, Dinh-Toi; Su, Lijuan; Han, Yongbin; Zhou, Jianzhong
2016-10-01
In the study, osmotically dehydrated cherry tomatoes were partially dried to water activity between 0.746 and 0.868, vacuum-packed and stored at 4-30 °C for 60 days. Adaptive neuro-fuzzy inference system (ANFIS) was utilized to predict the physicochemical and microbiological parameters of these partially dried cherry tomatoes during storage. Satisfactory accuracies were obtained when ANFIS was used to predict the lycopene and total phenolic contents, color and microbial contamination. The coefficients of determination for all the ANFIS models were higher than 0.86 and showed better performance for prediction compared with models developed by response surface methodology. Through ANFIS modeling, the effects of storage conditions on the properties of partially dried cherry tomatoes were visualized. Generally, contents of lycopene and total phenolics decreased with the increase in water activity, temperature and storage time, while aerobic plate count and number of yeasts and molds increased at high water activities and temperatures. Overall, ANFIS approach can be used as an effective tool to study the quality decrease and microbial pollution of partially dried cherry tomatoes during storage, as well as identify the suitable preservation conditions.
Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo
2013-03-01
The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.
Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.
2011-08-01
Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.
Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.
2014-10-01
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
AA Sabziparvar
2011-03-01
Full Text Available Solar radiation is an important climate parameter which can affect hydrological and meteorological processes. This parameter is a key element in development of solar energy application studies. The purpose of this study is the assessment of artificial intelligence techniques in prediction of solar radiation (Rs using artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. Minimum temperature, maximum temperature, average relative humidity, sunshine hours and daily solar radiation recorded in four synoptic stations (Esfahan, Urmieh, Shiraz and Kerman were used during the period 1992-2006. The results showed that ANN and ANFIS intelligent models are powerful tools in prediction of global solar radiation for the selected stations. Prediction by ANN was found to be more accurate than ANFIS. Also, the accuracy of prediction in Kerman with higher sunny hours was better than other stations (R2> 0.9. Additionally, using linear regression model, the most effective factors affecting Rs in each site was introduced. The results revealed that sunshine hour is the most important determining parameter affecting surface solar radiation. In contrast, in most sites minimum air temperature and mean relative humidity showed the least effect on surface global solar radiation.
D Panigrahy; P K Sahu
2015-06-01
Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother’s skin. The abdominal ECG is considered to be composite as it contains both mother’s and fetus’ ECG signals. We use extended Kalman filter framework to estimate the maternal component from abdominal ECG. The maternal component in the abdominal ECG signal is a nonlinear transformed version of maternal ECG. ANFIS network has been used to identify this nonlinear relationship, and to align the estimated maternal ECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the fetal ECG component by subtracting the aligned version of the estimated maternal ECG from the abdominal signal. Our results demonstrate the effectiveness of the proposed technique in extracting the fetal ECG component from abdominal signal at different noise levels. The proposed technique is also validated on the extraction of fetal ECG from both actual abdominal recordings and synthetic abdominal recording.
Aghajani, Khadijeh; Tayebi, Habib-Allah
2017-01-01
In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH = 2, time = 60 min and adsorbent dose = 1 g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).
Hassan Farhan Rashag
2013-04-01
Full Text Available Various aspects related to controlling induction motor are investigated. Direct torque control is an original high performance control strategy in the field of AC drive. In this proposed method, the control system is based on Space Vector Modulation (SVM, amplitude of voltage in direct- quadrature reference frame (d-q reference and angle of stator flux. Amplitude of stator voltage is controlled by PI torque and PI flux controller. The stator flux angle is adjusted by rotor angular frequency and slip angular frequency. Then, the reference torque and the estimated torque is applied to the input of PI torque controller and the control quadrature axis voltage is determined. The control d-axis voltage is determined from the flux calculator. These q and d axis voltage are converted into amplitude voltage. By applying polar to Cartesian on amplitude voltage and stator flux angle, direct voltage and quadratures voltage are generated. The reference stator voltages in d-q are calculated based on forcing the stator voltage error to zero at next sampling period. By applying inverse park transformation on d-q voltages, the stator voltages in &alpha and &beta frame are generated and apply to SVM. From the output of SVM, the motor control signal is generated and the speed of the induction motor regulated toward the rated speed. The simulation Results have demonstrated exceptional performance in steady and transient states and shows that decrease of torque and flux ripples is achieved in a complete speed range.
Design of direct drive robot using indigenously developed d.c. torque motors
Athani, Vithal V.
The range of high-performance torque motors, which were indigenously developed for use in multistage satellite launch vehicles, is described. The main features that set dc torque motors apart from dc servomotors are: high peak torque, power, and current over short periods of operation, low speed of operation, obviating the need for gearing, high torque/inertia and torque/weight ratios, and high figure of merit = torque/sq rt watt ratio. The dc torque motors are eminently suited to high-performance applications requiring high torque at low speed of operation, such as aircraft and missile control surface actuation, control of multistage satellite launch vehicles, certain computer peripherals like magnetic tape transports and hard disk drives, and robotics, CNC systems, and machine tool control.
Directly resolving particles in an electric field: local charge, force, torque, and applications
Liu, Qianlong
2011-11-01
Prosperetti's seminal Physalis method for fluid flows with suspended particles is extended to electric fields to directly resolve finite-sized particles and to investigate accurately the mutual fluid-particle, particle-particle, and particle-boundary interactions. The method can be used for uncharged/charged dielectrics, uncharged/charged conductors, conductors with specified voltage, and general weak and strong discontinuous interface conditions. These interface conditions can be in terms of field variable, its gradients, and surface integration which has not been addesed by other numerical methods. In addition, for the first time, we rigorously derive the force and torque on the finite-sized particles resulting from the interactions between harmonics. The method, for the first time, directly resolves the particles with accurate local charge distribution, force, and torque on the particles, making many applications in engineering, mechanics, physics, chemistry, and biology possible, such as heterogeneous materials, microfluidics, electrophotography, electric double layer capacitors, and microstructures of nanodispersions. The efficiency of the method is demonstrated with up to one hundred thousand 3D particles, which suggests that the method can be used for many important engineering applications of broad interest. This research is supported by the Department of Energy under funding for an EFRC (the HeteroFoaM Center), grant no. DE-SC0001061.
孙鹏
2014-01-01
Direct torque control has been applied widely due to its simple arithmetic, fast transient response and robust stability to parameter changed. As for the conventional direct torque control system for asynchronous motors, there is the effect of voltage space vector on the magnitude of stator flux and flux angle, especially there are large ripples at low speed. Aiming at this issue and based on conventional DTC, a new control strategy is proposed in this paper, this strategy combines flux linkage section subdivision control with synthesizing vectors and can improve the torque response time by introducing Fussy control algorithm, thereby reducing torque ripples effectively. Simulation results show that the strategy can greatly reduce the torque ripples and has a better dynamic and steady performance.%直接转矩控制具有控制简单、动态响应迅速、对参数变化鲁棒性强的特点，因此得到了广泛的应用。在传统的异步电动机直接转矩控制系统中，存在电压空间矢量对定子磁链幅值和磁通角的影响，特别是低速时系统脉动大。针对此问题，文章提出了一种的新的控制方法，该方法将磁链区间细分控制与电压矢量合成结合在一起，并通过引入模糊控制算法进一步提高了转矩响应时间，且减小了转矩脉动。仿真结果表明，本控制方法可以大大减小转矩脉动，具有较好的动静态性能。
Direct Numerical Simulations of Local and Global Torque in Taylor-Couette Flow up to Re=30.000
Brauckmann, Hannes
2015-01-01
The torque in turbulent Taylor-Couette flows for shear Reynolds numbers Re_S up to 3x10^4 at various mean rotations is studied by means of direct numerical simulations for a radius ratio of \\eta=0.71. Convergence of simulations is tested using three criteria of which the agreement of dissipation values estimated from the torque and from the volume dissipation rate turns out to be most demanding. We evaluate the influence of Taylor vortex heights on the torque for a stationary outer cylinder and select a value of the aspect ratio of \\Gamma=2, close to the torque maximum. The connection between the torque and the transverse current J^\\omega of azimuthal motion which can be computed from the velocity field enables us to investigate the local transport resulting in the torque. The typical spatial distribution of the individual convective and viscous contributions to the local current is analysed for a turbulent flow case. To characterise the turbulent statistics of the transport, PDF's of local current fluctuatio...
Low torque hydrodynamic lip geometry for bi-directional rotation seals
Dietle, Lannie L.; Schroeder, John E.
2009-07-21
A hydrodynamically lubricating geometry for the generally circular dynamic sealing lip of rotary seals that are employed to partition a lubricant from an environment. The dynamic sealing lip is provided for establishing compressed sealing engagement with a relatively rotatable surface, and for wedging a film of lubricating fluid into the interface between the dynamic sealing lip and the relatively rotatable surface in response to relative rotation that may occur in the clockwise or the counter-clockwise direction. A wave form incorporating an elongated dimple provides the gradual convergence, efficient impingement angle, and gradual interfacial contact pressure rise that are conducive to efficient hydrodynamic wedging. Skewed elevated contact pressure zones produced by compression edge effects provide for controlled lubricant movement within the dynamic sealing interface between the seal and the relatively rotatable surface, producing enhanced lubrication and low running torque.
Kar, Subrata; Majumder, D Dutta
2017-08-01
Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one
Asnaashari, Maryam; Farhoosh, Reza; Farahmandfar, Reza
2016-10-01
As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). The results showed ANFIS was the best model with high coefficient of determination (R(2) = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models. Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.
Kentel, E.; Dogulu, N.
2015-12-01
In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.
Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F
2014-10-15
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
Jayant P. Sangole; Gopal R. Patil
2014-01-01
Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun-tries. Intersections with no specific priority to any move-ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom-etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neuro-fuzzy interface system (ANFIS) to the modeling of gap acceptance behavior of right-turning vehicles at limited priority T-intersections (in India, vehicles are driven on the left side of a road). Field data are collected using video cameras at four T-intersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver’s decision (accepted/rejected). ANFIS models are developed by using 80% of the extracted data (total data observations for major road right-turning vehicles are 722 and 1,066 for minor road right-turning vehicles) and remaining are used for model vali-dation. Four different combinations of input variables are considered for major and minor road right turnings sepa-rately. Correct prediction by ANFIS models ranges from 75.17% to 82.16% for major road right turning and 87.20% to 88.62% for minor road right turning. The models developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.
Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems
Preindl, Matthias; Schaltz, Erik
2010-01-01
behaviour. It compensates the load torque influence on the speed control setting a feed forward torque value, i.e. current reference value. The benefits are twice. The speed controller reaches immediately the speed reference value avoiding offsets which must be compensated by the weak integrator. Moreover......, a better response to load torque variations which are detected and compensated leading to small speed variations is obtained....
Asmussen, M J; Bailey, A Z; Nelson, A J
2015-12-17
The neural command required to coordinate a multi-joint movement is inherently complex. During multi-joint movement of the limb, the force created from movement at one joint may create a torque at a second joint known as an interaction torque. Interaction torques may be assistive or resistive thereby aiding or opposing the motion of the second joint, respectively. For movement to be effectively controlled, the central nervous system should modulate neural output to the muscles to appropriately account for interaction torques. The present study examined the neural output from the primary motor cortex before and during reaching movements that required different combinations of assistive and resistive interaction torques occurring at the shoulder and elbow joints. Using transcranial magnetic stimulation to probe neural output from the primary motor cortex, results indicate that corticospinal output controlling the upper arm is related to resistive interaction torques occurring at the shoulder joint. Further, cortical output to bi-articular muscles is associated with interaction torque and this may be driven by the fact that these muscles are in an advantageous position to control torques produced between inter-connection segments. Humans have a tendency to avoid reaching movements that involve resistive interaction torques and this may be driven by the requirement of increased neural output associated with these movements.
CHEN Yong-jun; HUANG Sheng-hua; WAN Shan-ming; WU Fang
2008-01-01
A high-performance digital servo system built on the platform of a field programmable gate array (FPGA), a fully digitized hardware design scheme of a direct torque control (DTC) and a low speed permanent magnet synchronous motor (PMSM) is proposed. The DTC strategy of PMSM is described with Verilog hardware description language and is employed on-chip FPGA in accordance with the electronic design automation design methodology. Due to large torque ripples in low speed PMSM, the hysteresis controller in a conventional PMSM DTC was replaced by a fuzzy controller. This FPGA scheme integrates the direct torque controller strategy, the time speed measurement algorithm, the fuzzy regulating technique and the space vector pulse width modulation principle. Experimental results indicate the fuzzy controller can provide a controllable speed at 20 r min-1 and torque at 330 N m with satisfactory dynamic and static performance. Furthermore, the results show that this new control strategy decreases the torque ripple drastically and enhances control performance.
Lascu, Christian; Boldea, Ion; Blaabjerg, Frede
2004-01-01
A family of variable-structure controllers for induction machine drives is presented, in which the principles of direct torque control (DTC), variable-structure control (VSC) and space-vector pulsewidth modulation are combined to ensure high-performance operation, both in the steady state and under...
A. Noriega
2005-01-01
Full Text Available En este trabajo se presentan algunos esquemas de control neuro-difuso para el diseño de un controlador difuso simplificado de dos entradas y una salida. La simplificación introducida ha permitido lograr una importante reducción en el tiempo de cálculo de la señal de control, pero es posible que en algunos sistemas se pueda afectar el desempeño del sistema de control. Para resolver este problema se ha incorporado una red neuronal de manera que se pueda mejorar la calidad en el control y se pueda controlar procesos de dinámica compleja. Los resultados de las aplicaciones demuestran que se puede disponer de una metodología de control neuro-difuso general, aplicable a cualquier sistema.In this work some neuro-fuzzy control schemes for the design of a simplified controller of two inputs and one output are presented. This simplification has allowed getting an important reduction in the calculation control time but it is possible that this can affect the performance of the control system. To solve this problem a neural network has been incorporated so that the control quality can be improved and problems of complex dynamics can be solved. The results of the applications show that it is possible to have a neuro-fuzzy control methodology applicable to any system.
Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems
Preindl, Matthias; Schaltz, Erik
2011-01-01
to further improve dynamic behavior. It compensates the load torque influence on the speed control setting a feed forward torque reference value. The benefits are twice; the speed controller reaches the speed reference value without offsets which would need to be compensated by an integrator and a better...... response to load torque variations is obtained since they are detected and compensated leading to small speed variations. Moreover, the influence of parameter errors and disturbances has been analyzed and limited so that they play a minor role in operation....
Harmonic reduction of Direct Torque Control of six-phase induction motor.
Taheri, A
2016-07-01
In this paper, a new switching method in Direct Torque Control (DTC) of a six-phase induction machine for reduction of current harmonics is introduced. Selecting a suitable vector in each sampling period is an ordinal method in the ST-DTC drive of a six-phase induction machine. The six-phase induction machine has 64 voltage vectors and divided further into four groups. In the proposed DTC method, the suitable voltage vectors are selected from two vector groups. By a suitable selection of two vectors in each sampling period, the harmonic amplitude is decreased more, in and various comparison to that of the ST-DTC drive. The harmonics loss is greater reduced, while the electromechanical energy is decreased with switching loss showing a little increase. Spectrum analysis of the phase current in the standard and new switching table DTC of the six-phase induction machine and determination for the amplitude of each harmonics is proposed in this paper. The proposed method has a less sampling time in comparison to the ordinary method. The Harmonic analyses of the current in the low and high speed shows the performance of the presented method. The simplicity of the proposed method and its implementation without any extra hardware is other advantages of the proposed method. The simulation and experimental results show the preference of the proposed method.
Mustapha MESSAOUDI
2008-06-01
Full Text Available In this paper, the classical Direct Torque Control (DTC of Induction Motor (IM using an open loop pure integration suffers from the well-known problems of integration especially in the low speed operation range is detailed. To tackle this problem, the IM variables and parameters estimation is performed using a recursive non-linear observer known as EKF. This observer is used to estimate the stator currents, the rotor flux linkages, the rotor speed and the stator resistance. The main drawback of the EKF in this case is that the load dynamics has to be known which is not usually possible. Therefore, a new method based on the Model Reference Adaptive System (MRAS is used to estimate the rotor speed. The two different nonlinear observers applied to sensorless DTC of IM, are discussed and compared to each other. The rotor speed estimation in DTC technique is affected by parameter variations especially the stator resistance due to temperature particularly at low speeds. Therefore, it is necessary to compensate this parameter variation in sensorless induction motor drives using an online adaptation of the control algorithm by the estimated stator resistance. A simulation work leads to the selected results to support the study findings.
Chaoying Xia
2016-11-01
Full Text Available Compared to the doubly fed machine, the brushless doubly fed machine (BDFM has high reliability and low maintenance requirements. First, by taking the negative conjugation of the control motor variables in rotor reference frame, a state-space model of BDFM is derived. It is then transformed into synchronous reference frame, called synchronous reference frame state-space model (SSSM. In this way, all the variables of the SSSM are DC under the static state. Second, on the basis of the analysis of static equations, the possible output torque limits are obtained. Third, the causes of losing control are analyzed by the flux and the torque derivatives. A new control strategy called synthetic vector direct torque control (SVDTC is proposed to solve the losing control problems of the conventional direct torque control (DTC. Finally, the correctness of the results of this paper is verified by calculation examples and simulation results, the losing control problems can be solved, and the theoretical output capacity limits can be reached using SVDTC.
Pyrhoenen, O.
