Reachable set estimation for Takagi-Sugeno fuzzy systems against unknown output delays with application to tracking control of AUVs.

Science.gov (United States)

Zhong, Zhixiong; Zhu, Yanzheng; Ahn, Choon Ki

2018-03-20

In this paper, we address the problem of reachable set estimation for continuous-time Takagi-Sugeno (T-S) fuzzy systems subject to unknown output delays. Based on the reachable set concept, a new controller design method is also discussed for such systems. An effective method is developed to attenuate the negative impact from the unknown output delays, which likely degrade the performance/stability of systems. First, an augmented fuzzy observer is proposed to capacitate a synchronous estimation for the system state and the disturbance term owing to the unknown output delays, which ensures that the reachable set of the estimation error is limited via the intersection operation of ellipsoids. Then, a compensation technique is employed to eliminate the influence on the system performance stemmed from the unknown output delays. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of autonomous underwater vehicles. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Estabilidad de sistemas Takagi-Sugeno bajo perturbaciones persistentes: estimación de conjuntos inescapables

Directory of Open Access Journals (Sweden)

J.L. Pitarch

2015-10-01

Full Text Available Resumen: El presente trabajo analiza el comportamiento de sistemas borrosos Takagi-Sugeno ante perturbaciones persistentes (caracterizadas bien por cotas conocidas de amplitud o de potencia en media cuadrática. El análisis se centra en validar que, ante una determinada cota de potencia de perturbaciones y región de condiciones iniciales, existe una región inescapable (contenida en la región donde el modelo TS es válido como modelo de un sistema no lineal subyacente. Algunos de los problemas planteados se formulan como problemas de desigualdades matriciales lineales (LMI, posibles de resolver de forma óptima por programación semidefinida, y otros serán productos de matrices variables de decisión y dos escalares (BMI, que son resueltos de forma iterativa. Abstract: The present work analizes the behaviour of Takagi-Sugeno fuzzy systems in front of non-vanishing disturbances (characterized by known amplitude or quadratic-mean power bounds. Such analysis is focused in validating that, in front of a specific disturbance bound and an initial-condition region, there exist an inescapable region (contained in the region where the TS model is valid as a model of the underlying nonlinear system. Some of the stated problems here are cast as linear matrix inequality problems (LMI, efficiently solvable by semidefinite programming. Others, however, will involve nonconvex products of decision-variable matrices and two scalars (BMI, which are solved in an iterative way. Palabras clave: Takagi-Sugeno, Rechazo a perturbaciones, Conjunto inescapable, Estabilidad local, LMI, Perturbaciones persistentes., Keywords: Takagi-Sugeno, Disturbance rejection, Inescapable set, Local stability, LMI, Nonvanishing disturbances.

Advanced Takagi‒Sugeno fuzzy systems delay and saturation

CERN Document Server

Benzaouia, Abdellah

2014-01-01

This monograph puts the reader in touch with a decade’s worth of new developments in the field of fuzzy control specifically those of the popular Takagi-Sugeno (T-S) type. New techniques for stabilizing control analysis and design based on multiple Lyapunov functions and linear matrix inequalities (LMIs), are proposed. All the results are illustrated with numerical examples and figures and a rich bibliography is provided for further investigation. Control saturations are taken into account within the fuzzy model. The concept of positive invariance is used to obtain sufficient asymptotic stability conditions for the fuzzy system with constrained control inside a subset of the state space. The authors also consider the non-negativity of the states. This is of practical importance in many chemical, physical and biological processes that involve quantities that have intrinsically constant and non-negative sign: concentration of substances, level of liquids, etc. Results for linear systems are then extended to l...

Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

Directory of Open Access Journals (Sweden)

L. C. Félix-Herrán

2015-01-01

Full Text Available This work presents a novel semiactive model of a one-half lateral vehicle suspension. The contribution of this research is the inclusion of actuator dynamics (two magnetorheological nonlinear dampers in the modelling, which means that more realistic outcomes will be obtained, because, in real life, actuators have physical limitations. Takagi-Sugeno (T-S fuzzy approach is applied to a four-degree-of-freedom (4-DOF lateral one-half vehicle suspension. The system has two magnetorheological (MR dampers, whose numerical values come from a real characterization. T-S allows handling suspension’s components and actuator’s nonlinearities (hysteresis, saturation, and viscoplasticity by means of a set of linear subsystems interconnected via fuzzy membership functions. Due to their linearity, each subsystem can be handled with the very well-known control theory, for example, stability and performance indexes (this is an advantage of the T-S approach. To the best of authors’ knowledge, reported work does not include the aforementioned nonlinearities in the modelling. The generated model is validated via a case of study with simulation results. This research is paramount because it introduces a more accurate (the actuator dynamics, a complex nonlinear subsystem model that could be applied to one-half vehicle suspension control purposes. Suspension systems are extremely important for passenger comfort and stability in ground vehicles.

Dynamic Takagi-Sugeno Model for the Control of Ultrasonic Motor

Directory of Open Access Journals (Sweden)

Shi Jingzhuo

2011-01-01

Full Text Available Model of ultrasonic motor is the foundation of the design of ultrasonic motor's speed and position controller. A two-input and one-output dynamic Takagi-Sugeno model of ultrasonic motor driving system is worked out using fuzzy reasoning modeling method in this paper. Many fuzzy reasoning modeling methods are sensitive to the initial values and easy to fall into local minimum, and have a large amount of calculation. In order to overcome these defects, equalized universe method is used in this paper to get clusters centers and obtain fuzzy clustering membership functions, and then, the unknown parameters of the conclusions of fuzzy rules are identified using least-square method. Different experimental data that are tested with different operational conditions are used to examine the validity of the fuzzy model. Comparison between experimental data and calculated data of the model indicates that the model can well describe the nonlinear characteristics among the frequency, amplitude of driving voltage and rotating speed. The proposed fuzzy model can be used to analyze the performance of ultrasonic motor driving system, and also can be used to design the speed and position controller of ultrasonic motor.

Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model

DEFF Research Database (Denmark)

Khooban, Mohammad-Hassan; Vafamand, Navid; Niknam, Taher

2017-01-01

Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non......-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison...... is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation...

Wind Turbine Model and Observer in Takagi-Sugeno Model Structure

International Nuclear Information System (INIS)

Georg, Sören; Müller, Matthias; Schulte, Horst

2014-01-01

Based on a reduced-order, dynamic nonlinear wind turbine model in Takagi- Sugeno (TS) model structure, a TS state observer is designed as a disturbance observer to estimate the unknown effective wind speed. The TS observer model is an exact representation of the underlying nonlinear model, obtained by means of the sector-nonlinearity approach. The observer gain matrices are obtained by means of a linear matrix inequality (LMI) design approach for optimal fuzzy control, where weighting matrices for the individual system states and outputs are included. The observer is tested in simulations with the aero-elastic code FAST for the NREL 5 MW reference turbine, where it shows a stable behaviour in turbulent wind simulations

A Novel Finite-Sum Inequality-Based Method for Robust H∞ Control of Uncertain Discrete-Time Takagi-Sugeno Fuzzy Systems With Interval-Like Time-Varying Delays.

Science.gov (United States)

Zhang, Xian-Ming; Han, Qing-Long; Ge, Xiaohua

2017-09-22

This paper is concerned with the problem of robust H∞ control of an uncertain discrete-time Takagi-Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust H∞ fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.

Effective wind speed estimation: Comparison between Kalman Filter and Takagi-Sugeno observer techniques.