1998-12-31
Direct torque control (DTC) is a new control method for rotating field electrical machines. DTC controls directly the motor stator flux linkage with the stator voltage, and no stator current controllers are used. With the DTC method very good torque dynamics can be achieved. Until now, DTC has been applied to asynchronous motor drives. The purpose of this work is to analyse the applicability of DTC to electrically excited synchronous motor drives. Compared with asynchronous motor drives, electrically excited synchronous motor drives require an additional control for the rotor field current. The field current control is called excitation control in this study. The dependence of the static and dynamic performance of DTC synchronous motor drives on the excitation control has been analysed and a straightforward excitation control method has been developed and tested. In the field weakening range the stator flux linkage modulus must be reduced in order to keep the electro motive force of the synchronous motor smaller than the stator voltage and in order to maintain a sufficient voltage reserve. The dynamic performance of the DTC synchronous motor drive depends on the stator flux linkage modulus. Another important factor for the dynamic performance in the field weakening range is the excitation control. The field weakening analysis considers both dependencies. A modified excitation control method, which maximises the dynamic performance in the field weakening range, has been developed. In synchronous motor drives the load angle must be kept in a stabile working area in order to avoid loss of synchronism. The traditional vector control methods allow to adjust the load angle of the synchronous motor directly by the stator current control. In the DTC synchronous motor drive the load angle is not a directly controllable variable, but it is formed freely according to the motor`s electromagnetic state and load. The load angle can be limited indirectly by limiting the torque
Zhou, Ping; Suresh, Nina L.; Zev Rymer, William
2011-06-01
The objective of this study was to determine whether a novel technique using high density surface electromyogram (EMG) recordings can be used to detect the directional dependence of muscle activity in a multifunctional muscle, the first dorsal interosseous (FDI). We used surface EMG recordings with a two-dimensional electrode array to search for inhomogeneous FDI activation patterns with changing torque direction at the metacarpophalangeal joint, the locus of action of the FDI muscle. The interference EMG distribution across the whole FDI muscle was recorded during isometric contraction at the same force magnitude in five different directions in the index finger abduction-flexion plane. The electrode array EMG activity was characterized by contour plots, interpolating the EMG amplitude between electrode sites. Across all subjects the amplitude of the flexion EMG was consistently lower than that of the abduction EMG at the given force. Pattern recognition methods were used to discriminate the isometric muscle contraction tasks with a linear discriminant analysis classifier, based on the extraction of two different feature sets of the surface EMG signal: the time domain (TD) feature set and a combination of autoregressive coefficients and the root mean square amplitude (AR+RMS) as a feature set. We found that high accuracies were obtained in the classification of different directions of the FDI muscle isometric contraction. With a monopolar electrode configuration, the average overall classification accuracy from nine subjects was 94.1 ± 2.3% for the TD feature set and 95.8 ± 1.5% for the AR+RMS feature set. Spatial filtering of the signal with bipolar electrode configuration improved the average overall classification accuracy to 96.7 ± 2.7% for the TD feature set and 98.1 ± 1.6% for the AR+RMS feature set. The distinct EMG contour plots and the high classification accuracies obtained from this study confirm distinct interference EMG pattern distributions as a
Kalaivani Lakshmanan
2014-01-01
Full Text Available In this paper, various intelligent controllers such as Fuzzy Logic Controller (FLC and Adaptive Neuro Fuzzy Inference System (ANFIS-based current compensating techniques are employed for minimizing the torque ripples in switched reluctance motor. FLC and ANFIS controllers are tuned using MATLAB Toolbox. For the purpose of comparison, the performance of conventional Proportional-Integral (PI controller is also considered. The statistical parameters like minimum, maximum, mean, standard deviation of total torque, torque ripple coefficient and the settling time of speed response for various controllers are reported. From the simulation results, it is found that both FLC and ANFIS controllers gives better performance than PI controller. Among the intelligent controllers, ANFIS gives outer performance than FLC due to its good learning and generalization capabilities thereby improves the dynamic performance of SRM drives.
Achalhi, A.; Bezza, M.; Belbounaguia, N.; Boujoudi, B.
2017-03-01
The performances of Direct Torque Control (DTC) of Induction machine are highly related to the inverter used therewith. The purpose of this paper is to highlight the efficiency of the space vector modulation (SVM) control of three level inverter associated with the direct torque control. The first part of this work is devoted to present the mathematical models of the DTC associated with 2-levels inverter then 3-levels inverter. Simulations on Matlab/Simulink will allow a comparative study to highlight advantages of the use of three levels inverter. The second part is devoted to the improvement of the DTC associated with a 3-levels inverter by application of the space vector modulation strategy (SVM) in order to manage the switching frequency and reduce harmonics. The efficiency of this solution will be attested by simulation on Matlab/Simulink.
A. J. Arbi
2008-03-01
Full Text Available This paper presents a study of current sensor failure in a Direct Torque Control applied to a Double Fed Induction Generator based Variable Speed Wind System. The effect of scaling and offset current sensor errors is discussed through sensibility analysis. A control reconfiguration is then proposed to remedy this sensor failure. Simulation results emphasize the good performances of the proposed current sensor fault tolerant control
Weiran Wang
2013-06-01
Full Text Available In order to improve the performance of bearingless brushless DC motor, a closed-loop suspended force controller combining the discrete space voltage vector modulation is applied and the direct torque control is presented in this paper. Firstly, we increase the number of the control vector to reduce the torque ripple. Then, the suspending equation is constructed which is spired by the direct torque control algorithm. As a result, the closed-loop suspended force controller is built. The simulated and experimental results evaluate the performance of the proposed method. The more advantage is that the proposed algorithm can achieve the fast torque response, reduce the torque ripple, and follow ideal stator flux track. Furthermore, the motor which implants the closed-loop suspended force controller cannot onlyobtain the dynamic response rapidly and displacement control accurately, but also has the characteristics of bearingless brushless DC motor (such as simple structure, high energy efficiency, small volume and low failure rate.
Débora Zenaide Gorri Mazzali
2015-01-01
Técnicas de Inteligência Artificial (IA) buscam imitar o raciocínio humano através da aplicação de regras lógicas, para um conjunto de dados disponível, de modo a chegar a uma forma mais eficiente de resolver problemas. Sendo um dos ramos da IA, a técnica neuro-fuzzy abordada neste estudo, será aplicada em controladores de processos que, por sua vez, são formados por estruturas de regras lógicas de difícil definição, pois existem inúmeras possibilidades de configurações que podem ser adotadas...
Zhang, Rui-Qin; Qi, Fei; Hermann, Klaus; Van Hove, Michel A
2016-01-01
Torque is ubiquitous in many molecular systems, including collisions, chemical reactions, vibrations, electronic excitations and especially rotor molecules. We present a straightforward theoretical method based on forces acting on atoms and obtained from atomistic quantum mechanics calculations, to quickly and qualitatively determine whether a molecule or sub-unit thereof has a tendency to rotation and, if so, around which axis and in which sense: clockwise or counterclockwise. The method also indicates which atoms, if any, are predominant in causing the rotation. Our computational approach can in general efficiently provide insights into the rotational ability of many molecules and help to theoretically screen or modify them in advance of experiments or before analyzing their rotational behavior in more detail with more extensive computations guided by the results from the torque approach. As an example, we demonstrate the effectiveness of the approach using a specific light-driven molecular rotary motor whi...
孙笑辉; 韩曾晋
2001-01-01
Due to the large torque ripple of inductance motor based on direct torque control，especially in alow speed，this paper proposes a new control strategy．On the basis of conventional direct torque control，atorque ripple minimum controller is introduced in the strategy in order to decrease torque ripple．The simulationshows that it can get good torque performance．%针对基于直接转矩控制的感应电动机在低速运转时存在较大的转矩脉动问题，提出一种新的控制方法。该方法是在利用传统直接转矩控制原理的基础上，引入一个转矩脉动最小化控制器，以减小感应电动机的转矩脉动。仿真试验说明了该方法能够很好解决转矩脉动问题。
Blaabjerg, Frede; Andreescu, G.-D.; Pitic, C.I.;
2008-01-01
This paper proposes a motion-sensorless control system using direct torque control with space vector modulation for interior permanent magnet synchronous motor (IPMSM) drives, for wide speed range operation, including standstill. A novel stator flux observer with variable structure uses a combined...... voltage-current model with PI compensator for low-speed operations. As speed increases, the observer switches gradually to a PI compensated closed-loop voltage model, which is solely used at high speeds. High-frequency rotating-voltage injection with a single D-module bandpass vector filter and a phase...
贺德华; 刘国荣; 韦婷华; 徐美清; 曹时德; 周桂珍
2011-01-01
在传统的异步电动机直接转矩控制系统中,存在电压空间矢量对定子磁链幅值和磁通角的影响,特别是低速时系统脉动大.针对该问题,提出了一种的新的控制方法,将磁链区间细分控制与电压矢量合成相结合,并且为进一步提高转矩响应和减小转矩脉动,引入了模糊控制.仿真结果表明该控制方法可以大大减小转矩脉动,具有较好的动静态性能.%Considering the influence of voltage space vector on the magnitude of stator flux and the flux angle in the conventional direct torque control for induction motors especially large ripples at low speed, a new control strategy was presented in this paper. This strategy combined flux linkage section subdivide control with synthesizing vectors which can reduce torque ripples effectively. Fuzzy control was also introduced to improve torque response and decrease torque ripples. Simulation results show that a great reduction of torque ripples is achieved and the strategy has a better dynamic and steady performance.
基于空间矢量调制的直接转矩控制%Direct torque control based on space vector modulation
景晓东; 高赟; 李燕涛; 王坤
2013-01-01
针对传统的直接转矩控制存在较大的转矩和磁链脉动问题,提出了一种基于空间矢量调制的直接转矩控制策略.该控制策略采用磁链、转矩PI控制器代替传统直接转矩控制系统中的滞环比较器,以空间矢量脉宽调制代替电压矢量开关表合成电压矢量,来补偿磁链误差和转矩误差,达到消除滞环脉动的目的.Matlab/Simulink仿真结果表明,基于空间矢量调制的电动机控制系统相对于传统的直接转矩控制系统,磁链轨迹更接近圆形,转矩、磁链和电流响应脉动更小.%In view of problems of big ripple of torque and flux linkage of traditional direct torque control,the paper proposed a control strategy of direct torque control based on space vector modulation.The control strategy uses flux and torque PI controller to instead of hysteresis comparator of traditional direct torque control system,and adopts space vector pulse width modulation to instead of voltage vector switch to synthetic voltage vector which can compensate for flux error and torque error to avoid hysteresis ripple.The Matlab/Simulink simulation result shows that the flux truck of control system of motor based on space vector pulse width modulation is more closed to round,and its torque and flux and current ripple is smaller compared with the traditional direct torque control system.
Simon N. Pearson
2016-06-01
Full Text Available Grinding is a key physical element in America’s Cup sailing. This study aimed to describe kinematics and muscle activation patterns in relation to torque applied in forward and backward grinding. Ten male America’s Cup sailors (33.6 ± 5.7 years, 97.9 ± 13.4 kg, 186.6 ± 7.4 cm completed forward and backward grinding on a customised grinding ergometer. In forward grinding peak torque (77 Nm occurred at 95° (0° = crank vertically up on the downward section of the rotation at the end of shoulder flexion and elbow extension. Backward grinding torque peaked at 35° (69 Nm following the pull action (shoulder extension, elbow flexion across the top of the rotation. During forward grinding, relatively high levels of torque (>50 Nm were maintained through the majority (72% of the cycle, compared to 47% for backward grinding, with sections of low torque corresponding with low numbers of active muscles. Variation in torque was negatively associated with forward grinding performance (r = −0.60; 90% CI −0.88 to −0.02, but positively associated with backward performance (r = 0.48; CI = −0.15 to 0.83. Magnitude and distribution of torque generation differed according to grinding direction and presents an argument for divergent training methods to improve forward and backward grinding performance.
Lucimar M.F. de Carvalho
2008-06-01
Full Text Available OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication architecture and an artificial neural network with backpropagation learning algorithm (ANNB. RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%; the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.OBJETIVO: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. MÉTODO: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB. RESULTADOS: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%; o melhor resultado de especificidade foi obtido com a RNAB (95,65%. CONCLUSÃO: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema.
Experimental Investigations on the Influence of Flux Control Loop in a Direct Torque Control Drive.
bhoopendra singh
2012-10-01
Full Text Available
Accurate flux estimation and control of stator flux by the flux control loop is the determining factor in effective implementation of DTC algorithm. In this paper a comparison of voltage model based flux estimation techniques for flux response improvement is carried out. The effectiveness of these methods is judged on the basis of Root Mean Square Flux Error (RMSFE and Total Harmonic Distortion (THD of stator current. The theoretical aspects of these methods are discussed and a comparative analysis is provided with emphasis on digital signal processor (DSP based controller implementation. Further the effect of operating flux on the performance of induction motor drive in terms of dynamic response, torque ripple and efficiency of operation is carried out. The proposed investigation is experimentally validated on a test drive.
A Two-stage Kalman Filter for Sensorless Direct Torque Controlled PM Synchronous Motor Drive
Boyu Yi
2013-01-01
Full Text Available This paper presents an optimal two-stage extended Kalman filter (OTSEKF for closed-loop flux, torque, and speed estimation of a permanent magnet synchronous motor (PMSM to achieve sensorless DTC-SVPWM operation of drive system. The novel observer is obtained by using the same transformation as in a linear Kalman observer, which is proposed by C.-S. Hsieh and F.-C. Chen in 1999. The OTSEKF is an effective implementation of the extended Kalman filter (EKF and provides a recursive optimum state estimation for PMSMs using terminal signals that may be polluted by noise. Compared to a conventional EKF, the OTSEKF reduces the number of arithmetic operations. Simulation and experimental results verify the effectiveness of the proposed OTSEKF observer for DTC of PMSMs.
Sayyed Asghar Gholamian
2013-07-01
Full Text Available Multiphase machines have gained an increasing attention due to their more advantages in comparison with three-phase machines. In recent literatures, only voltage source inverters (VSIs have been used to supply five-phase drives. Matrix converters (MCs pose many advantages over conventional VSIs, such as lack of dc-bulk capacitors, high quality power output waveform and higher number of output voltages. Due to some special applications of multiphase machines such as ship propulsion and aerospace, the volume of these drives is an important challenging problem. As a consequence, using MCs can be a reasonable alternative. In this paper, a new direct torque control (DTC algorithm using a three-to-five phase MC is proposed for five-phase permanent magnet synchronous motors (PMSMs. All of output voltage space vectors of three-to-five phase MC are extracted and a new switching table is proposed. Because of higher number of output voltages in MCs, there is a degree of freedom to control input power factor to keep close to unit moreover the torque and flux control. In other words, this proposed method use the advantages of both DTC method and MCs. Simulation results show the effectiveness of presented method in different operation modes.
余世鹏; 杨劲松; 刘广明; 姚荣江; 王相平
2014-01-01
为探讨前馈型人工神经网络BP-ANN（back propagation artificial neural network）和模糊神经NF （neuro-fuzzy）2种神经网络算法在区域地下水盐分动态预测中的应用过程与效果，首先通过经典统计分析确定区域地下水盐分动态的主要驱动因子以及可用的模型输入因子组合，采用“试错法”确定神经网络模型的最优结构，进而开展地下水盐分中长期动态的有效模拟预测。结果表明，在长江河口寅阳和大兴地区以降水动态为单输入的NF（5-gbellmf-160）和以降水与内河水盐分动态为双输入的NF（4-gaussmf-100）为最优预测模型。研究表明神经网络模型对地下水盐分动态的预测精度优于常规线性模型，其中，NF、BP-ANN、线性模型在寅阳测点的预测相关系数分别为0.565、0.445、0.261，在大兴测点的预测相关系数分别为0.886、0.784、0.543。与BP-ANN、线性模型相比，基于模糊神经算法的 NF 模型具有更好的误差纠错和仿真能力，在寅阳和大兴测点的预测误差分别降低了30%以上和50%以上。相关研究结果在区域水盐动态科学预警研究领域有较好地应用前景。%The study conducted a detailed analysis of the modeling processes and performances of 2 types of different neural network models including back propagation artificial neural network (BP-ANN) and neuro-fuzzy (NF), in the groundwater salinity dynamics forecasting. Firstly, the classical statistical analysis was used to determine the dominant driving factors of groundwater salinity dynamics and to reveal the available model inputs combinations. Then, the optimal neural network model structures were determined by the trial-and-error method and used to effectively forecast the mid-long term groundwater salinity dynamics. By our research, the idea of necessity in selecting the optimal NF model parameters of transfer functions, rule numbers and iteration steps was innovatively
Kim, Chan Moon; Parnichkun, Manukid
2017-02-01
Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system (k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.
Video Smoke Detection Based on Adaptive Neuro-fuzzy Inference System%基于自适应神经模糊推理系统的视频烟雾检测
王涛; 刘渊; 谢振平
2011-01-01
This paper presents a video smoke detection algorithm based on Adaptive Neuro-fuzzy Inference System(ANFIS). The smoke features are extracted from video sequences, and the subtractive clustering is introduced to confirm the fuzzy rule number. The premise parameters and the consequent parameters are updated by hybrid learning rule. The fuzzy inference rules are obtained. Experimental results show that compared with the traditional BP neural network algorithm and Support Vector Machine(SVM) algorithm, the new algorithm has better performance on Receiver Operating Characteristic(ROC) curve.%提出一种基于自适应神经模糊推理系统的视频烟雾检测算法.从视频图像中提取烟雾特征,采用减法聚类确定模糊规则数,建立初始模糊系统.通过神经网络的自学习机制调整前提参数和结论参数,确定模糊推理规则.实验结果表明,与传统BP神经网络算法及支持向量机算法相比,该算法具有较优的ROC曲线特性.
Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin
2017-10-01
Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.
Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin
2017-04-01
Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.
Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed
2016-06-01
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.
F. Naceri
2010-01-01
Full Text Available This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control methodis used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation(DTC-SVM. The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates theperformance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque.As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require goodtorque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimationmethod is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate theproposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.
Chikhi Abdesselam
2010-01-01
Full Text Available This paper presents a comparative study of field-oriented control (IFOC and direct-torque control (DTC of induction motors using an adaptive flux observer. The main characteristics of field-oriented control and direct torque control schemes are studied by simulation, emphasizing their advantages and disadvantages. The performances of the two control schemes are evaluated in terms of torque, current ripples and transient responses to load toque variations. We can nevertheless observe a slight advance of DTC scheme compared to FOC scheme regarding the dynamic flux control performance and the implementation complexity. Consequently, the choice of one or the other scheme will depend mainly on specific requirements of the application.
Björklund, Martin; Svedmark, Åsa; Srinivasan, Divya; Djupsjöbacka, Mats
2017-01-01
Background Cervical range of motion (ROM) is commonly assessed in clinical practice and research. In a previous study we decomposed active cervical sagittal ROM into contributions from lower and upper levels of the cervical spine and found level- and direction-specific impairments in women with chronic non-specific neck pain. The present study aimed to validate these results and investigate if the specific impairments can be explained by the neutral posture (defining zero flexion/extension) or a movement strategy to avoid large gravitationally induced torques on the cervical spine. Methods Kinematics of the head and thorax was assessed in sitting during maximal sagittal cervical flexion/extension (high torque condition) and maximal protraction (low torque condition) in 120 women with chronic non-specific neck pain and 40 controls. We derived the lower and upper cervical angles, and the head centre of mass (HCM), from a 3-segment kinematic model. Neutral head posture was assessed using a standardized procedure. Findings Previous findings of level- and direction-specific impairments in neck pain were confirmed. Neutral head posture was equal between groups and did not explain the direction-specific impairments. The relative magnitude of group difference in HCM migration did not differ between high and low torques conditions, lending no support for our hypothesis that impairments in sagittal ROM are due to torque avoidance behaviour. Interpretation The direction- and level-specific impairments in cervical sagittal ROM can be generalised to the population of women with non-specific neck pain. Further research is necessary to clarify if torque avoidance behaviour can explain the impairments. PMID:28099504
Zhu, X.; Ostilla-Monico, Rodolfo; Verzicco, R.; Lohse, D.
2016-01-01
We present direct numerical simulations of Taylor–Couette flow with grooved walls at a fixed radius ratio ${\\it\\eta}=r_{i}/r_{o}=0.714$η=ri/ro=0.714 with inner cylinder Reynolds number up to $Re_{i}=3.76\\times 10^{4}$Rei=3.76×104, corresponding to Taylor number up to $Ta=2.15\\times 10^{9}$Ta=2.15×10
Farina, Francesco; Bojoi, Radu; Tenconi, Alberto; Profumo, Francesco
A Direct Torque Control (DTC) strategy for dual-three phase induction motor drives is discussed in this paper. The induction machine has two sets of stator three-phase windings spatially shifted by 30 electrical degrees with isolated neutral points. The proposed control strategy is based on Proportional Integral (PI) regulators implemented in the stator flux synchronous reference frame. To improve the flux estimation, an Adaptive Stator Flux Observer (ASFO) has been used. Doing so, besides a better flux estimation in contrast to open-loop flux estimators, it is possible to use the observed currents to compensate the inverter non-linear behavior (such as dead-time effects), improving the drive performance at low speed. This is particularly important for low voltage/high current applications, as the drive considered in this paper. The advantages of the discussed control strategy are: constant inverter switching frequency, good transient and steady-state performance and less distorted machine currents in contrast to DTC schemes with variable switching frequency. Experimental results are presented for a 10kW dual three-phase induction motor drive prototype.
Direct Torque Control and Simulation of Permanent Magnet Synchronous Motor%永磁同步电机直接转矩控制及仿真
冯涛; 陆华
2013-01-01
A simple and effective direct torque control method based on permanent magnet synchronous motor was described.The direct torque control system was built by using simulink,and the performance of permanent magnet synchronous motor under different speeds and torque was analyzed.The feasibility of the method was verified through simulation analysis.This control method could satisfy the rapidity requirement,that was,it had good speed control characteristic.The results showed that stator flux and motor torque could be controlled effectively if a reasonable voltage space vector is selected.%介绍了一种基于永磁同步电机(PMSM)的简单有效的直接转矩控制(DTC)技术.利用Simulink搭建DTC系统,仿真分析了PMSM在不同转速和转矩条件下的性能,并验证算法的可行性.该控制技术能够满足系统控制快速性的要求,具有良好的速度控制特性.结果分析表明,定子磁链和电机转矩可通过选择合理的电压空间矢量得到有效控制.
Abootorabi Zarchi, H., E-mail: abootorabi9@yahoo.co [Faculty of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Arab Markadeh, Gh.R., E-mail: arab-gh@eng.sku.ac.i [Department of Engineering, Shahrekord University, Shahrekord (Iran, Islamic Republic of); Soltani, J., E-mail: j1234sm@cc.iut.ac.i [Faculty of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of)
2010-01-15
In this paper, a nonlinear speed tracking controller is introduced for three-phase synchronous reluctance motor (SynRM) on the basis of input-output feedback linearization (IOFL), considering the different control strategies (maximum torque per Ampere, high efficiency and minimum KVA rating for the inverter) related to this motor. The proposed control approach is capable of decoupling control of stator flux and motor generated torque. The validity and effectiveness of the method is verified by simulation and experimental results.
孙振川
2011-01-01
In order to solve the problems of the torque ripple and variable switching frequency in traditional direct torque control ( DTC) of the asynchronous machines, a new method based on duty ratio control was proposed in this paper. In duty ratio control technique, a zero voltage vector was applied in each sampling period to reduce the torque ripple. A new method of calculating the slopes of the rose and decline of the torque was proposed in this paper, the duty ratio can be calculated. Finally, the proposed strategy was verified by simulations. Simulation results show that a great reduction of torque ripple was achieved. The switch frequency of the inverter can be made constant by setting the fixed sampling period. Therefore, a good statistic and dynamic performance are obtained.%为了解决直接转矩控制中转矩脉动过大、开关频率不固定等问题,文中提出了一种新的基于占空比控制的异步电动机直接转矩控制方案.占空比控制技术通过在每个采样周期中插入零电压矢量来减小转矩的波动.本文提出了一种新的计算转矩上升和下降斜率的方法,求解出了占空比的计算公式.最后,对所提出的直接转矩控制算法进行了仿真.仿真结果表明,该方案可以大大减小输出转矩的波动.通过设定固定的采样周期,可以使逆变器的开关频率保持恒定,从而大大改善了调速系统的静、动态性能.
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-02-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-11-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
Direct Torque and Fault Tolerant Control for Six-phase Induction Motor%六相感应电机直接转矩及容错控制
耿乙文; 鲍宇; 王昊; 马鸿宇
2016-01-01
The problem of large torque ripples exists in conventional direct torque control (DTC). Based on the consideration of the six-phase induction motor with its own characteristics and analysis of the mathematical model, the paper proposed a six-phase induction motor direct torque control method. Firstly, the mathematical model of motors with normal and stator winding open-circuited conditions was built and analyzed respectively. Furthermore, formulation of a space vector pulse width modulation (SVPWM) provides a method to reduce stator current harmonics of the motor. Analysis of torque and flux provides a theoretical basis for the direct torque control. Finally, using the fuzzy reasoning and changing the duty ratio of the vector action time method, the inverter can output any angle and amplitude of the voltage vector. Experimental results show that the proposed strategy is able to reduce torque ripples and can improve current waveforms, and the torque dynamic response of the system is fast.%针对传统直接转矩控制转矩脉动大的问题，在考虑六相感应电机自身特点及分析其缺相数学模型的基础上，提出了一种六相感应电机直接转矩及容错控制方法。首先，建立和分析电机正常及缺相时的数学模型，并确定空间矢量脉宽调制(space vector pulse width modulation，SVPWM)方案以减小电机定子电流谐波含量；然后，通过对电机转矩和磁链进行分析，为直接转矩控制提供理论依据；在此基础上，利用模糊推理和改变矢量作用时间占空比的方法，使得逆变器可以输出任意角度和幅值的电压矢量。实验结果表明，该控制方案下电机转矩响应迅速，并能很好地改善电机转矩脉动和定子电流波形。
Direct Torque Control for Induction Motor Based on Space Vector Modulation%异步电动机的SVM-DTC控制
蒋建虎; 姬宣德
2011-01-01
针对基本直接转矩控制(BASIC-DTC)转矩和磁链脉动大的缺点,采用空间电压矢量调制与直接转矩控制相结合(SVM-DTC)的方法,以减小转矩和磁链脉动；为了提高定子磁链的观测精度,利用全阶磁链观测器观测定子磁链,并提出一种单自由度极点配置方法实现磁链观测器的极点配置.仿真结果表明,系统不但实现了转矩和磁链的定量控制,降低了转矩脉动和磁链脉动,提高了定子磁链的观测精度,而且同时使得逆变器开关周期恒定,更易于数字化实现.%For the disadvantage of the basic direct torque control (BASIC-DTC) 's torque and flux ripple when it runs, the direct torque control combined with space vector modulation (SVM-DTC) was used to reduce the torque and flux ripple. To improve the accuracy of the stator flux observation,the full order flux observer was used to achieve the stator flux observation. The new method of pole placement was proposed to achieve the configuration of the flux full order observer gain matrix parameters. The simulation results show that the torque ripple and flux ripple is reduced and the accuracy of the stator flux observation is improved,and in this method the invertor has constant switching period and is easy to realizing digital.
INTELLIGENT DTC FOR PMSM DRIVE USING ANFIS TECHNIQUE
AHMED A. MAHFOUZ
2012-03-01
Full Text Available This paper describes intelligent direct torque control (DTC technique for Permanent Magnet Synchronous Motor (PMSM drive based on Adaptive Neuro Fuzzy Inference Systems (ANFIS. The proposed system has proven successful in controlling the instantaneous torque so as not to depend only on the estimation flux, torque and position, but also the estimation of the lookup table and the generation of driver switching table. Experimental results prove the MATLAB simulation results for torque, speed and flux estimations.
Badrinarayan Bansilal Pimple
2015-02-01
Full Text Available This study proposes a polar voltage control-based direct torque control method to reduce the effects of unbalanced grid voltage on doubly-fed induction generator (DFIG-based wind turbine system. Under unbalanced grid voltage, the stator flux has a negative sequence component which leads to second harmonic pulsation in torque, stator active power, stator reactive power, stator current and rotor current. In the control scheme, the negative sequence rotor voltage vector is controlled to compensate the negative sequence stator flux by negative sequence rotor flux. Simulation study is carried out on a 2 MW DFIG system using MATLAB/SIMULINK. Feasibility of the proposed control strategy is experimentally verified on a 1.5 kW DFIG system.
无刷直流电机无位置传感器直接转矩控制%Direct Torque Control of Sensorless Brushless DC Motor
雷雄
2012-01-01
A direct torque control for sensorless brushless DC motor is presented in this paper. The control method presented requires observation of the DC bus voltage and the three-phase stator currents of brushless DC motor, and estimate the flux, torque, and rotor position angles of motor In order to control brushless DC motor, the appropriate voltage space vector is selected according to the torque, current component on d-q axes and rotor position. The simulation and experimental results show that the control method is valid and feasible.%本文提出了一种将直接转矩控制应用于无刷直流电机的无位置传感器控制方法。需观测无刷直流电机的直流母线电压及定子侧的三相电流，从而估算出电机的磁链、转矩以及位置角，再根据转矩、d-q坐标系上的电流分量以及转子位置信息来选择相应的电压空间矢量，以实现对无刷直流电机的控制。仿真和实验结果表明本文提出的控制方法有效可行。
无轴承无刷直流电机的直接转矩控制%Direct torque control of bearingless brushless DC motor
许洁; 刘贤兴; 李兵伟
2012-01-01
无轴承无刷直流电机结合了无轴承电机和无刷直流电机的优点、具有良好的行特性和广泛的运用前景.研究了无轴承无刷直流电机的控制方法,并将直接转矩控制技术应用于无轴承无刷直流电机的转矩绕组.通过Matlab/Simulink仿真和实验结果表明,转矩绕组的直接转矩控制(DTC)能提高系统的响应速度,减少转矩脉动,提高系统的静动态性能且控制方法简单有效.%Bearingless brushless DC motor, combining advantages of brushless DC motor and bearingless motor, has favorable operating characteristics and wide range of applications. In this paper, the control method of bear ingless brushless DC motor is researched, and the bearingless brushless DC motor's torque winding is controlled by using the method of direct torque control ( DTC ). By Matlab/Simulink simulation and experiment of the con trol system, the results show that the application of DTC to torque winding control of bearingless brushless DC motor is correct and feasible. DTC improves the system's response speed, reduces torque ripple, and optimizes the control effect. The control method is relatively simple.
Kopella Sai Teja
2015-02-01
Full Text Available Wind farm is connected to the grid directly.The wind is not constant voltage fluctuations occur at point of common coupling (PCC and WF terminal . To over come this problem a new compensation strategy is used . By using Custom power devices (UPQC.It injects reactive power at PCC . The advantages of UPQC is it consists of both DVR and D-STATCOM . DVR is connected in series to the line and it injects in phase voltage into the line .D-STATCOM is connected shunt to the line .The internal control strategy is based on management of active and reactive power in series and shunt converters of UPQC . The power exchainge is done by using DC-link
Students Classification With Adaptive Neuro Fuzzy
Mohammad Saber Iraji
2012-07-01
Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students
Yang, Ruiping; Li, Renxian; Qin, Shitong; Ding, Chunying; Mitri, F. G.
2017-02-01
The effect of polarization on the optical spin torque (OST) exerted on an absorptive Rayleigh dielectric sphere by a vector Bessel beam is investigated using the dipole approximation method, with particular emphasis on the polarization of the plane wave component forming the beam. On the basis of the mathematical descriptions for the electric fields, which are derived using the angular spectrum decomposition method in plane waves, analytical formulas of the OST are established. The OSTs are numerically calculated, and the effects of polarization, beam-order, and half-cone angle are discussed in detail. Numerical results show that by choosing an appropriate polarization, order and half-cone angle, the transverse OST will manifest vortex-like behaviors, and the sphere will experience negative axial OSTs, i.e. OST sign reversal. Important applications in particle manipulation, rotation and handling using optical Bessel polarized beams would benefit from the results of the present investigation.
Limited Angle Torque Motors Having High Torque Density, Used in Accurate Drive Systems
R. Obreja
2011-01-01
Full Text Available A torque motor is a special electric motor that is able to develop the highest possible torque in a certain volume. A torque motor usually has a pancake configuration, and is directly jointed to a drive system (without a gear box. A limited angle torque motor is a torque motor that has no rotary electromagnetic field — in certain papers it is referred to as a linear electromagnet. The main intention of the authors for this paper is to present a means for analyzing and designing a limited angle torque motor only through the finite element method. Users nowadays require very high-performance limited angle torque motors with high density torque. It is therefore necessary to develop the highest possible torque in a relatively small volume. A way to design such motors is by using numerical methods based on the finite element method.
Ramesh, Tejavathu; Panda, A. K.; Kumar, S. Shiva
2013-08-01
In this research study, the performance of direct torque and flux control induction motor drive (IMD) is presented using five different speed control techniques. The performance of IMD mainly depends on the design of speed controller. The PI speed controller requires precise mathematical model, continuous and appropriate gain values. Therefore, adaptive control based speed controller is desirable to achieve high-performance drive. The sliding-mode speed controller (SMSC) is developed to achieve continuous control of motor speed and torque. Furthermore, the type-1 fuzzy logic speed controller (T1FLSC), type-1 fuzzy SMSC and a new type-2 fuzzy logic speed controller are designed to obtain high performance, dynamic tracking behaviour, speed accuracy and also robustness to parameter variations. The performance of each control technique has been tested for its robustness to parameter uncertainties and load disturbances. The detailed comparison of different control schemes are carried out in a MATALB/Simulink environment at different speed operating conditions, such as, forward and reversal motoring under no-load, load and sudden change in speed.
Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S
2015-07-01
In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation.
Fuzzy Direct Torque Control System Simulation by Matlab/simulink%模糊直接转矩控制系统MATLAB/SIMULINK仿真
窦曰轩; 王洪艳
2001-01-01
文章采用MATLAB5.2/SIMULINK建立模糊直接转矩控制系统的仿真模型，介绍了用SIMULINK软件进行封装、S函数设计及用模糊工具箱设计模糊控制器的方法，并通过仿真结果验证了此模型的正确性。%This paper uses MATLAB5.2/SIMULINK to build fuzzy direct torque control system's simulating model.SIMULINK software is used for masking and designing S-function,we also design fuzzy controller by using Fuzzy Toolbox.Simulating result is presented to demonstrate this model is true.
基于GA+BP网络速度辨识的直接转矩控制%Speed Observe of Direct Torque Control Based on GA and BP Network
蔡斌军
2012-01-01
There are big ripples in motor drive on current and flux linkage and. torque when using traditional direct torque control (DTC) . System with speed sensor has lower reliability and higher system cost. To solve these problems, a strategy has been put forward that using Genetic algorithm ( GA) optimize BP neural network motor speed identifier, thus speed sensor-less direct torque control for induction motor is realized. Rapid torque response and strong robustness of direct torque control method are still maintained. Ripples on current and flux linkage and torque axe dramatically reduced. In the mean time, the system based on BP and GA is robust to motor load disturbance. The system dynamic and static performance are dramatically improved. The experimental results show the feasibility and effectiveness of this method.%针对传统直接转矩控制中存在电流、磁链和转矩脉动较大及速度传感器的使用降低了系统的可靠性,增加了系统的成本等问题,提出了利用遗传算法(GA)优化的BP网络电机速度辨识方法,实现了异步电机无速度传感器直接转矩控制.该方法保持了直接转矩控制固有的转矩响应快和系统鲁棒性强的优点,降低了磁链、转矩脉动,加快了系统的响应速度,并对负载的扰动具有较强的鲁棒性,有效地改善了系统的动、静态性能,实验结果证实了该方法的可行性和有效性.
M. M. Krishan
2010-01-01
Full Text Available Problem statement: Neural networks and fuzzy inference systems are becoming well-recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called neuro-fuzzy architectures have been developed. The mo Approach: Motivation behind the use of neuro-fuzzy approaches was based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation, ensure more robustness of the overall system and to reduce the chattering phenomenon introduced by sliding mode control which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. Results: In fact, the aim of such a research consists first in simplifying the control of the motor by decoupling between two principles variables which provoque the torque in the motor by using the feedback linearization method. Then, using sliding mode controllers to give our process more robustness towards the variation of different parameters of the motor. However, the latter technique of control called sliding mode control caused an indesirable phenomenon which harmful and could leads to the deterioration of the inverters components called chattering. So, here the authors propose to use neuro-fuzzy systems to reduce this phenomenon and perform the performances of the adopted control process. The type of the neuro-fuzzy system used here is called: Adaptive Neuro Fuzzy Inference System (ANFIS. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Conclusion: Therefore, from a control design consideration, the adopted neuro-fuzzy system has opened up a new
某种永磁同步发电机直接转矩控制系统设计%A Permanent Magnet Synchronous Generator Direct Torque Control System Design
张进超; 王玲; 郝永平; 张令涛
2015-01-01
Based on two dimensional trajecto-ry correction principle, a revised steering gear method of direct torque control of permanent mag-net synchronous generator is proposed.For a per-manent magnet synchronous motor model of d q coordinates,a permanent magnet synchronous mo-tor direct torque control scheme is studied.The re-sults showed that corrected steering gear direct torque control system of the generator output torque can achieve high speed rotating projectile correction.%依据二维弹道修正原理，提出修正舵机中永磁同步发电机直接转矩控制方法，针对 d q 坐标系下的永磁同步电机模型，研究了永磁同步电机直接转矩控制方案。结果表明，修正舵机中发电机直接转矩控制系统输出转矩能够达到高速旋转弹的修正效果。
Artificial Intelligence Techniques for the Estimation of Direct Methanol Fuel Cell Performance
Hasiloglu, Abdulsamet; Aras, Ömür; Bayramoglu, Mahmut
2016-04-01
Artificial neural networks and neuro-fuzzy inference systems are well known artificial intelligence techniques used for black-box modelling of complex systems. In this study, Feed-forward artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used for modelling the performance of direct methanol fuel cell (DMFC). Current density (I), fuel cell temperature (T), methanol concentration (C), liquid flow-rate (q) and air flow-rate (Q) are selected as input variables to predict the cell voltage. Polarization curves are obtained for 35 different operating conditions according to a statistically designed experimental plan. In modelling study, various subsets of input variables and various types of membership function are considered. A feed -forward architecture with one hidden layer is used in ANN modelling. The optimum performance is obtained with the input set (I, T, C, q) using twelve hidden neurons and sigmoidal activation function. On the other hand, first order Sugeno inference system is applied in ANFIS modelling and the optimum performance is obtained with the input set (I, T, C, q) using sixteen fuzzy rules and triangular membership function. The test results show that ANN model estimates the polarization curve of DMFC more accurately than ANFIS model.
Forces and torques between nonintersecting straight currents
Binder, P.-M.; Cross, Felicity; Silva, J. K.
2016-07-01
We analyse two very long current-carrying straight wires that point in arbitrary directions without touching. We find general expressions for the forces and torques for arbitrary points on one wire due to the other. This allows us to make calculations for the overall forces and torques and statements about the stability of parallel and anti-parallel current arrangements.
史涔溦; 邱建琪; 金孟加; Friedrich W.Fuchs
2005-01-01
提出一种用于永磁同步电动机的基于磁链误差矢量补偿的直接转矩控制(EFVC-DTC)策略,给出了磁链误差矢量的估计算法,并将该控制策略下的稳态和动态运行性能与常规DTC进行比较.仿真及实验结果表明EFVC-DTC可以使电力开关器件工作在基本固定的频率上,磁链和转矩脉动显著减小,比常规DTC具有更优越的稳态性能,而动态转矩响应几乎与常规DTC相同.%A modified direct torque control strategy based on error flux linkage vector compensation (EFVC-DTC) for permanent magnet synchronous machines (PMSM) is presented. The theoretical background of EFVC-DTC is introduced and an algorithm to estimate the flux linkage error is proposed. The steady state and dynamic performances of EFVC-DTC have been compared with those of the conventional direct torque control (DTC). The simulation and experimental results confirm that both flux linkage and torque ripples are significantly reduced in EFVC-DTC with a fixed switching frequency while the dynamic torque response isalmost as good as the basic DTC.