Science.gov (United States)

Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst

2016-05-01

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Fouling detection in heat exchangers by Takagi-Sugeno observers

International Nuclear Information System (INIS)

Delrot, Sabrina

2012-01-01

The phenomenon of fouling in heat exchangers is currently an important topic. Indeed, the fouling is a costly issue that increases the energy loss (directly or indirectly through an over-sizing of the equipment), and therefore increases the water consumption. As a side effect, fouling increases CO 2 consumption that leads to environmental consequences. Fouling can be detected either on local scale, using expensive and specific sensors or on global scale. Global estimation of fouling can be done by measuring the variation of the mass of the exchanger, or by estimating the efficiency of the exchanger through the transfer coefficient. These two methods require very restricting conditions: a powered exchanger to measure mass variation and a steady state exchanger to estimate the efficiency. The work introduced in this thesis deals with the development of non-linear observers that detect fouling early enough to start an efficient cleaning process. As a beginning, a finite element model of a counter current tubular exchanger was proposed. Then three approaches, based on non-linear Takagi-Sugeno observers, were suggested to detect early fouling in heat exchangers. First approach consisted in a set of observers that estimated the parameters of fouling effect through an interpolation method. The second approach proposed a polynomial Takagi-Sugeno observer, using the theory of sums of squares. Finally, a observer of Takagi-Sugeno type with unknown inputs was developed. As a conclusion, a comparison between those different methods was done. (author)

Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

Science.gov (United States)

Sivaraos; Khalim, A. Z.; Salleh, M. S.; Sivakumar, D.; Kadirgama, K.

2018-03-01

Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process.

Disturbance observer based Takagi-Sugeno fuzzy control for an active seat suspension

Science.gov (United States)

Ning, Donghong; Sun, Shuaishuai; Zhang, Fei; Du, Haiping; Li, Weihua; Zhang, Bangji

2017-09-01

In this paper, a disturbance observer based Takagi-Sugeno (TS) fuzzy controller is proposed for an active seat suspension; both simulations and experiments have been performed verifying the performance enhancement and stability of the proposed controller. The controller incorporates closed-loop feedback control using the measured acceleration of the seat and deflection of the suspension; these two variables can be easily measured in practical applications, thus allowing the proposed controller to be robust and adaptable. A disturbance observer that can estimate the disturbance caused by friction, model simplification, and controller output error has also been used to compensate a H∞ state feedback controller. The TS fuzzy control method is applied to enhance the controller's performance by considering the variation of driver's weight during operation. The vibration of a heavy duty vehicle seat is largest in the frequency range between 2 Hz and 4 Hz, in the vertical direction; therefore, it is reasonable to focus on controlling low frequency vibration amplitudes and maintain the seat suspensions passivity at high frequency. Moreover, both the simulation and experimental results show that the active seat suspension with the proposed controller can effectively isolate unwanted vibration amplitudes below 4.5 Hz, when compared with a well-tuned passive seat suspension. The active controller has been further validated under bump and random road tests with both a 55 kg and a 70 kg loads. The bump road test demonstrated the controller has good transient response capabilities. The random road test result has been presented both in the time domain and the frequency domain. When with the above two loads, the controlled seat suspensions root-mean-square (RMS) accelerations were reduced by 45.5% and 49.5%, respectively, compared with a well-tuned passive seat suspension. The proposed active seat suspension controller has great potential and is very practical for application

LMI-based stability and performance conditions for continuous-time nonlinear systems in Takagi-Sugeno's form.

Science.gov (United States)

Lam, H K; Leung, Frank H F

2007-10-01

This correspondence presents the stability analysis and performance design of the continuous-time fuzzy-model-based control systems. The idea of the nonparallel-distributed-compensation (non-PDC) control laws is extended to the continuous-time fuzzy-model-based control systems. A nonlinear controller with non-PDC control laws is proposed to stabilize the continuous-time nonlinear systems in Takagi-Sugeno's form. To produce the stability-analysis result, a parameter-dependent Lyapunov function (PDLF) is employed. However, two difficulties are usually encountered: 1) the time-derivative terms produced by the PDLF will complicate the stability analysis and 2) the stability conditions are not in the form of linear-matrix inequalities (LMIs) that aid the design of feedback gains. To tackle the first difficulty, the time-derivative terms are represented by some weighted-sum terms in some existing approaches, which will increase the number of stability conditions significantly. In view of the second difficulty, some positive-definitive terms are added in order to cast the stability conditions into LMIs. In this correspondence, the favorable properties of the membership functions and nonlinear control laws, which allow the introduction of some free matrices, are employed to alleviate the two difficulties while retaining the favorable properties of PDLF-based approach. LMI-based stability conditions are derived to ensure the system stability. Furthermore, based on a common scalar performance index, LMI-based performance conditions are derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.