Peresada, Sergei; Kovbasa, Serhii; Dymko, Serhii; BOZHKO, Serhiy
2016-01-01
The paper presents a novel maximum torque per Ampere (MTA) controller for induction motor (IM) drives. The proposed controller exploits the concept of direct (observer based) field orientation and guarantees asymptotic torque tracking of smooth reference trajectories and maximizes the torque per Ampere ratio when the developed torque is constant or slowly varying. A dynamic output-feedback linearizing technique is employed for the torque subsystem design. In order to improve torque tracking a...
Spin Orbit Torque in Ferromagnetic Semiconductors
Li, Hang
2016-06-21
effect on spin orbit torque in nanoribbons with a hexagonal lattice. We find a dramatic modification of the nature of the torque (field like and damping-like component) when crossing the topological phase transition. The relative agnitude of the two torque components can be significantly modifies by changing the magnetization direction. Finally, motivated by recent experimental results, we conclude by investigating the features of spin-orbit torque in magnetic transition metal dichalcogenides. We find the torque is associated with the valley polarization. By changing the magnetization direction, the torque can be changed from a finite value to zero when the valley polarization decreases from a finite value to zero.
吴伟乾; 凌有铸; 陈孟元
2016-01-01
为抑制传统直接转矩控制系统中固有的磁链和转矩脉动，分析了零矢量在永磁同步电机(PMSM)直接转矩控制中对磁链和转矩的影响。基于电压空间矢量调制技术当中电压矢量合成的控制思想，采取有效电压矢量和零矢量共同作用的控制方式构成基于占空比调制的 PMSM直接转矩控制系统(DTC－DRM)，并改进了占空比的计算方式，以转矩偏差、定子磁链偏差以及电机的转速信息来计算占空比，所需参数少，简化了传统占空比控制技术复杂的占空比计算。与传统DTC的仿真结果对比，验证了系统方案的可行性和有效性，且能保持系统结构简单和较强的鲁棒性。%To reduce the undesired torque and flux ripple in conventional direct torque control(DTC),this paper presents a novel direct torque control strategy based on duty ratio modulation (DTC-DRM) through the voltage synthesis theory in the space voltage vector modulation.The effect of zero vector on direct torque control is analyzed,and the combination of active vector and zero vector is also introduced. To distinguish from the conventional methods,the duty ratio is determined according to the torque error and flux error,and the speed of PMSM,which can simplify the calculation,with less parameters of PMSM needed.The results of simulation are compared between proposed strategy and conventional DTC,indicating that an obvious reduction of the flux and torque ripple is achieved,and the simplicity and robustness of DTC are maintained.
螺旋桨负载永磁同步电机直接转矩控制系统研究%Research on the direct torque control system of PMSM with propeller load
任俊杰; 刘彦呈; 赵友涛; 郭昊昊
2012-01-01
To research the performance of the variable frequency speed regulation system used the space vector modu-lation strategy for permanent magnet synchronous motor ( PMSM) with the propeller load, through combining the analysis of direct torque control ( DTC ) and propeller load characteristic theory with the open water curve of the propeller, it obtained the function relationship for thrust coefficient Kp and torque coefficient Km with the advance speed ratio /, respectively. It proposed the calculation method of thrust and torque coefficient and established the PMSM DTC simulation model with propeller load. The simulation results show that the response time of the motor dynamic speed is fast in DTC system, and the electromagnetic torque is equal to the load torque produced by rotating propeller when the motor speed in steady states. Compared with the measured motor's operation data, the simulation motor torque value is consistent with the measured data in different rotating speeds and verify the validity of the model.%为了研究空间矢量调制策略对大功率永磁同步电机在螺旋桨负载特性下变频调速性能的影响,通过对直接转矩控制变频调速和螺旋桨负载特性理论的分析,结合螺旋桨敞水特性曲线,得到螺旋桨推力系数Kp和扭矩系数Km分别与进速比J之间的函数关系.提出了推力系数和扭矩系数的计算方法,建立了螺旋桨负载和永磁推进电机直接转矩控制的仿真模型.仿真结果表明,直接转矩控制系统中永磁推进电机转速动态响应快,转速稳定后电磁转矩与螺旋桨旋转产生的负载转矩相等.与实测电机运行数据比较可知,不同转速下仿真得到的电机转矩值与实测值相一致,验证了该模型的有效性.
杨晓武; 李干蓉; 李劲松
2012-01-01
The advantage and application area of Brushless Direct Current Motor（BLDCM） was described, the relative mathematic model was built and the effect of electromagnetic torque ripple on the performance of BLDCM was elaborat- ed. The paper discussed factors that affected the torque ripple from the case of the ideal back-EMF and non-ideal back-EMF for BLDCM, then the relevant control strategy are put forward to minimize the torque ripple. The strategy com- bines the advanced commutation with the current control to minimize the torque ripple caused by non-ideal back-EMF. Finally, the paper built simulation model for BLDCM by using software MATLAB according to the proposed control strat- egy targeted at torque ripple, the experimental simulation results show that the proposed control strategy for reducing torque ripple is correct and reasonable.%在介绍无刷直流电机的优点及其应用领域的基础上，建立了电机的基本数学模型，并且阐述了其电磁转矩波动对电机运行中性能的影响，重点从理想反电动势无刷直流电机和非理想反电动势无刷直流电机两种情况下分析电磁转矩波动的因素，并采用相对的控制策略来控制电磁转矩波动；提前角开通换相法与电流控制相结合的控制策略，最后，利用MATLAB仿真软件组建了无刷直流电机的仿真模型，仿真实验结果验证了所提出的转矩波动控制策略的合理性和正确性。
高中臣; 张爱玲; 陈晨
2009-01-01
To reduce the low speed torque ripple of induction motor and keep constant switch frequency, this paper proposed a new control strategy on the basis of direct torque control (DTC),which uses Space Vector Pulse Width Modulation instead of switch table.The experiment and simulation results show that it can reduce the torque ripple,and improve the waveform of stator flux and current.%针对传统直接转矩控制开关频率不恒定及低速时的转矩脉动大的缺点,采用了基于SVPWM的直接转矩控制方法.根据转矩和定子磁链的误差确定应该施加的参考电压矢量,然后利用电压空间矢量脉宽调制(SVPWM)的方法合成该矢量.仿真和实验结果表明,基于SVPWM的直接转矩控制(SVPWM-DTC)能够有效减小转矩脉动,改善磁链和电流波形.
Esquivel-Sirvent, Raul; Schatz, George
2014-03-01
The theory of generalized van der Waals forces by Lifshtz when applied to optically anisotropic media predicts the existence of a torque. In this work we present a theoretical calculation of the van der Waals torque for two systems. First we consider two isotropic parallel plates where the anisotropy is induced using an external magnetic field. The anisotropy will in turn induce a torque. As a case study we consider III-IV semiconductors such as InSb that can support magneto plasmons. The calculations of the torque are done in the Voigt configuration, that occurs when the magnetic field is parallel to the surface of the slabs. The change in the dielectric function as the magnetic field increases has the effect of decreasing the van der Waals force and increasing the torque. Thus, the external magnetic field is used to tune both the force and torque. The second example we present is the use of the torque in the non retarded regime to align arrays of nano particle slabs. The torque is calculated within Barash and Ginzburg formalism in the nonretarded limit, and is quantified by the introduction of a Hamaker torque constant. Calculations are conducted between anisotropic slabs of materials including BaTiO3 and arrays of Ag nano particles. Depending on the shape and arrangement of the Ag nano particles the effective dielectric function of the array can be tuned as to make it more or less anisotropic. We show how this torque can be used in self assembly of arrays of nano particles. ref. R. Esquivel-Sirvent, G. C. Schatz, Phys. Chem C, 117, 5492 (2013). partial support from DGAPA-UNAM.
Decoupled Speed and Torque Control of IPMSM Drives Using a Novel Load Torque Estimator
ZAKY, M.
2017-08-01
Full Text Available This paper proposes decoupled speed and torque control of interior permanent magnet synchronous motor (IPMSM drives using a novel load torque estimator (LTE. The proposed LTE is applied for computing a load torque and yielding a feed-forward value in the speed controller to separate the torque control from the speed control. Indirect flux weakening using direct current component is obtained for high speed operation of the IPMSM drive, and its value for maximum torque per ampere (MTPA control in constant torque region is also used. LTE uses values of direct and quadrature currents to improve the behavior of the speed controller under the reference tracking and torque disturbances. The complete IPMSM drive by Matlab/Simulink is built. The effectiveness of the proposed control scheme using an experimental setup of the complete drive system implemented on a DSP-DS1102 control board is confirmed. Extensive results over a wide speed range are verified. The efficacy of the proposed method is confirmed in comparison to a conventional PI controller under both the reference speed tracking and load torque disturbance.
薛诚; 宋文胜; 冯晓云
2016-01-01
The multi-phase permanent magnet synchronous machine (PMSM) has the property of small size, low noise and high power density, so it has been widely used in high-power and low-voltage occasion. Based on the analysis of traditional direct torque control (DTC) models and space vector pulse width modulation (SVPWM) technologies, a fixed switching frequency modified DTC algorithm of five-phase PMSM with multi-objective optimization was proposed, which aims at improving the trajectory of stator flux effectively, reducing torque ripples and low harmonic components of stator currents. The proposed algorithm brings the following benefits: such as fast torque control dynamic response, the fixed switching frequency, the optimization control of torque, stator flux and stator phase currents. Simulation and experimental results show the feasibility and effectiveness of the proposed algorithm.%多相永磁同步电机驱动系统因具有体积小、噪声低、功率密度高等诸多优点，在低压、大功率输出及可靠性要求高的场合已得到了广泛关注和应用。该文以五相永磁同步电机为研究对象，分析并建立传统直接转矩控制(direct torque control，DTC)算法数学模型，在此基础上，以进一步优化转矩纹波及磁链轨迹，降低定子电流低次谐波为控制目标，结合空间矢量脉宽调制(space vector pulse width modulation， SVPWM)技术，提出改进DTC的定频多目标优化控制模型。该算法不仅保持了传统DTC快速的转矩控制动态响应，实现了开关频率恒定及转矩的无稳态误差，同时也兼顾了定子磁链以及相电流的优化控制。仿真和实验结果均表明该算法的正确性和有效性。
Modelling of a Magnetostrictive Torque Sensor
Tsiantos Vasilios
2016-01-01
Full Text Available Existing magnetostrictive torque sensor designs typically measure the rotation of the saturation magnetization under an applied torque and their theoretical treatment revolves around the minimization of the free energy equation adapted according to the assumptions considered valid in each design. In the torque measurement design discussed in this paper, Ni-rich NiFe films have been electrodeposited on cylindrical austenitic steel rods. Contrary to existing designs, the excitation field is applied along the axial direction and is low enough to ensure that the resulting magnetization along the same direction remains in the linear region of the M(H characteristic. Assuming homogeneous magnetization, positive magnetostriction constant λ, negligible hysteresis and demagnetizing fields, torque T may be expressed in terms of an effective uniaxial anisotropy constant Ku around 45° to the axial direction. It is shown, that for the proposed arrangement, the resulting M is the linear superposition of the effect of a torque-induced effective field and the excitation field, the applied field accounts for the vertical offset of the magnetization response and the applied torque increases the slope of the M(H characteristic.
Surface Roughness Effects on Vortex Torque of Air Supported Gyroscope
LIANG Yingchun; LIU Jingshi; SUN Yazhou; LU Lihua
2011-01-01
In order to improve the drift precision of air supported gyroscope, effects of surface roughness magnitude and direction on vortex torque of air supported gyroscope are studied. Based on Christensen's rough surface stochastic model and consistency transformation method, Reynolds equation of air supported gyroscope containing surface roughness information is established.Also effects of mathematical models of main machining errors on vortex torque are established. By using finite element method,the Reynolds equation is solved numerically and the vortex torque in the presence of machining errors and surface roughness is calculated. The results show that surface roughness of slit has a significant effect on vortex torque. Transverse surface roughness makes vortex torque greater, while longitudinal surface roughness makes vortex torque smaller. The maximal difference approaches 11.4％ during the range analyzed in this article. However surface roughness of journal influences vortex torque insignificantly. The research is of great significance for designing and manufacturing air supported gyroscope and predicting its performance.
Dynamic Torque Calibration Unit
Agronin, Michael L.; Marchetto, Carl A.
1989-01-01
Proposed dynamic torque calibration unit (DTCU) measures torque in rotary actuator components such as motors, bearings, gear trains, and flex couplings. Unique because designed specifically for testing components under low rates. Measures torque in device under test during controlled steady rotation or oscillation. Rotor oriented vertically, supported by upper angular-contact bearing and lower radial-contact bearing that floats axially to prevent thermal expansion from loading bearings. High-load capacity air bearing available to replace ball bearings when higher load capacity or reduction in rate noise required.
Peterson, D. H.
1981-01-01
Torque-wrench extension makes it easy to install and remove fasteners that are beyond reach of typical wrenches or are located in narrow spaces that prevent full travel of wrench handle. At same time, tool reads applied torque accurately. Wrench drive system, for torques up to 125 inch-pounds, uses 2 standard drive-socket extensions in aluminum frame. Extensions are connected to bevel gear that turns another bevel gear. Gears produce 1:1 turn ratio through 90 degree translation of axis of rotation. Output bevel has short extension that is used to attach 1/4-inch drive socket.
杨影; 陈鑫; 涂小卫; 韩冰
2014-01-01
Calculation duty ratio is an important issue in Direct torque control ( DTC) of permanent mag-net synchronous motor ( PMSM) based on the duty ratio modulation. The effect of zero vector on elctro-magnetic torque during all speed range was analyzed on the basis of the relation between voltage vector and torque current component and an important phenomenon was revealed and explained that torque rip-ple in low speed range is less than that in high speed range in DTC based on the duty ratio modulation. A robust DTC method of PMSM based on the duty ratio control was designed in which the duty ratio is cal-culated with the error of torque and flux. The method is simple to implement and insensitive to motor pa-rameter error. Simulation and experiments are carried out and show that the improved DTC based on duty ratio modulation has the advantage of simplicity, robustness and improved performance in low speed.%针对如何确定占空比调制的永磁同步电机直接转矩控制的零矢量作用时间问题。根据电压矢量与电流转矩分量关系，分析了在不同转速下零矢量作用时电磁转矩的变化规律，引入占空比调制后低速区转矩脉动要小于中高速区转矩脉动。设计了基于占空比调制的永磁同步电机直接转矩控制系统，利用转矩偏差和定子磁链的偏差计算占空比，该方案易于实现，对电机参数误差不敏感。仿真和实验结果表明，该方法结构简单、鲁棒性强，能够明显改善低速性能。
杨俊华; 刘远涛; 谢景凤; 刘慧媛; 吴捷
2011-01-01
A novel sliding-mode variable structure（SMVS） control strategy is proposed to reduce the ripples of ？ux and torque of brushless double-fed machines（BDFM） based on direct torque control system（DTC）.In order to ensure the constant switching frequency for the inverter,two hysteresis regulators in the conventional DTC system are substituted by the SMVS controllers of ？ux and torque,and the space voltage vector pulse-width-modulation is used in the output of the voltage vector switches.The SMVS controllers are designed with the method of exponential approach law,and the Lyapunov stability theory is utilized to solve for the control law of the SMVS controllers.The simulation model of DTC is established in MATLAB/Simulink environment.The simulation results show that this new control method effectively reduces the torque ripples and improves the waveforms of the stator ？ux and current.The inherent advantage of the fast dynamic-response of the torque in DTC is reserved.The stability and robust performance of the system is enhanced.%针对无刷双馈电机直接转矩控制系统磁链和转矩脉动大的问题,引入滑模变结构控制策略.以转矩和磁链两个滑模控制器来代替传统直接转矩控制中的两个滞环控制器,电压矢量开关的输出采用空间电压矢量PWM调制的方法,保证了逆变器开关频率恒定,应用指数趋近率方法设计滑模控制器,由Lyapunov方法求得相应的滑模变结构控制律,建立了MATLAB/Simulink环境下直接转矩控制系统的仿真模型,仿真结果表明,新型控制方案能有效减小转矩脉动,改善定子磁链和电流波形,同时仍可保持直接转矩控制固有的转矩快速响应的优点,提高系统的稳定性和鲁棒性.
Exhaust powered drive shaft torque enhancer
Koch, A.B.
1986-09-30
This patent describes a power producing combination including an internal combustion engine and a mounting frame therefor, and power transmission means including rotating drive shaft means connected to the engine. The improvement described here is a drive shaft torque enhancing device, the device comprising: a multiplicity of blades secured to the drive shaft, equally spaced therearound, each generally lying in a plane containing the axis of the drive shaft; torque enhancer feed duct means for selectively directing a stream of exhaust gases from the engine to impact against the blades to impart torque to the drive shaft; and wherein the power producing combination is used in a vehicle, the vehicle having braking means including a brake pedal; and the power producing combination further comprising torque enhancer disengagement means responsive to motion of the brake pedal.
Displaceable Gear Torque Controlled Driver
Cook, Joseph S., Jr. (Inventor)
1997-01-01
Methods and apparatus are provided for a torque driver including a displaceable gear to limit torque transfer to a fastener at a precisely controlled torque limit. A biasing assembly biases a first gear into engagement with a second gear for torque transfer between the first and second gear. The biasing assembly includes a pressurized cylinder controlled at a constant pressure that corresponds to a torque limit. A calibrated gage and valve is used to set the desired torque limit. One or more coiled output linkages connect the first gear with the fastener adaptor which may be a socket for a nut. A gear tooth profile provides a separation force that overcomes the bias to limit torque at the desired torque limit. Multiple fasteners may be rotated simultaneously to a desired torque limit if additional output spur gears are provided. The torque limit is adjustable and may be different for fasteners within the same fastener configuration.
Direct Torque Control of Induction Motor Based on Back-stepping Principle%基于反推原理感应电机直接转矩控制
刘艳科; 申群太
2012-01-01
In order to achieve high performance on induction motor control, to solve the parameter perturbations , uncertainties and other issues , the application of a nonlinear controller based on the principle of back-stepping was used, consider the induction motor iron loss in the static model to establish non-linear motor controller. The overall stability of the controller was proved by Lyapunov theory, using the voltage space vector modulation to direct torque control system. Simulation slows that using back-stepping controller enabled direct torque control of induction motors to achieve good tracking results.%为了实现感应电机高性能控制,克服运行过程参数摄动、不确定等问题的影响,应用反推原理设计的非线性控制器,考虑感应电机的铁损.并且通过李雅普诺夫理论证明控制器的整体稳定,采用电压空间矢量调制,应用于直接转矩控制系统.仿真结果表明,采用反推原理设计控制器能够使感应电机直接转矩控制达到很好的跟踪效果,具有推广意义.