Advanced embedded nonlinear observer design and HIL validation using a Takagi-Sugeno approach with unmeasurable premise variables

International Nuclear Information System (INIS)

Olteanu, S C; Belkoura, L; Aitouche, A

2014-01-01

The article's goals are to illustrate the feasibility of implementing a Takagi Sugeno state observer on an embedded microcontroller based platform and secondly to present a methodology for validating a physical embedded system using a Hardware In The Loop architecture, where a simulation software replaces the process. As an application, a three water tank system was chosen. For the validation part, LMS AMESim software is employed to reproduce the process behaviour. The interface to the embedded platform is assured by Simulink on a Windows operating system, chosen as it is the most commonly used operating system. The lack of real time behaviour of the operating system is compensated by a real time kernel that manages to offer deterministic response times. The Takagi-Sugeno observer in the case of this process has the complex form that considers the premise variables to be unmeasurable. The embedded system consists of two Arduino boards connected in parallel, thus offering distributed resources

Advanced embedded nonlinear observer design and HIL validation using a Takagi-Sugeno approach with unmeasurable premise variables

Energy Technology Data Exchange (ETDEWEB)

Olteanu, S C; Belkoura, L [University of Lille 1 (France); Aitouche, A [HEI Lille (France)

2014-12-16

The article's goals are to illustrate the feasibility of implementing a Takagi Sugeno state observer on an embedded microcontroller based platform and secondly to present a methodology for validating a physical embedded system using a Hardware In The Loop architecture, where a simulation software replaces the process. As an application, a three water tank system was chosen. For the validation part, LMS AMESim software is employed to reproduce the process behaviour. The interface to the embedded platform is assured by Simulink on a Windows operating system, chosen as it is the most commonly used operating system. The lack of real time behaviour of the operating system is compensated by a real time kernel that manages to offer deterministic response times. The Takagi-Sugeno observer in the case of this process has the complex form that considers the premise variables to be unmeasurable. The embedded system consists of two Arduino boards connected in parallel, thus offering distributed resources.

Constructing compact Takagi-Sugeno rule systems: identification of complex interactions in epidemiological data.

Science.gov (United States)

Zhou, Shang-Ming; Lyons, Ronan A; Brophy, Sinead; Gravenor, Mike B

2012-01-01

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

Fuzzy stability and synchronization of hyperchaos systems

International Nuclear Information System (INIS)

Wang Junwei; Xiong Xiaohua; Zhao Meichun; Zhang Yanbin

2008-01-01

This paper studies stability and synchronization of hyperchaos systems via a fuzzy-model-based control design methodology. First, we utilize a Takagi-Sugeno fuzzy model to represent a hyperchaos system. Second, we design fuzzy-model-based controllers for stability and synchronization of the system, based on so-called 'parallel distributed compensation (PDC)'. Third, we reduce a question of stabilizing and synchronizing hyperchaos systems to linear matrix inequalities (LMI) so that convex programming techniques can solve these LMIs efficiently. Finally, the generalized Lorenz hyperchaos system is employed to illustrate the effectiveness of our designing controller

Design of a stable fuzzy controller for an articulated vehicle.

Science.gov (United States)

Tanaka, K; Kosaki, T

1997-01-01

This paper presents a backward movement control of an articulated vehicle via a model-based fuzzy control technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzy controller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach. The stability conditions are characterized in terms of linear matrix inequalities since the stability analysis is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Simulation results and experimental results show that the designed fuzzy controller effectively achieves the backward movement control of the articulated vehicle.