陈修波; 黎健明; 张志林; 金挺; 杭俊
2014-01-01
结合EPS系统的功能特点，提出1种适用于汽车转向的无刷直流电机（ BLDC）系统直接转矩控制（ DTC）方法，以提升汽车转向系统的控制性能。从BLDC系统的转矩特性和磁链特性的关系入手，根据电机系统的转矩、磁链跟踪误差及定子磁链所处扇区号，查询预先规划的离线查找表（ LUT），直接输出最优的电压空间矢量。并基于Matlab/Simulink仿真平台进行BLDC调速系统的仿真实验。结果表明，DTC方法可以对电磁转矩脉动进行有效控制，而且能大大提高BLDC的动、稳态驱动性能，提供了1种适用于电动助力转向EPS系统控制的新思路。%According to the functional characteristics of EPS system , this article proposes a brushless DC motor ( BLDC) direct torque control ( DTC) method which is suitable to the automobile steering to improve the control performance of the automobile steering system .Beginning with the relationship between the torque and flux linkage characteristics of BLDC system , and according to the motor system′s torque, flux tracking error and sector number of the stator , it is to search for the pre-planned offline lookup table ( LUT) and directly output the optimal voltage space vector .Finally, the experiment of BLDC control system is simulated based on the Matlab/Simulink simulation platform .The experimental results show that the DTC method can effectively control the electromagnetic torque ripple , and greatly improve the dynamic , steady driving performance of BLDC , and provide a new idea that is suitable for the EPS control of the electric power steering system .
Ironless armature torque motor
Fisher, R. L.
1972-01-01
Four iron-less armature torque motors, four Hall device position sensor assemblies, and two test fixtures were fabricated. The design approach utilized samarium cobalt permanent magnets, a large airgap, and a three-phase winding in a stationary ironless armature. Hall devices were employed to sense rotor position. An ironless armature torque motor having an outer diameter of 4.25 inches was developed to produce a torque constant of 65 ounce-inches per ampere with a resistance of 20.5 ohms. The total weight, including structural elements, was 1.58 pounds. Test results indicated that all specifications were met except for generated voltage waveform. It is recommended that investigations be made concerning the generated voltage waveform to determine if it may be improved.
Chen, Jun; Ng, Jack; Ding, Kun; Fung, Kin Hung; Lin, Zhifang; Chan, C T
2014-09-17
Light carries angular momentum, and as such it can exert torques on material objects. Applications of these opto-mechanical effects were limited initially due to their smallness in magnitude, but later becomes powerful and versatile after the invention of laser. Novel and practical approaches for harvesting light for particle rotation have since been demonstrated, where the structure is always subjected to a positive optical torque along a certain axis if the incident angular momentum has a positive projection on the same axis. We report here an interesting phenomenon of "negative optical torque", meaning that incoming photons carrying angular momentum rotate an object in the opposite sense. Surprisingly this can be realized quite straightforwardly in simple planar structures. Field retardation is a necessary condition and discrete rotational symmetry of material object plays an important role. The optimal conditions are explored and explained.
Robust spin transfer torque in antiferromagnetic tunnel junctions
Saidaoui, Hamed Ben Mohamed
2017-04-18
We theoretically study the current-induced spin torque in antiferromagnetic tunnel junctions, composed of two semi-infinite antiferromagnetic layers separated by a tunnel barrier, in both clean and disordered regimes. We find that the torque enabling electrical manipulation of the Néel antiferromagnetic order parameter is out of plane, ∼n×p, while the torque competing with the antiferromagnetic exchange is in plane, ∼n×(p×n). Here, p and n are the Néel order parameter direction of the reference and free layers, respectively. Their bias dependence shows behavior similar to that in ferromagnetic tunnel junctions, the in-plane torque being mostly linear in bias, while the out-of-plane torque is quadratic. Most importantly, we find that the spin transfer torque in antiferromagnetic tunnel junctions is much more robust against disorder than that in antiferromagnetic metallic spin valves due to the tunneling nature of spin transport.
Kish, J.
1991-01-01
Geared drive train transmits torque from input shaft in equal parts along two paths in parallel, then combines torques in single output shaft. Scheme reduces load on teeth of meshing gears while furnishing redundancy to protect against failures. Such splitting and recombination of torques common in design of turbine engines.
Self-induced torque in hyperbolic metamaterials.
Ginzburg, Pavel; Krasavin, Alexey V; Poddubny, Alexander N; Belov, Pavel A; Kivshar, Yuri S; Zayats, Anatoly V
2013-07-19
Optical forces constitute a fundamental phenomenon important in various fields of science, from astronomy to biology. Generally, intense external radiation sources are required to achieve measurable effects suitable for applications. Here we demonstrate that quantum emitters placed in a homogeneous anisotropic medium induce self-torques, aligning themselves in the well-defined direction determined by an anisotropy, in order to maximize their radiation efficiency. We develop a universal quantum-mechanical theory of self-induced torques acting on an emitter placed in a material environment. The theoretical framework is based on the radiation reaction approach utilizing the rigorous Langevin local quantization of electromagnetic excitations. We show more than 2 orders of magnitude enhancement of the self-torque by an anisotropic metamaterial with hyperbolic dispersion, having negative ratio of permittivity tensor components, in comparison with conventional anisotropic crystals with the highest naturally available anisotropy.
Torque Characteristics of Saturated Permanent-Magnet Synchronous Motors
Takahashi, Akeshi; Kikuchi, Satoshi; Wakui, Shinichi; Mikami, Hiroyuki; Ide, Kazumasa; Shima, Kazuo
The evaluation of torque characteristics in a saturated magnetic field for permanent-magnet (PM) synchronous motors is presented. The torque saturation characteristics of non-salient and salient pole machines are investigated by finite element analysis and measurement. Thus, it is found that the torque saturation originates in the magnetic saturation in both the stator teeth, which are located on the leading position toward the direct axis, and in the stator back yoke, which is located on the lagging position toward the direct axis. This mechanism can also explain the reason for the significant torque saturation in the salient-pole machine; the higher inductance of the quadrature axis of the salient-pole machine causes a significant magnetic saturation in the stator back yoke. Therefore, less saliency or a wider back yoke can improve the torque saturation.
Characterizing root distribution with adaptive neuro-fuzzy analysis
Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
Abraham, Ajith
2004-01-01
Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN a...
Application of Neuro-Fuzzy Techniques for Solar Radiation
W. A. Rahoma; U. A. Rahoma; A. H. Hassan
2011-01-01
Problem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated m...
Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach
2007-11-02
detect sigma spindles (SS), which are in the 12-14 Hz range. The electrooculogram (EOG) and the electromyogram ( EMG ) are used to determine the presence of...from the international 10-20 system (FP1-C3, C3-O1, FP2-C4, C4-O2, and C3-C4); EOG for REMov detection; tonic chin and diaphragmatic EMGs ; ECG; body...movement detection of upper and lower limbs using piezo-electric crystal transducers ; abdominal ventilatory movements, using a mercury strain gauge; and
Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC
无
2005-01-01
The proton exchange membrane generation technology is highly efficient, and clean and is considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online.This paper analyzed the characters of the PEMFC; and used the approach and self-study ability of artificial neural networks to build the model of nonlinear system, and adopted the adaptive neural-networks fuzzy infer system to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusted the model parameters to control online. The model and control were implemented in SIMULINK environment.The results of simulation show the test data and model have a good agreement. The model is useful for the optimal and real time control of PEMFC system.
Performance Enhancement of Intrusion Detection using Neuro - Fuzzy Intelligent System
Dr. K. S. Anil Kumar
2014-10-01
Full Text Available This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K-medoids Algorithm, Fuzzy Set Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.
A neuro-fuzzy architecture for real-time applications
Ramamoorthy, P. A.; Huang, Song
1992-01-01
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.
Skin Cancer Recognition by Using a Neuro-Fuzzy System
Bareqa Salah; Mohammad Alshraideh; Rasha Beidas; Ferial Hayajneh
2011-01-01
Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) ...
FACE RECOGNITION USING FEATURE EXTRACTION AND NEURO-FUZZY TECHNIQUES
Ritesh Vyas
2012-09-01
Full Text Available Face is a primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. The human ability to recognize faces is remarkable. People can recognize thousands of faces learned throughout their lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards or changes in hair style. In this work, a system is designed to recognize human faces depending on their facial features. Also to reveal the outline of the face, eyes and nose, edge detection technique has been used. Facial features are extracted in the form of distance between important feature points. After normalization, these feature vectors are learned by artificial neural network and used to recognize facial image.
Neuro-fuzzy based Controller for Solving Active Power Filter
Homayoun Ebrahimian
2016-03-01
Full Text Available In this paper, two soft computing techniques by fuzzy logic, neural network are used to design alternative control schemes for switching the APF active power filter (APF. The control of a shunt active power filter designed for harmonic and reactive current mitigation. Application of the mentioned model has been combined by an intelligent algorithm for improving the efficiency of proposed controller. Effectiveness of the proposed method has been applied over test case and shows the validity of proposed model.
Application of neuro-fuzzy methods to gamma spectroscopy
Grelle, Austin L.
Nuclear non-proliferation activities are an essential part of national security activities both domestic and abroad. The safety of the public in densely populated environments such as urban areas or large events can be compromised if devices using special nuclear materials are present. Therefore, the prompt and accurate detection of these materials is an important topic of research, in which the identification of normal conditions is also of importance. With gamma-ray spectroscopy, these conditions are identified as the radiation background, which though being affected by a multitude of factors is ever present. Therefore, in nuclear non-proliferation activities the accurate identification of background is important. With this in mind, a method has been developed to utilize aggregate background data to predict the background of a location through the use of an Artificial Neural Network (ANN). After being trained on background data, the ANN is presented with nearby relevant gamma-ray spectroscopy data---as identified by a Fuzzy Inference System - to create a predicted background spectra to compare to a measured spectra. If a significant deviation exists between the predicted and measured data, the method alerts the user such that a more thorough investigation can take place. Research herein focused on data from an urban setting in which the number of false positives was observed to be 28 out of a total of 987, representing 2.94% error. The method therefore currently shows a high rate of false positives given the current configuration, however there are promising steps that can be taken to further minimize this error. With this in mind, the method stands as a potentially significant tool in urban nuclear nonproliferation activities.
Moody, Paul E.
1995-07-01
A high-torque quiet gear construction consists of an inner hub having a plurality of circumferentially spaced arms extending radially outwardly therefrom, and an outer ring member having a plurality of circumferentially spaced-teeth extending radially inwardly therefrom. The ring member further includes a plurality of gear formations on an outer surface thereof for intermeshing with other gears. The teeth of the ring member are received in spaced relation in corresponding spaces formed between adjacent arms of the hub. An elastomeric member is received in the space formed between the hub and the ring member to form a resilient correction between the arms of the hub and the teeth of the ring member. The side surfaces of the arms and the teeth extend generally parallel to each other and at least partially overlap in a longitudinal direction. The purpose of this configuration is to place the elastomeric member in compression when torque is applied to the hub. Since elastomeric material is relatively incompressible, the result is low shear loads on the adhesive bonds which hold the elastomeric member to both the hub and outer ring member.
黄祯祥; 吴峻; 邓怀雄
2012-01-01
Based on active-disturbance rejection controller( ADRC ) theory, a direct torque control (DTC) system of permanent magnet AC servo was established. In the system uncertain external load disturbance( external disturbances) and system parameters variations (internal disturbances) were regarded as one integrated disturbance,and ADRC was used to observe and compensate this integrated disturbance. The simulation results show this system has very strong robustness not only to uncertain external load disturbance,but also to internal parameter variations such as coefficient of torque. Compared with PI controller,the system has higher dynamic performance,powerful capability of anti-interference and high control precision,etc.%设计了一种基于自抗扰控制器的永磁同步电动机直接转矩控制系统.该系统将不确定性负载扰动(外扰)和系统参数变化(内扰)视为一个综合扰动项,然后利用自抗扰控制器(ADRC)对综合扰动项进行观测和补偿.仿真结果证明,该系统不仅有效地抑制了不确定负载扰动的影响,同时对系统内部参数如电机转动惯量等摄动也具有较强的鲁棒性.该系统相比PI控制具有动态控制性能优越、抗扰能力强、控制精度高等特点.
A Comparative Performance Analysis of Torque Control Schemes for Induction Motor Drives
R Rajendran
2012-03-01
Full Text Available This paper presents a comparative study of field-oriented control(FOC, conventional direct torque control(DTC and proposed space vector modulated direct torque control with low pass filter(SVM-DTC. The main characteristics of FOC, DTC and proposed SVM-DTC schemes are studied by simulation, emphasizing their advantages and disadvantages. The performance of three control schemes is evaluated in terms of torque, current ripples and transient responses. It is shown that the proposed scheme improves the performance by combining a low torque, current ripple characteristics with fast torque dynamics.
Magnon-mediated Dzyaloshinskii-Moriya torque in homogeneous ferromagnets
Manchon, Aurelien
2014-12-01
In thin magnetic layers with structural inversion asymmetry and spin-orbit coupling, the Dzyaloshinskii-Moriya interaction arises at the interface. When a spin-wave current jm flows in a system with a homogeneous magnetization m, this interaction produces an effective fieldlike torque of the form TFLm×(z×jm) as well as a dampinglike torque, TDLm×[(z×jm)×m], the latter only in the presence of spin-wave relaxation (z is normal to the interface). These torques mediated by the magnon flow can reorient the time-averaged magnetization direction and display a number of similarities with the torques arising from the electron flow in a magnetic two-dimensional electron gas with Rashba spin-orbit coupling. This magnon-mediated spin-orbit torque can be efficient in the case of magnons driven by a thermal gradient.
Radiative torques on interstellar grains; 1, superthermal spinup
Draine, B T; Weingartner, Joseph C
1996-01-01
Irregular dust grains are subject to radiative torques when irradiated by interstellar starlight. It is shown how these radiative torques may be calculated using the discrete dipole approximation. Calculations are carried out for one irregular grain geometry, and three different grain sizes. It is shown that radiative torques can play an important dynamical role in spinup of interstellar dust grains, resulting in rotation rates which may exceed even those expected from H_2 formation on the grain surface. Because the radiative torque on an interstellar grain is determined by the overall grain geometry rather than merely the state of the grain surface, the resulting superthermal rotation is expected to be long-lived. By itself, long-lived superthermal rotation would permit grain alignment by normal paramagnetic dissipation on the "Davis-Greenstein" timescale. However, radiative torques arising from anisotropy of the starlight background can act directly to alter the grain alignment on much shorter timescales, a...
Transferability between Isolated Joint Torques and a Maximum Polyarticular Task: A Preliminary Study
Costes Antony
2016-04-01
Full Text Available The aims of this study were to determine if isolated maximum joint torques and joint torques during a maximum polyarticular task (i.e. cycling at maximum power are correlated despite joint angle and velocity discrepancies, and to assess if an isolated joint-specific torque production capability at slow angular velocity is related to cycling power. Nine cyclists completed two different evaluations of their lower limb maximum joint torques. Maximum Isolated Torques were assessed on isolated joint movements using an isokinetic ergometer and Maximum Pedalling Torques were calculated at the ankle, knee and hip for flexion and extension by inverse dynamics during cycling at maximum power. A correlation analysis was made between Maximum Isolated Torques and respective Maximum Pedalling Torques [3 joints x (flexion + extension], showing no significant relationship. Only one significant relationship was found between cycling maximum power and knee extension Maximum Isolated Torque (r=0.68, p<0.05. Lack of correlations between isolated joint torques measured at slow angular velocity and the same joint torques involved in a polyarticular task shows that transfers between both are not direct due to differences in joint angular velocities and in mono-articular versus poly articular joint torque production capabilities. However, this study confirms that maximum power in cycling is correlated with slow angular velocity mono-articular maximum knee extension torque.
Spin transfer torque in antiferromagnetic spin valves: From clean to disordered regimes
Saidaoui, Hamed Ben Mohamed
2014-05-28
Current-driven spin torques in metallic spin valves composed of antiferromagnets are theoretically studied using the nonequilibrium Green\\'s function method implemented on a tight-binding model. We focus our attention on G-type and L-type antiferromagnets in both clean and disordered regimes. In such structures, spin torques can either rotate the magnetic order parameter coherently (coherent torque) or compete with the internal antiferromagnetic exchange (exchange torque). We show that, depending on the symmetry of the spin valve, the coherent and exchange torques can either be in the plane, ∝n×(q×n) or out of the plane ∝n×q, where q and n are the directions of the order parameter of the polarizer and the free antiferromagnetic layers, respectively. Although disorder conserves the symmetry of the torques, it strongly reduces the torque magnitude, pointing out the need for momentum conservation to ensure strong spin torque in antiferromagnetic spin valves.
Insertion torque, resonance frequency, and removal torque analysis of microimplants.
Tseng, Yu-Chuan; Ting, Chun-Chan; Du, Je-Kang; Chen, Chun-Ming; Wu, Ju-Hui; Chen, Hong-Sen
2016-09-01
This study aimed to compare the insertion torque (IT), resonance frequency (RF), and removal torque (RT) among three microimplant brands. Thirty microimplants of the three brands were used as follows: Type A (titanium alloy, 1.5-mm × 8-mm), Type B (stainless steel, 1.5-mm × 8-mm), and Type C (titanium alloy, 1.5-mm × 9-mm). A synthetic bone with a 2-mm cortical bone and bone marrow was used. Each microimplant was inserted into the synthetic bone, without predrilling, to a 7 mm depth. The IT, RF, and RT were measured in both vertical and horizontal directions. One-way analysis of variance and Spearman's rank correlation coefficient tests were used for intergroup and intragroup comparisons, respectively. In the vertical test, the ITs of Type C (7.8 Ncm) and Type B (7.5 Ncm) were significantly higher than that of Type A (4.4 Ncm). The RFs of Type C (11.5 kHz) and Type A (10.2 kHz) were significantly higher than that of Type B (7.5 kHz). Type C (7.4 Ncm) and Type B (7.3 Ncm) had significantly higher RTs than did Type A (4.1 Ncm). In the horizontal test, both the ITs and RTs were significantly higher for Type C, compared with Type A. No significant differences were found among the groups, and the study hypothesis was accepted. Type A had the lowest inner/outer diameter ratio and widest apical facing angle, engendering the lowest IT and highest RF values. However, no significant correlations in the IT, RF, and RT were observed among the three groups.
Xu, Xiaohao; Cheng, Chang; Zhang, Yao; Lei, Hongxiang; Li, Baojun
2016-01-21
Linearly polarized light can exert an orienting torque on plasmonic nanorods. The torque direction has generally been considered to change when the light wavelength passes through a plasmon longitudinal resonance. Here, we use the Maxwell stress tensor to evaluate this torque in general terms. According to distinct light-matter interaction processes, the total torque is decomposed into scattering and extinction torques. The scattering torque tends to orient plasmonic nanorods parallel to the light polarization, independent of the choice of light wavelength. The direction of the extinction torque is not only closely tied to the excitation of plasmon resonance but also depends on the specific plasmon mode around which the light wavelength is tuned. Our findings show that the conventional wisdom that simply associates the total torque with the plasmon longitudinal resonances needs to be replaced with an understanding based on the different torque components and the details of spectral distribution.
Single-interface Casimir torque
Morgado, Tiago A.; Silveirinha, Mário G.
2016-10-01
A different type of Casimir-type interaction is theoretically predicted: a single-interface torque at a junction of an anisotropic material and a vacuum or another material system. The torque acts to reorient the polarizable microscopic units of the involved materials near the interface, and thus to change the internal structure of the materials. The single-interface torque depends on the zero-point energy of the interface localized and extended modes. Our theory demonstrates that the single-interface torque is essential to understand the Casimir physics of material systems with anisotropic elements and may influence the orientation of the director of nematic liquid crystals.
Interaction torque contributes to planar reaching at slow speed
Hoshi Fumihiko
2008-10-01
Full Text Available Abstract Background How the central nervous system (CNS organizes the joint dynamics for multi-joint movement is a complex problem, because of the passive interaction among segmental movements. Previous studies have demonstrated that the CNS predictively compensates for interaction torque (INT which is arising from the movement of the adjacent joints. However, most of these studies have mainly examined quick movements, presumably because the current belief is that the effects of INT are not significant at slow speeds. The functional contribution of INT for multijoint movements performed in various speeds is still unclear. The purpose of this study was to examine the contribution of INT to a planer reaching in a wide range of motion speeds for healthy subjects. Methods Subjects performed reaching movements toward five targets under three different speed conditions. Joint position data were recorded using a 3-D motion analysis device (50 Hz. Torque components, muscle torque (MUS, interaction torque (INT, gravity torque (G, and net torque (NET were calculated by solving the dynamic equations for the shoulder and elbow. NET at a joint which produces the joint kinematics will be an algebraic sum of torque components; NET = MUS - G - INT. Dynamic muscle torque (DMUS = MUS-G was also calculated. Contributions of INT impulse and DMUS impulse to NET impulse were examined. Results The relative contribution of INT to NET was not dependent on speed for both joints at every target. INT was additive (same direction to DMUS at the shoulder joint, while in the elbow DMUS counteracted (opposed to INT. The trajectory of reach was linear and two-joint movements were coordinated with a specific combination at each target, regardless of motion speed. However, DMUS at the elbow was opposed to the direction of elbow movement, and its magnitude varied from trial to trial in order to compensate for the variability of INT. Conclusion Interaction torque was important at
Optimizing Casimir torque between corrugated metallic plates
Rodrigues, Robson B. [Universidade Federal Fluminense, Niteroi, RJ (Brazil); Maia Neto, Paulo A. [Instituto de Fisica, Universidade Federal do Rio de Janeiro, RJ (Brazil)
2013-07-01
Full text: The Casimir effect plays a major role in micro- and nano-electromechanical systems (MEMS and NEMS). Besides the normal Casimir force between metallic or dielectric plates, the observation of the lateral Casimir force between corrugated plates opens novel possibilities of micro-mechanical control. The lateral force results from breaking the translational symmetry along directions parallel to the plates by imprinting periodic corrugations on both metallic plates. As the rotational symmetry is broken by this geometry, a Casimir torque arises when the corrugations are not aligned. We calculate the Casimir torque between two parallel metallic plates with surface profiles in the form of 'fans' with arbitrary relative spatial orientation. As compared to the case of anisotropic dielectric plates, the torque per unit area is increased by up to three orders of magnitude for a given separation distance. The experiment proposed here can be performed with torsion pendulum techniques for separation distances as large as 1 μm. From the point of view of fundamental physics, this torque makes possible a precise experimental investigation of the non-trivial geometry dependence of the Casimir effect. We follow the scattering approach and calculate the Casimir energy up to second order in the corrugation amplitudes, taking into account nonspecular reflections, polarization mixing and the finite conductivity of the metals. We investigate the experimental conditions that optimize the effect. (author)
Comparison of different passive knee extension torque-angle assessments.
Freitas, Sandro R; Vaz, João R; Bruno, Paula M; Valamatos, Maria J; Mil-Homens, Pedro
2013-11-01
Previous studies have used isokinetic dynamometry to assess joint torques and angles during passive extension of the knee, often without reporting upon methodological errors and reliability outcomes. In addition, the reliability of the techniques used to measure passive knee extension torque-angle and the extent to which reliability may be affected by the position of the subjects is also unclear. Therefore, we conducted an analysis of the intra- and inter-session reliability of two methods of assessing passive knee extension: (A) a 2D kinematic analysis coupled to a custom-made device that enabled the direct measurement of resistance to stretch and (B) an isokinetic dynamometer used in two testing positions (with the non-tested thigh either flexed at 45° or in the neutral position). The intra-class correlation coefficients (ICCs) of torque, the slope of the torque-angle curve, and the parameters of the mathematical model that were fit to the torque-angle data for the above conditions were measured in sixteen healthy male subjects (age: 21.4 ± 2.1 yr; BMI: 22.6 ± 3.3 kg m(-2); tibial length: 37.4 ± 3.4 cm). The results found were: (1) methods A and B led to distinctly different torque-angle responses; (2) passive torque-angle relationship and stretch tolerance were influenced by the position of the non-tested thigh; and (3) ICCs obtained for torque were higher than for the slope and for the mathematical parameters that were fit to the torque-angle curve. In conclusion, the measurement method that is used and the positioning of subjects can influence the passive knee extension torque-angle outcome.
ESTIMATION OF GRASPING TORQUE USING ROBUST REACTION TORQUE OBSERVER FOR ROBOTIC FORCEPS
塚本, 祐介; Tsukamoto, Yusuke
2015-01-01
Abstract— In this paper, the estimation of the grasping torque of robotic forceps without the use of a force/torque sensor is discussed. To estimate the grasping torque when the robotic forceps driven by a rotary motor with a reduction gear grasps an object, a novel robust reaction torque observer is proposed. In the case where a conventional reaction force/torque observer is applied, the estimated torque includes not only the grasping torque, namely the reaction torque, but also t...
王莹; 胡育文; 杨建飞
2011-01-01
在以往的永磁同步电机直接转矩控制研究中,将零电压矢量引入到转矩调节器中,认为其起到保持当前转矩的作用.该文对永磁同步电机中零电压矢量对转矩脉动和磁链脉动的影响进行了详细的理论分析,指出在电机高速运行的情况下,零矢量实际是起到减小转矩的作用.转速越高,零矢量减小转矩的效果越明显.仿真结果证明了理论分析的正确性,这为正确理解零矢量在永磁同步电机直接转矩控制的作用以及今后对零矢量作用的范围进行优化设计具有指导意义.%In previous study of permanent magnet synchronous motor direct torque control ( PMSM DTC ) , considered to keep the torque stable, zero voltage vector was introduced into the torque controller. The influence of zero voltage vector on flux and torque ripple in PMSM DTC. Was analyzed detailedly in this paper. And it pointed out that, when the motor run with a high speed, zero voltage vector actually reduced the torque. The higher the rotor speed was, the more obvious the effect of reducing the torque was. The simulation result verified the validity of the theory analysis. It is a significant guide for understanding correctly the function of zero voltage vector in PMSM DTC and optimizing the design of zero voltage vector working zone in the future.
Direct Torque Control System based on Neural Network Speed Recognization%基于神经网络速度辨识的直接转矩控制系统
刘文胜; 王一; 王新伟
2007-01-01
介绍了一种采用电压空间矢量脉宽调制(Space Vector PWM,简称SVPWM)的新型直接转矩控制(Direct Torque Control,简称DTC)系统.针对DTC系统存在的低速时转矩脉动问题,将电压空间矢量与DTC技术相结合,用PI调节的方法取代了传统DTC系统中采用的滞环控制,提出了一种基于SVPWM的DTC方法.在SVPWM-DTC系统中采用了神经网络速度辨识器,通过神经网络对电机的定、转子磁链和转速进行在线辨识.实验结果表明,这种新型DTC系统有着良好的动、静态性能和全速范围的调速精度.
李兴友; 李彦
2012-01-01
The structure and characteristics of ship electric propulsion system are introduced. The principle of direct torque control(DTC) based on space vector modulation(SVM) is analyzed. The compute method of expect voltage vector and ship-airscrew model are introduced, and the simulation model of ship electric propulsion system DTC in Matlab/Simulink environment is constructed. The simulation results indicate that the performance of the propulsion system is improved with the application of DTC based on SVM.%介绍了船舶电力推进系统的结构和特点；分析了基于空间矢量调制(SVM)直接转矩控制(DTC)的原理；介绍求取预期电压矢量的方法以及船舶的船-桨模型；在Matlab/Simulink搭建了船舶电力推进DTC仿真模型.仿真结果表明基于SVM的DTC可以提高船舶电力推进系统的性能.
张凤阁; 金石; 张武
2011-01-01
A maximal power tracking method through controlling torque and power factor of brushless doubly-fed wind power generator to regulate active power is proposed based on direct torque control （DTC）, according to the power output characteristics of wind turbine. Considering the complicated relation of internal magnetic fields of brushless doubly-fed generator（BDFG）, a novel speed observer is designed by estimating the rotor flux synchronous rotating speed and slip speed to improve the reliability of wind power generation system in harsh wind field environment. Furthermore, the model reference adaptive identification technology is adopted to design control winding flux observer in order to enhance the accuracy of estimated flux within wide speed range and further to improve the operation performance of the DTC system at the low frequency of the control winding. Simulation results prove the validity and feasibility of the proposed novel speed estimation method and DTC scheme.%根据风力机的功率输出特性，针对无刷双馈风力发电机，提出了一种基于直接转矩控制方法控制发电机转矩和功率因数来调节有功功率的最大功率跟踪方法。为了提高风电系统的可靠性，使系统更能适用于恶劣的风场环境，本文针对无刷双馈发电机复杂的内部磁场关系，设计了一种新型速度观测器，通过估算转子磁链同步旋转速度和转差速度来获得发电机转速。此外，还采用模型参考自适应辨识技术来设计控制绕组磁链观测器，以提高无刷双馈发电机在全速范围内磁链估计的准确性，进而改善直接转矩控制系统在控制绕组侧低频时的运行性能。仿真结果验证了所提出的新型转速估算方法的正确性和直接转矩控制方案的可行性。
An ironless armature brushless torque motor
Studer, P. A.
1973-01-01
A high torque motor with improved servo mechanism is reported. Armature windings are cast into an epoxy cylinder and armature conductors are integrally cast with an aluminum mounting ring which provides thermal conductance directly into the structure. This configuration eliminates magnetic hysteresis because there is no relative motion between the rotating magnetic field and any stationary iron. The absence of destabilization forces provides a fast electrical response compared with a typical torquer of conventional construction.
Cogging torque mitigation of modular permanent magnet machines
Li, G. J.; Ren, B.; Zhu, Z-Q.; Li, Y X; Ma, J.
2015-01-01
This paper proposes a novel cogging torque mitigation method for modular permanent magnet (PM) machines with flux gaps in alternate stator teeth. The slot openings of the modular PM machines are divided into two groups in a special way. By shifting the slot openings of two groups in opposite directions with the same angle, the machine cogging torque can be significantly reduced. Analytical formula of the desired shift angle is derived, and can be applicable to other modular machines with diff...
Current-induced torques and interfacial spin-orbit coupling
Haney, Paul M.
2013-12-19
In bilayer systems consisting of an ultrathin ferromagnetic layer adjacent to a metal with strong spin-orbit coupling, an applied in-plane current induces torques on the magnetization. The torques that arise from spin-orbit coupling are of particular interest. Here we use first-principles methods to calculate the current-induced torque in a Pt-Co bilayer to help determine the underlying mechanism. We focus exclusively on the analog to the Rashba torque, and do not consider the spin Hall effect. The details of the torque depend strongly on the layer thicknesses and the interface structure, providing an explanation for the wide variation in results found by different groups. The torque depends on the magnetization direction in a way similar to that found for a simple Rashba model. Artificially turning off the exchange spin splitting and separately the spin-orbit coupling potential in the Pt shows that the primary source of the “fieldlike” torque is a proximate spin-orbit effect on the Co layer induced by the strong spin-orbit coupling in the Pt.
Hybrid synchronous motor electromagnetic torque research
Suvorkova Elena E.
2014-01-01
Full Text Available Electromagnetic field distribution models in reluctance and permanent magnet parts were made by means of Elcut. Dependences of electromagnetic torque on torque angle were obtained.
A hybrid attitude controller consisting of electromagnetic torque rods and an active fluid ring
Nobari, Nona A.; Misra, Arun K.
2014-01-01
In this paper, a novel hybrid actuation system for satellite attitude stabilization is proposed along with its feasibility analysis. The system considered consists of two magnetic torque rods and one fluid ring to produce the control torque required in the direction in which magnetic torque rods cannot produce torque. A mathematical model of the system dynamics is derived first. Then a controller is developed to stabilize the attitude angles of a satellite equipped with the abovementioned set of actuators. The effect of failure of the fluid ring or a magnetic torque rod is examined as well. It is noted that the case of failure of the magnetic torque rod whose torque is along the pitch axis is the most critical, since the coupling between the roll or yaw motion and the pitch motion is quite weak. The simulation results show that the control system proposed is quite fault tolerant.
Estimation for torques applied to the master side in a construction robot teleoperation system
Huang Lingtao
2016-01-01
Full Text Available The purpose of this research is to develop a method that measures a torque which an operator applies to the joystick without using a torque/force sensor on a joystick. We dealt with a construction robot teleoperation system which is comprised of two joysticks as the master, and an excavator with four degrees of freedom consisting of fork glove, swing, boom, and arm as the slave. It is necessary to know the torque (force exerted on the master side in force control. Because the joystick does not have a force/torque sensor in our lab, it is not possible to directly obtain the torque. To give prominence to the simple and practical construction robot teleoperation system in our lab, we proposed a method that measures the torque exerted on the joystick to existing equipment. Its effectiveness was verified by a reaction torque experiment.
Analyzing the installation angle error of a SAW torque sensor
Fan, Yanping; Ji, Xiaojun; Cai, Ping
2014-09-01
When a torque is applied to a shaft, normal strain oriented at ±45° direction to the shaft axis is at its maximum, which requires two one-port SAW resonators to be bonded to the shaft at ±45° to the shaft axis. In order to make the SAW torque sensitivity high enough, the installation angle error of two SAW resonators must be confined within ±5° according to our design requirement. However, there are few studies devoted to the installation angle analysis of a SAW torque sensor presently and the angle error was usually obtained by a manual method. Hence, we propose an approximation method to analyze the angle error. First, according to the sensitive mechanism of the SAW device to torque, the SAW torque sensitivity is deduced based on the linear piezoelectric constitutive equation and the perturbation theory. Then, when a torque is applied to the tested shaft, the stress condition of two SAW resonators mounted with an angle deviating from ±45° to the shaft axis, is analyzed. The angle error is obtained by means of the torque sensitivities of two orthogonal SAW resonators. Finally, the torque measurement system is constructed and the loading and unloading experiments are performed twice. The torque sensitivities of two SAW resonators are obtained by applying average and least square method to the experimental results. Based on the derived angle error estimation function, the angle error is estimated about 3.447°, which is close to the actual angle error 2.915°. The difference between the estimated angle and the actual angle is discussed. The validity of the proposed angle error analysis method is testified to by the experimental results.
Manipulating the voltage dependence of tunneling spin torques
Manchon, Aurelien
2012-10-01
Voltage-driven spin transfer torques in magnetic tunnel junctions provide an outstanding tool to design advanced spin-based devices for memory and reprogrammable logic applications. The non-linear voltage dependence of the torque has a direct impact on current-driven magnetization dynamics and on devices performances. After a brief overview of the progress made to date in the theoretical description of the spin torque in tunnel junctions, I present different ways to alter and control the bias dependence of both components of the spin torque. Engineering the junction (barrier and electrodes) structural asymmetries or controlling the spin accumulation profile in the free layer offer promising tools to design effcient spin devices.
Charge-Induced Spin Torque in Anomalous Hall Ferromagnets
Nomura, Kentaro; Kurebayashi, Daichi
2015-09-01
We demonstrate that spin-orbit coupled electrons in a magnetically doped system exert a spin torque on the local magnetization, without a flowing current, when the chemical potential is modulated in a magnetic field. The spin torque is proportional to the anomalous Hall conductivity, and its effective field strength may overcome the Zeeman field. Using this effect, the direction of the local magnetization is switched by gate control in a thin film. This charge-induced spin torque is essentially an equilibrium effect, in contrast to the conventional current-induced spin-orbit torque, and, thus, devices using this operating principle possibly have higher efficiency than the conventional ones. In addition to a comprehensive phenomenological derivation, we present a physical understanding based on a model of a Dirac-Weyl semimetal, possibly realized in a magnetically doped topological insulator. The effect might be realized also in nanoscale transition materials, complex oxide ferromagnets, and dilute magnetic semiconductors.
Torque analysis for double-stator permanent-magnet motor
柴凤; 程树康; 崔淑梅
2002-01-01
In addition to the characteristics of a conventional motor, a novel direct-drive double-stator perma-nent-magnet brushless motor proposed can operate in the state of either a generator or a motor as appropriate.Through numerical calculation and analysis, the output torque of double-stator permanent-magnet brushless motor of the same volume as the traditional machine is discussed, and the reduction of torque ripple by using the structure features of this motor is investigated. The results indicate that lower torque ripple under the condition of ideal effective torque can be obtained by the rational design of motor. The prototype motors tested show that this kind of motor structure has a higher power density.
Mechanics of torque generation in the bacterial flagellar motor
Mandadapu, Kranthi K; Berry, Richard M; Oster, George
2015-01-01
The bacterial flagellar motor (BFM) is responsible for driving bacterial locomotion and chemotaxis, fundamental processes in pathogenesis and biofilm formation. In the BFM, torque is generated at the interface between transmembrane proteins (stators) and a rotor. It is well-established that the passage of ions down a transmembrane gradient through the stator complex provides the energy needed for torque generation. However, the physics involved in this energy conversion remain poorly understood. Here we propose a mechanically specific model for torque generation in the BFM. In particular, we identify two fundamental forces involved in torque generation: electrostatic and steric. We propose that electrostatic forces serve to position the stator, while steric forces comprise the actual 'power stroke'. Specifically, we predict that ion-induced conformational changes about a proline 'hinge' residue in an $\\alpha$-helix of the stator are directly responsible for generating the power stroke. Our model predictions f...
许桢
2012-01-01
This paper uses Simulink/WPower System, use structured and modular method of asynchronous motor, variable frequency speed regulation System, models and simulation, according to PWM inverter power supply of induction motor drive direct torque control variable frequency speed regulation System, under the characteristics of the Matlab environment, and introduces the method to construct the son modules and functions. The construction of the simulation model and the practical variable frequency speed regulation system for high performance, are close to the asynchronous motor speed control system design and commissioning provides a good inspection means and achieve. It is simple and convenient, easy to modify the model, and the simulation results verify the effectiveness of the method.%针对PWM逆变器供电驱动的异步电机直接转矩控制变频调速系统的特点,在Matlah环境下,利用Simulink/Power System,采用结构化和模块化的方法,对异步电机变频调速系统进行了建模和仿真,并详细介绍了各子模块的构造方法及功能.构建的仿真模型与实际变频调速系统比较接近,为高性能的异步电机变频调速控制系统地设计与调试提供了一种较好的检验手段,且实现简单,便于修改.仿真结果验证了建模方法的有效性.
Drag Torque Prediction Model for the Wet Clutches
HU Jibin; PENG Zengxiong; YUAN Shihua
2009-01-01
Reduction of drag torque in disengaged wet clutch is one of important potentials for vehicle transmission improvement. The flow of the oil film in clutch clearance is investigated. A three-dimension Navier-Stokes(N-S) equation based on laminar flow is presented to model the drag torque. Pressure and speed distribution in radial and circumferential directions are deduced. The theoretical analysis reveals that oil flow acceleration in radial direction caused by centrifugal force is the key reason for the shrinking of oil film as constant feeding flow rate. The peak drag torque occurs at the beginning of oil film shrinking. A variable is introduced to describe effective oil film area and drag torque after oil film shrinking is well evaluated with the variable. Under the working condition, tests were made to obtain drag torque curves at different clutch speed and oil viscosity. The tests confirm that simulation results agree with test data. The model performs well in the prediction of drag torque and lays a theoretical foundation to reduce it.
He HAO; Wei-zhong FEI; Dong-min MIAO; Meng-jia JIN; Jian-xin SHEN
2016-01-01
In this study, we investigated the torque characteristics of large low-speed direct-drive permanent magnet synchronous generators with stator radial ventilating air ducts for offshore wind power applications. Magnet shape optimization was used fi rst to improve the torque characteristics using two-dimensional fi nite element analysis (FEA) in a permanent magnet synchronous generator with a common stator. The rotor step skewing technique was then employed to suppress the impacts of mechanical tolerances and defects, which further improved the torque quality of the machine. Comprehensive three-dimensional FEA was used to evaluate accurately the overall effects of stator radial ventilating air ducts and rotor step skewing on torque features. The infl uences of the radial ventilating ducts in the stator on torque characteristics, such as torque pulsation and average torque in the machine with and without rotor step skewing techniques, were comprehensively investigated using three-dimensional FEA. The results showed that stator radial ventilating air ducts could not only reduce the average torque but also increase the torque ripple in the machine. Furthermore, the torque ripple of the machine under certain load conditions may even be increased by rotor step skewing despite a reduction in cogging torque.
Mcdougal, A. R.; Norman, R. M. (Inventor)
1976-01-01
A gear head wrench particularly suited for use in applying torque to bolts without transferring torsional stress to bolt-receiving structures is introduced. The wrench is characterized by a coupling including a socket, for connecting a bolt head with a torque multiplying gear train, provided within a housing having an annulus concentrically related to the socket and adapted to be coupled with a spacer interposed between the bolt head and the juxtaposed surface of the bolt-receiving structure for applying a balancing counter-torque to the spacer as torque is applied to the bolt head whereby the bolt-receiving structure is substantially isolated from torsional stress. As a result of the foregoing, the operator of the wrench is substantially isolated from any forces which may be imposed.
Planet migration and magnetic torques
Strugarek, A.; Brun, A. S.; Matt, S. P.; Reville, V.
2016-10-01
The possibility that magnetic torques may participate in close-in planet migration has recently been postulated. We develop three dimensional global models of magnetic star-planet interaction under the ideal magnetohydrodynamic (MHD) approximation to explore the impact of magnetic topology on the development of magnetic torques. We conduct twin numerical experiments in which only the magnetic topology of the interaction is altered. We find that magnetic torques can vary by roughly an order of magnitude when varying the magnetic topology from an aligned case to an anti-aligned case. Provided that the stellar magnetic field is strong enough, we find that magnetic migration time scales can be as fast as ~100 Myr. Hence, our model supports the idea that magnetic torques may participate in planet migration for some close-in star-planet systems.
14 CFR 27.361 - Engine torque.
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Engine torque. 27.361 Section 27.361... STANDARDS: NORMAL CATEGORY ROTORCRAFT Strength Requirements Flight Loads § 27.361 Engine torque. (a) For turbine engines, the limit torque may not be less than the highest of— (1) The mean torque for...
Vess, Melissa F.; Starin, Scott R.
2007-01-01
During design of the SDO Science and Inertial mode PID controllers, the decision was made to disable the integral torque whenever system stability was in question. Three different schemes were developed to determine when to disable or enable the integral torque, and a trade study was performed to determine which scheme to implement. The trade study compared complexity of the control logic, risk of not reenabling the integral gain in time to reject steady-state error, and the amount of integral torque space used. The first scheme calculated a simplified Routh criterion to determine when to disable the integral torque. The second scheme calculates the PD part of the torque and looked to see if that torque would cause actuator saturation. If so, only the PD torque is used. If not, the integral torque is added. Finally, the third scheme compares the attitude and rate errors to limits and disables the integral torque if either of the errors is greater than the limit. Based on the trade study results, the third scheme was selected. Once it was decided when to disable the integral torque, analysis was performed to determine how to disable the integral torque and whether or not to reset the integrator once the integral torque was reenabled. Three ways to disable the integral torque were investigated: zero the input into the integrator, which causes the integral part of the PID control torque to be held constant; zero the integral torque directly but allow the integrator to continue integrating; or zero the integral torque directly and reset the integrator on integral torque reactivation. The analysis looked at complexity of the control logic, slew time plus settling time between each calibration maneuver step, and ability to reject steady-state error. Based on the results of the analysis, the decision was made to zero the input into the integrator without resetting it. Throughout the analysis, a high fidelity simulation was used to test the various implementation methods.
Modeling Grain Alignment by Radiative Torques and Hydrogen Formation Torques in Reflection Nebula
Hoang, Thiem; Andersson, B-G
2014-01-01
Reflection nebulae--dense cores--illuminated by surrounding stars offer a unique opportunity to directly test our quantitative model of grain alignment based on radiative torques (RATs) and to explore new effects arising from additional torques. In this paper, we first perform detailed modeling of grain alignment by RATs for the IC 63 reflection nebula illuminated both by a nearby $\\gamma$ Cas star and the diffuse interstellar radiation field. We calculate linear polarization $p$ of background stars by radiatively aligned grains and explore the variation of fractional polarization (p/A$_V)$ with visual extinction $A_{V}$ across the cloud. We show that the variation of $p/A_{V}$ from the surface of the dayside toward the IC 63 center can be described by a power law $p/A_{V}\\propto A_{V}^{\\eta}$, having a shallow slope $\\eta \\sim- 0.1$ for $A_{V} 4$. We then consider the effects of additional torques due to H$_{2}$ formation and model grain alignment by joint action of RATs and H$_2$ torques. We find that p/A$_...
DTC Based Induction Motor Speed Control Using 10-Sector Methodology for Torque Ripple Reduction
Pavithra, S.; Dinesh Krishna, A. S.; Shridharan, S.
2014-09-01
A direct torque control (DTC) drive allows direct and independent control of flux linkage and electromagnetic torque by the selection of optimum inverter switching modes. It is a simple method of signal processing which gives excellent dynamic performance. Also transformation of coordinates and voltage decoupling are not required. However, the possible discrete inverter switching vectors cannot always generate exact stator voltage required, to obtain the demanded electromagnetic torque and flux linkages. This results in the production of ripples in the torque as well as flux waveforms. In the present paper a torque ripple reduction methodology is proposed. In this method the circular locus of flux phasor is divided into 10 sector as compared to six sector divisions in conventional DTC method. The basic DTC scheme and the 10-sector method are simulated and compared for their performance. An analysis is done with sector increment so that finally the torque ripple varies slightly as the sector is increased.
张炳义; 刘博年; 冯桂宏; 丁宏龙
2016-01-01
传统油田修井机采用柴油机或异步电机驱动.柴油机驱动系统的工作效率低、能耗大、污染高.异步电机和减速箱组成的驱动系统的功率因数低、系统维护复杂、噪声大.永磁电机直驱系统具有结构简单,高效节能,系统维护简单,噪声低等优点,可以解决传统柴油机以及异步电机存在的问题.该系统还具有易于控制,运行稳定的优势.通过单星形与双串星形双绕组变换的方法实现电机的宽恒功率调速.采用特殊表贴式转子结构可以削弱电机转矩脉动.控制上采用闭环矢量控制来实现电机的平稳运行以及带载悬停等功能.%The traditional oil field workover adopts a diesel engine or asynchronous motor as the drive.The diesel engine driving system has low efficiency,high energy consumption and high pol-lution.The drive system of the induction motor with the gearbox has low power factor,system maintenance is complex and the noise is large.Permanent magnetic motor direct drive system has the advantages of simple structure,high efficiency and energy saving,system maintenance is sim-ple and the noise is low.Permanent magnetic motor direct drive system can solve the problem of traditional diesel engine and asynchronous machine.At the same time,the system also has the ad-vantages of easy control and stable operation.The wide range of constant power expansion of the motor is realized by using the transformation of single star winding and double star winding .The torque ripple of the motor can be reduced by using a special form of surface mounted rotor struc-ture.The closed-loop vector control is adopted to realize the stable running of the motor and the function of cease with load.
Angular dependence of spin-orbit spin-transfer torques
Lee, Ki-Seung
2015-04-06
In ferromagnet/heavy-metal bilayers, an in-plane current gives rise to spin-orbit spin-transfer torque, which is usually decomposed into fieldlike and dampinglike torques. For two-dimensional free-electron and tight-binding models with Rashba spin-orbit coupling, the fieldlike torque acquires nontrivial dependence on the magnetization direction when the Rashba spin-orbit coupling becomes comparable to the exchange interaction. This nontrivial angular dependence of the fieldlike torque is related to the Fermi surface distortion, determined by the ratio of the Rashba spin-orbit coupling to the exchange interaction. On the other hand, the dampinglike torque acquires nontrivial angular dependence when the Rashba spin-orbit coupling is comparable to or stronger than the exchange interaction. It is related to the combined effects of the Fermi surface distortion and the Fermi sea contribution. The angular dependence is consistent with experimental observations and can be important to understand magnetization dynamics induced by spin-orbit spin-transfer torques.
Immediate effects of whole body vibration on patellar tendon properties and knee extension torque.
Rieder, F; Wiesinger, H-P; Kösters, A; Müller, E; Seynnes, O R
2016-03-01
Reports about the immediate effects of whole body vibration (WBV) exposure upon torque production capacity are inconsistent. However, the changes in the torque-angle relationship observed by some authors after WBV may hinder the measurement of torque changes at a given angle. Acute changes in tendon mechanical properties do occur after certain types of exercise but this hypothesis has never been tested after a bout of WBV. The purpose of the present study was to investigate whether tendon compliance is altered immediately after WBV, effectively shifting the optimal angle of peak torque towards longer muscle length. Twenty-eight subjects were randomly assigned to either a WBV (n = 14) or a squatting control group (n = 14). Patellar tendon CSA, stiffness and Young's modulus and knee extension torque-angle relationship were measured using ultrasonography and dynamometry 1 day before and directly after the intervention. Tendon CSA was additionally measured 24 h after the intervention to check for possible delayed onset of swelling. The vibration intervention had no effects on patellar tendon CSA, stiffness and Young's modulus or the torque-angle relationship. Peak torque was produced at ~70° knee angle in both groups at pre- and post-test. Additionally, the knee extension torque globally remained unaffected with the exception of a small (-6%) reduction in isometric torque at a joint angle of 60°. The present results indicate that a single bout of vibration exposure does not substantially alter patellar tendon properties or the torque-angle relationship of knee extensors.
Casimir torque in weak coupling
Milton, Kimball A; Long, William
2013-01-01
In this paper, dedicated to Johan H{\\o}ye on the occasion of his 70th birthday, we examine manifestations of Casimir torque in the weak-coupling approximation, which allows exact calculations so that comparison with the universally applicable, but generally uncontrolled, proximity force approximation may be made. In particular, we examine Casimir energies between planar objects characterized by $\\delta$-function potentials, and consider the torque that arises when angles between the objects are changed. The results agree very well with the proximity force approximation when the separation distance between the objects is small compared with their sizes. In the opposite limit, where the size of one object is comparable to the separation distance, the shape dependence starts becoming irrelevant. These calculations are illustrative of what to expect for the torques between, for example, conducting planar objects, which eventually should be amenable to both improved theoretical calculation and experimental verific...
Multi-digit maximum voluntary torque production on a circular object
SHIM, JAE KUN; HUANG, JUNFENG; HOOKE, ALEXANDER W.; LATSH, MARK L.; ZATSIORSKY, VLADIMIR M.
2010-01-01
Individual digit-tip forces and moments during torque production on a mechanically fixed circular object were studied. During the experiments, subjects positioned each digit on a 6-dimensional force/moment sensor attached to a circular handle and produced a maximum voluntary torque on the handle. The torque direction and the orientation of the torque axis were varied. From this study, it is concluded that: (1) the maximum torque in the closing (clockwise) direction was larger than in the opening (counter clockwise) direction; (2) the thumb and little finger had the largest and the smallest share of both total normal force and total moment, respectively; (3) the sharing of total moment between individual digits was not affected by the orientation of the torque axis or by the torque direction, while the sharing of total normal force between the individual digit varied with torque direction; (4) the normal force safety margins were largest and smallest in the thumb and little finger, respectively. PMID:17454086
High torque miniature rotary actuator
Nalbandian, Ruben
2005-07-01
This paper summarizes the design and the development of a miniature rotary actuator (36 mm diameter by 100 mm length) used in spacecraft mechanisms requiring high torques and/or ultra-fine step resolution. This actuator lends itself to applications requiring high torque but with strict volume limitations which challenge the use of conventional rotary actuators. The design challenge was to develop a lightweight (less than 500 grams), very compact, high bandwidth, low power, thermally stable rotary actuator capable of producing torques in excess of 50 N.m and step resolutions as fine as 0.003 degrees. To achieve a relatively high torsional stiffness in excess of 1000 Nm/radian, the design utilizes a combination of harmonic drive and multistage planetary gearing. The unique design feature of this actuator that contributes to its light weight and extremely precise motion capability is a redundant stepper motor driving the output through a multistage reducing gearbox. The rotary actuator is powered by a high reliability space-rated stepper motor designed and constructed by Moog, Inc. The motor is a three-phase stepper motor of 15 degree step angle, producing twenty-four full steps per revolution. Since micro-stepping is not used in the design, and un-powered holding torque is exhibited at every commanded step, the rotary actuator is capable of reacting to torques as high as 35 Nm by holding position with the power off. The output is driven through a gear transmission having a total train ratio of 5120:1, resulting in a resolution of 0.003 degrees output rotation per motor step. The modular design of the multi-stage output transmission makes possible the addition of designs having different output parameters, such as lower torque and higher output speed capability. Some examples of an actuator family based on this growth capability will be presented in the paper.
Magnetic torque anomaly in the quantum limit of Weyl semimetals
Moll, Philip J. W.; Potter, Andrew C.; Nair, Nityan L.; Ramshaw, B. J.; Modic, K. A.; Riggs, Scott; Zeng, Bin; Ghimire, Nirmal J.; Bauer, Eric D.; Kealhofer, Robert; Ronning, Filip; Analytis, James G.
2016-01-01
Electrons in materials with linear dispersion behave as massless Weyl- or Dirac-quasiparticles, and continue to intrigue due to their close resemblance to elusive ultra-relativistic particles as well as their potential for future electronics. Yet the experimental signatures of Weyl-fermions are often subtle and indirect, in particular if they coexist with conventional, massive quasiparticles. Here we show a pronounced anomaly in the magnetic torque of the Weyl semimetal NbAs upon entering the quantum limit state in high magnetic fields. The torque changes sign in the quantum limit, signalling a reversal of the magnetic anisotropy that can be directly attributed to the topological nature of the Weyl electrons. Our results establish that anomalous quantum limit torque measurements provide a direct experimental method to identify and distinguish Weyl and Dirac systems. PMID:27545105
Installation Torque Tables for Noncritical Applications
Rivera-Rosario, Hazel T.; Powell, Joseph S.
2017-01-01
The objective of this project is to define torque values for bolts and screws when loading is not a concern. Fasteners require a certain torque to fulfill its function and prevent failure. NASA Glenn Research Center did not have a set of fastener torque tables for non-critical applications without loads, usually referring to hand-tight or wrench-tight torqueing. The project is based on two formulas, torque and pullout load. Torque values are calculated giving way to preliminary data tables. Testing is done to various bolts and metal plates, torqueing them until the point of failure. Around 640 torque tables were developed for UNC, UNF, and M fasteners. Different lengths of thread engagement were analyzed for the 5 most common materials used at GRC. The tables were put together in an Excel spreadsheet and then formatted into a Word document. The plan is to later convert this to an official technical publication or memorandum.
Optical torque on a magneto-dielectric Rayleigh absorptive sphere by a vector Bessel (vortex) beam
Li, Renxian; Yang, Ruiping; Ding, Chunying; Mitri, F. G.
2017-04-01
The optical torque exerted on an absorptive megneto-dielectric sphere by an axicon-generated vector Bessel (vortex) beam with selected polarizations is investigated in the framework of the dipole approximation. The total optical torque is expressed as the sum of orbital and spin torques. The axial orbital torque component is calculated from the z-component of the cross-product of the vector position r and the optical force exerted on the sphere F. Depending on the beam characteristics (such as the half-cone angle and polarization type) and the physical properties of the sphere, it is shown here that the axial orbital torque vanishes before reversing sign, indicating a counter-intuitive orbital motion in opposite handedness of the angular momentum carried by the incident waves. Moreover, analytical formulas for the spin torque, which is divided into spin torques induced by electric and magnetic dipoles, are derived. The corresponding components of both the optical spin and orbital torques are numerically calculated, and the effects of polarization, the order of the beam, and half-cone angle are discussed in detail. The left-handed (i.e., negative) optical torque is discussed, and the conditions for generating optical spin and orbital torque sign reversal are numerically investigated. The transverse optical spin torque has a vortex-like character, whose direction depends on the polarization, the half-cone angle, and the order of the beam. Numerical results also show that the vortex direction depends on the radial position of the particle in the transverse plane. This means that a sphere may rotate with different directions when it moves radially. Potential applications are in particle manipulation and rotation, single beam optical tweezers, and other emergent technologies using vector Bessel beams on a small magneto-dielectric (nano) particle.
14 CFR 29.361 - Engine torque.
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Engine torque. 29.361 Section 29.361... STANDARDS: TRANSPORT CATEGORY ROTORCRAFT Strength Requirements Flight Loads § 29.361 Engine torque. The limit engine torque may not be less than the following: (a) For turbine engines, the highest of— (1)...
Measuring the uncertainty of tapping torque
Belluco, Walter; De Chiffre, Leonardo
An uncertainty budget is carried out for torque measurements performed at the Institut for Procesteknik for the evaluation of cutting fluids. Thirty test blanks were machined with one tool and one fluid, torque diagrams were recorded and the repeatability of single torque measurements was estimat...
Calibration of the optical torque wrench
Pedaci, F.; Huang, Z.; Van Oene, M.; Dekker, N.H.
2012-01-01
The optical torque wrench is a laser trapping technique that expands the capability of standard optical tweezers to torque manipulation and measurement, using the laser linear polarization to orient tailored microscopic birefringent particles. The ability to measure torque of the order of kBT (∼4 pN
Research on the Electrostatic Torques in the MUM-ESG
无
2007-01-01
A research on the electrostatic torques in the electrostatically suspended gyroscope (ESG) that uses the mass-unbalance modulation (MUM) scheme (MUM-ESG) to sense the direction of the rotor's spinning axis is presented here. Spherical harmonic functions are used to describe the asphericity of the rotor and the electrodes. Rotational matrix theory in quantum mechanics is utilized to transform the description of the rotor's asphericity from the rotor-fixed frame into the cavity-fixed frame. And the principle of the virtual displacement is applied to find out the electrostatic torques acting on the rotor. Analytical expressions of the electrostatic torques indicate that there are only three kinds of factors need to be considered when looking for the electrostatic torques in the MUM-ESG. The factors are the asphericity of the rotor, the eccentric rotation of the rotor, and the coupling between the rotor's translational displacement and asphericity. An intuitionistic explanation for the electrostatic torque that comes of the rotor's eccentric rotation is presented also. The effects of these conclusions should be tested and verified in the future experiments and applications.
Spin Hall effect-driven spin torque in magnetic textures
Manchon, Aurelien
2011-07-13
Current-induced spin torque and magnetization dynamics in the presence of spin Hall effect in magnetic textures is studied theoretically. The local deviation of the charge current gives rise to a current-induced spin torque of the form (1 - ΒM) × [(u 0 + αH u 0 M) ∇] M, where u0 is the direction of the injected current, H is the Hall angle and is the non-adiabaticity parameter due to spin relaxation. Since αH and ×can have a comparable order of magnitude, we show that this torque can significantly modify the current-induced dynamics of both transverse and vortex walls. © 2011 American Institute of Physics.
Research on torsional capacity of composite drive shaft under clockwise and counter-clockwise torque
Yefa Hu
2015-04-01
Full Text Available The design of lay-up has a great influence on the mechanical properties of carbon fiber–reinforced plastic drive shaft. In this research, the stress states of each layer in the carbon fiber–reinforced plastic drive shaft were studied, which were different under opposite torque directions. The Tsai–Wu criterion was used to judge the torsional stability of the composite laminates. The data from finite element analysis showed that torsional capacities of a stacking sequence vary greatly with torque direction, and reasonable lay-up design can reduce the difference. Torque direction should not be ignored when designing a carbon fiber–reinforced plastic drive shaft.
Aspects Concerning the Torque Ripple Control of the Brushless DC Motor
BALUTA, G.
2013-05-01
Full Text Available This paper deals with two advanced numerical structures to control the electromagnetic torque ripple of Brushless Direct Current Motors (BLDCM, indirectly achieved by phase currents control and directly by the Direct Torque Control (DTC technique. In DTC there was implemented an observer to increase the rudimentary transducer resolution, containing three Hall Effect sensors. The experimental results describe the evolution of torque in both situations of control and are obtained by applying a control strategy for an electric drive system with BLDCM with trapezoidal Back-EMF in Two-Phase Mode.
徐艳平; 钟彦儒
2009-01-01
永磁同步电机传统直接转矩控制在电机低速运行时存在着转矩脉动大、开关频率不恒定等问题,本文在详细分析影响磁链和转矩脉动大小因素的基础上,提出了一种准确确定占空比大小的永磁同步电机新型直接转矩控制方法.该方法基于精确的数学模型利用转矩误差计算出当前所选有效电压矢量的作用时间在整个采样周期中的占空比,实时地调整有效电压矢量的作用时间.仿真和实验结果表明,基于占空比控制的永磁同步电机直接转矩控制在保持传统直接转矩控制优点的基础上,能够有效减小转矩脉动,改善了传统直接转矩控制系统性能.%Disadvantages of traditional direct torque control (DTC) systems for permanent magnet synchronous motors (PMSM) are high torque ripples and inconstant switching frequency at low speed. A novel DTC strategy of PMSM combined with exact duty ratio calculation is proposed based on the detailed analysis of effects factors of flux linkage and torque ripples. In this method the duty ratio between the action time of effective voltage vector and the sampling period is calculated according to torque errors based on exact mathematical models and the action time of active voltage vectors is adjusted. Simulation and experimental results show that the new DTC strategy based on duty ratio control has advantages of traditional DTC and effectively reduces torque ripples which improve the performance of traditional DTC.
Radiative torques: Analytical Model and Basic Properties
Lazarian, Alex
2007-01-01
We attempt to get a physical insight into grain alignment processes by studying basic properties of radiative torques (RATs). For this purpose we consider a simple toy model of a helical grain that reproduces well the basic features of RATs. The model grain consists of a spheroidal body with a mirror attached at an angle to it. Being very simple, the model allows analytical description of RATs that act upon it. We show a good correspondence of RATs obtained for this model and those of irregular grains calculated by DDSCAT. Our analysis of the role of different torque components for grain alignment reveals that one of the three RAT components does not affect the alignment, but induces only for grain precession. The other two components provide a generic alignment with grain long axes perpendicular to the radiation direction, if the radiation dominates the grain precession, and perpendicular to magnetic field, otherwise. We study a self-similar scaling of RATs as a function of $\\lambda/a_{eff}$. We show that th...
杨杰; 黄坤
2013-01-01
针对基于PI控制器的永磁同步电动机直接转矩控制系统存在转矩波动大、易受负载变化影响的问题,设计了一种基于转速外环的自抗扰控制器,代替PI控制器以改善永磁同步电动机直接转矩控制系统的性能；采用粒子群优化算法对自抗扰控制器的相关参数进行了优化计算,改进了控制器的调节性能.仿真和实验结果表明,基于参数优化自抗扰控制器的永磁同步电动机直接转矩控制系统具有较高的抗负载扰动能力,更快的响应速度和良好的动、静态性能.%In view of problems of big torque fluctuation and easy to be influenced by load change existed in direct torque control system of permanent magnet synchronous motor based on PI controller,the paper designed an active disturbance rejection controller based on speed loop to replace PI controller to improve performances of direct torque control system of permanent magnet synchronous motor.It used particle swarm optimizer algorithm to make optimization calculation for the parameters of the active disturbance rejection controller and improved adjustment performance of the controller.The simulation and experimental results show that the system based on the active disturbance rejection controller with parameters optimization has higher ability of anti-disturbance of load,faster response speed and good dynamic and static performance.
Hida, Hajime; Tomigashi, Yoshio; Ueyama, Kenji; Inoue, Yukinori; Morimoto, Shigeo
This paper proposes a new torque estimation method that takes into account the spatial harmonics of permanent magnet synchronous motors and that is capable of real-time estimation. First, the torque estimation equation of the proposed method is derived. In the method, the torque ripple of a motor can be estimated from the average of the torque calculated by the conventional method (cross product of the fluxlinkage and motor current) and the torque calculated from the electric input power to the motor. Next, the effectiveness of the proposed method is verified by simulations in which two kinds of motors with different components of torque ripple are considered. The simulation results show that the proposed method estimates the torque ripple more accurately than the conventional method. Further, the effectiveness of the proposed method is verified by performing on experiment. It is shown that the torque ripple is decreased by using the proposed method to the torque control.
Mello, Emanuele Moraes; Magalhães, Fernando Henrique; Kohn, André Fabio
2013-12-01
The present study examined the association between plantar flexion torque variability during isolated isometric contractions and during quiet bipedal standing. For plantar flexion torque measurements in quiet stance (QS), subjects stood still over a force plate. The mean plantar flexion torque level exerted by each subject in QS (divided by 2 to give the torque due to a single leg) served as the target torque level for right leg force-matching tasks in extended knee (KE) and flexed knee (KF) conditions. Muscle activation levels (EMG amplitudes) of the triceps surae and mean, standard deviation and coefficient of variation of plantar flexion torque were computed from signals acquired during periods with and without visual feedback. No significant correlations were found between EMG amplitudes and torque variability, regardless of the condition and muscle being analyzed. A significant correlation was found between torque variability in QS and KE, whereas no significant correlation was found between torque variability in QS and KF, regardless of vision availability. Therefore, torque variability measured in a controlled extended knee plantar flexion contraction is a predictor of torque variability in the anterior-posterior direction when the subjects are in quiet standing. In other words, larger plantar flexion torque variability in KE (but not in KF) implies less stable balance. The mechanisms underlying the findings above are probably associated with the similar proprioceptive feedback from the triceps surae in QS and KE and poorer proprioceptive feedback from the triceps surae in KF due to the slackening of the gastrocnemii. An additional putative mechanism includes the different torque contributions of each component of the triceps surae in the two knee angles. From a clinical and research standpoint, it would be advantageous to be able to estimate changes in balance ability by means of simple measurements of torque variability in a force matching task.
Das, Indrajit
2016-01-01
Collisions of gas particles with a drifting grain give rise to a mechanical torque on the grain. Recent work by Lazarian & Hoang showed that mechanical torques might play a significant role in aligning helical grains along the interstellar magnetic field direction, even in the case of subsonic drift. We compute the mechanical torques on 13 different irregular grains and examine their resulting rotational dynamics, assuming steady rotation about the principal axis of greatest moment of inertia. We find that the alignment efficiency in the subsonic drift regime depends sensitively on the grain shape, with more efficient alignment for shapes with a substantial mechanical torque even in the case of no drift. The alignment is typically more efficient for supersonic drift. A more rigorous analysis of the dynamics is required to definitively appraise the role of mechanical torques in grain alignment.
Estimation of torque transmitted by clutch during shifting process for dry dual clutch transmission
Zhao, Zhiguo; He, Lu; Yang, Yunyun; Wu, Chaochun; Li, Xueyan; Karl Hedrick, J.
2016-06-01
The key toward realizing no-impact gear shifting for dual clutch transmission (DCT) lies in the coordination control between the engine and dual clutches, as well as the accurate closed-loop control of torque transmitted by each clutch and the output torque of the engine. However, the implementation and control precision of closed-loop control are completely dependent on the effective measurement or estimation of the instant transmission torque of the clutch. This study analyzes the DCT shifting process, and builds a three-dimensional (3D) clutch model and mathematical model of a DCT vehicle powertrain system. The torque transmitted by a twin clutch during the upshifting process is estimated by applying the unscented Kalman filter (UKF) algorithm. Then, the torque estimation algorithm is verified using a DCT prototype vehicle installed with a torque sensor on the drive half-shaft. The experimental results show that the designed UKF torque estimation algorithm can estimate the transmission torques of two clutches in real time; further, it can be directly used for DCT shift control and improving the shifting quality.
Herter, Troy M; Kurtzer, Isaac; Cabel, D William; Haunts, Kirk A; Scott, Stephen H
2007-04-01
The present study examined neural activity in the shoulder/elbow region of primary motor cortex (M1) during a whole-limb postural task. By selectively imposing torques at the shoulder, elbow, or both joints we addressed how neurons represent changes in torque at a single joint, multiple joints, and their interrelation. We observed that similar proportions of neurons reflected changes in torque at the shoulder, elbow, and both joints and these neurons were highly intermingled across the cortical surface. Most torque-related neurons were reciprocally excited and inhibited (relative to their unloaded baseline activity) by opposing flexor and extensor torques at a single joint. Although coexcitation/coinhibition was occasionally observed at a single joint, it was rarely observed at both joints. A second analysis assessed the relationship between single-joint and multijoint activity. In contrast to our previous observations, we found that neither linear nor vector summation of single-joint activities could capture the breadth of neural responses to multijoint torques. Finally, we studied the neurons' directional tuning across all the torque conditions, i.e., in joint-torque space. Our population of M1 neurons exhibited a strong bimodal distribution of preferred-torque directions (PTDs) that was biased toward shoulder-extensor/elbow-flexor (whole-limb flexor) and shoulder-flexor/elbow-extensor (whole-limb extensor) torques. Notably, we recently observed a similar bimodal distribution of PTDs in a sample of proximal arm muscles. This observation illustrates the intimate relationship between M1 and the motor periphery.
Landau-Lifshitz theory of thermomagnonic torque
Kim, Se Kwon; Tserkovnyak, Yaroslav
2015-07-01
We derive the thermomagnonic torque associated with smooth magnetic textures subjected to a temperature gradient in the framework of the stochastic Landau-Lifshitz-Gilbert equation. Our approach captures on equal footing two distinct contributions: (i) a local entropic torque that is caused by a temperature dependence of the effective exchange field, the existence of which had been previously suggested based on numerics, and (ii) the well-known spin-transfer torque induced by thermally induced magnon flow. The dissipative components of two torques have the same structure, following a common phenomenology, but opposite signs, with the twice as large entropic torque leading to a domain-wall motion toward the hotter region. We compare the efficiency of the torque-driven domain-wall motion with the recently proposed Brownian thermophoresis.
Reaction torque minimization techniques for articulated payloads
Kral, Kevin; Aleman, Roberto M.
1988-01-01
Articulated payloads on spacecraft, such as antenna telemetry systems and robotic elements, impart reaction torques back into the vehicle which can significantly affect the performance of other payloads. This paper discusses ways to minimize the reaction torques of articulated payloads through command-shaping algorithms and unique control implementations. The effects of reaction torques encountered on Landsat are presented and compared with simulated and measured data of prototype systems employing these improvements.
Nishino, Atsuhiro; Ueda, Kazunaga; Fujii, Kenichi
2017-02-01
To allow the application of torque standards in various industries, we have been developing torque standard machines based on a lever deadweight system, i.e. a torque generation method using gravity. However, this method is not suitable for expanding the low end of the torque range, because of the limitations to the sizes of the weights and moment arms. In this study, the working principle of the torque generation method using an electromagnetic force was investigated by referring to watt balance experiments used for the redefinition of the kilogram. Applying this principle to a rotating coordinate system, an electromagnetic force type torque standard machine was designed and prototyped. It was experimentally demonstrated that SI-traceable torque could be generated by converting electrical power to mechanical power. Thus, for the first time, SI-traceable torque was successfully realized using a method other than that based on the force of gravity.
Evolution and Future of Torque Measurement Technology
Dr. W. Krimmel
2006-03-01
Full Text Available The journey to the past of torque measurement technology begins in the 17th century. It takes us from the first incipiencies of torque measurement to the problem of the transfer of the measurement signal from a rotating shaft, which existed for several decades. This task was solved by the integration of high-precise digital measuring amplifiers in the torque sensors, which is expressed by broad application fields, today. The future will appertain to highly dynamic measuring sensors as well as to intelligent torque sensors, which are able to transmit their sensor-specific characteristics to evaluation devices.
Spin-transfer torque generated by a topological insulator
Mellnik, A. R.
2014-07-23
Magnetic devices are a leading contender for the implementation of memory and logic technologies that are non-volatile, that can scale to high density and high speed, and that do not wear out. However, widespread application of magnetic memory and logic devices will require the development of efficient mechanisms for reorienting their magnetization using the least possible current and power. There has been considerable recent progress in this effort; in particular, it has been discovered that spin-orbit interactions in heavy-metal/ferromagnet bilayers can produce strong current-driven torques on the magnetic layer, via the spin Hall effect in the heavy metal or the Rashba-Edelstein effect in the ferromagnet. In the search for materials to provide even more efficient spin-orbit-induced torques, some proposals have suggested topological insulators, which possess a surface state in which the effects of spin-orbit coupling are maximal in the sense that an electron\\' s spin orientation is fixed relative to its propagation direction. Here we report experiments showing that charge current flowing in-plane in a thin film of the topological insulator bismuth selenide (Bi2Se3) at room temperature can indeed exert a strong spin-transfer torque on an adjacent ferromagnetic permalloy (Ni81Fe19) thin film, with a direction consistent with that expected from the topological surface state. We find that the strength of the torque per unit charge current density in Bi 2Se3 is greater than for any source of spin-transfer torque measured so far, even for non-ideal topological insulator films in which the surface states coexist with bulk conduction. Our data suggest that topological insulators could enable very efficient electrical manipulation of magnetic materials at room temperature, for memory and logic applications. © 2014 Macmillan Publishers Limited. All rights reserved.
Spin-transfer torque generated by a topological insulator.
Mellnik, A R; Lee, J S; Richardella, A; Grab, J L; Mintun, P J; Fischer, M H; Vaezi, A; Manchon, A; Kim, E-A; Samarth, N; Ralph, D C
2014-07-24
Magnetic devices are a leading contender for the implementation of memory and logic technologies that are non-volatile, that can scale to high density and high speed, and that do not wear out. However, widespread application of magnetic memory and logic devices will require the development of efficient mechanisms for reorienting their magnetization using the least possible current and power. There has been considerable recent progress in this effort; in particular, it has been discovered that spin-orbit interactions in heavy-metal/ferromagnet bilayers can produce strong current-driven torques on the magnetic layer, via the spin Hall effect in the heavy metal or the Rashba-Edelstein effect in the ferromagnet. In the search for materials to provide even more efficient spin-orbit-induced torques, some proposals have suggested topological insulators, which possess a surface state in which the effects of spin-orbit coupling are maximal in the sense that an electron's spin orientation is fixed relative to its propagation direction. Here we report experiments showing that charge current flowing in-plane in a thin film of the topological insulator bismuth selenide (Bi2Se3) at room temperature can indeed exert a strong spin-transfer torque on an adjacent ferromagnetic permalloy (Ni81Fe19) thin film, with a direction consistent with that expected from the topological surface state. We find that the strength of the torque per unit charge current density in Bi2Se3 is greater than for any source of spin-transfer torque measured so far, even for non-ideal topological insulator films in which the surface states coexist with bulk conduction. Our data suggest that topological insulators could enable very efficient electrical manipulation of magnetic materials at room temperature, for memory and logic applications.
Game programmer's guide to Torque under the hood of the Torque game engine
Maurina , Edward F
2006-01-01
game programmer working with the Torque game engine must have ""The Game Programmer's Guide To Torque"": it teaches everything needed to design your own game, using experiences of game makers and industry veterans well versed in Torque technology. A Torque Game engine demo is included on an accompanying cd while step-by-step examples tell how to use it. Its focus on all the basics makes for an exceptional coverage for all levels of game programmer. -Bookwatch, August 2006
Oberstrass, Florian C.; Fernandes, Louis E.; Lebel, Paul; Bryant, Zev
2013-01-01
Changes in global DNA linking number can be accommodated by localized changes in helical structure. We have used single-molecule torque measurements to investigate sequence-specific strand separation and Z-DNA formation. By controlling the boundary conditions at the edges of sequences of interest, we have confirmed theoretical predictions of distinctive boundary-dependent backbending patterns in torque-twist relationships. Abrupt torque jumps are associated with the formation and collapse of DNA bubbles, permitting direct observations of DNA breathing dynamics. PMID:23679785
Reduction of torque ripple in DTC induction motor drive with discrete voltage vectors
Rosić Marko
2014-01-01
Full Text Available This paper presents а practical implementation of direct torque control (DTC of an induction machine on MSK2812 DSP platform, and the analysis of possibilities for reduction of torque ripple. Basic theoretical background relating the DTC was primarily set and the obtained experimental results have been given. It is shown that the torque ripple can be reduced by adjusting the intensity of voltage vectors and by modification of hysteresis comparator, while the simplicity of the basic DTC algorithm has been maintained. [Projekat Ministarstva nauke Republike Srbije, br. TR33016
Remote control canard missile with a free-rolling tail brake torque system
Blair, A. B., Jr.
1981-01-01
An experimental wind-tunnel investigation has been conducted at supersonic Mach numbers to determine the static aerodynamic characteristics of a cruciform canard-controlled missile with fixed and free-rolling tail-fin afterbodies. Mechanical coupling effects of the free-rolling tail afterbody were investigated using an electronic/electromagnetic brake system that provides arbitrary tail-fin brake torques with continuous measurements of tail-to-mainframe torque and tail-roll rate. Results are summarized to show the effects of fixed and free-rolling tail-fin afterbodies that include simulated measured bearing friction torques on the longitudinal and lateral-directional aerodynamic characteristics.
The Casimir Torque on a Cylindrical Gear
Vaidya, Varun
2013-01-01
We utilize Effective Field Theory(EFT) techniques to calculate the casimir torque on a cylindrical gear in the presence of a polarizable but neutral object. We present results for the energy and torque as a function of angle for a gear with multiple cogs, as well as for the case of a concentric cylindrical gear.