Fuzzy model-based adaptive synchronization of time-delayed chaotic systems

International Nuclear Information System (INIS)

Vasegh, Nastaran; Majd, Vahid Johari

2009-01-01

In this paper, fuzzy model-based synchronization of a class of first order chaotic systems described by delayed-differential equations is addressed. To design the fuzzy controller, the chaotic system is modeled by Takagi-Sugeno fuzzy system considering the properties of the nonlinear part of the system. Assuming that the parameters of the chaotic system are unknown, an adaptive law is derived to estimate these unknown parameters, and the stability of error dynamics is guaranteed by Lyapunov theory. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach.

A Takagi-Sugeno fuzzy power-distribution method for a prototypical advanced reactor considering pump degradation

Energy Technology Data Exchange (ETDEWEB)

Yuan, Yue [Institute of Nuclear and New Energy Technology, Tsinghua University, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Beijing (China); Coble, Jamie [Dept. of Nuclear Engineering, University of Tennessee, Knoxville (United States)

2017-08-15

Advanced reactor designs often feature longer operating cycles between refueling and new concepts of operation beyond traditional baseload electricity production. Owing to this increased complexity, traditional proportional–integral control may not be sufficient across all potential operating regimes. The prototypical advanced reactor (PAR) design features two independent reactor modules, each connected to a single dedicated steam generator that feeds a common balance of plant for electricity generation and process heat applications. In the current research, the PAR is expected to operate in a load-following manner to produce electricity to meet grid demand over a 24-hour period. Over the operational lifetime of the PAR system, primary and intermediate sodium pumps are expected to degrade in performance. The independent operation of the two reactor modules in the PAR may allow the system to continue operating under degraded pump performance by shifting the power production between reactor modules in order to meet overall load demands. This paper proposes a Takagi–Sugeno (T–S) fuzzy logic-based power distribution system. Two T–S fuzzy power distribution controllers have been designed and tested. Simulation shows that the devised T–S fuzzy controllers provide improved performance over traditional controls during daily load-following operation under different levels of pump degradation.

Neuro-fuzzy system modeling based on automatic fuzzy clustering

Institute of Scientific and Technical Information of China (English)

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.

Sensor Fault Diagnosis Observer for an Electric Vehicle Modeled as a Takagi-Sugeno System

Directory of Open Access Journals (Sweden)

S. Gómez-Peñate

2018-01-01

Full Text Available A sensor fault diagnosis of an electric vehicle (EV modeled as a Takagi-Sugeno (TS system is proposed. The proposed TS model considers the nonlinearity of the longitudinal velocity of the vehicle and parametric variation induced by the slope of the road; these considerations allow to obtain a mathematical model that represents the vehicle for a wide range of speeds and different terrain conditions. First, a virtual sensor represented by a TS state observer is developed. Sufficient conditions are given by a set of linear matrix inequalities (LMIs that guarantee asymptotic convergence of the TS observer. Second, the work is extended to perform fault detection and isolation based on a generalized observer scheme (GOS. Numerical simulations are presented to show the performance and applicability of the proposed method.

Adaptive Fuzzy Logic based MPPT Control for PV System Under Partial Shading Condition

OpenAIRE

Choudhury, Subhashree; Rout, Pravat Kumar

2016-01-01

Partial shading causes power loss, hotspots and threatens the reliability of the Photovoltaic generation system. Moreover characteristic curves exhibit multiple peaks. Conventional MPPT techniques under this condition often fail to give optimum MPP. Focusing on the afore mentioned problem an attempt has been made to design an Adaptive Takagi-Sugeno Fuzzy Inference System based Fuzzy Logic Control MPPT.The mathematical model of PV array is simulated using in MATLAB/Simulink environment.Various...

Fuzzy control and identification

CERN Document Server

Lilly, John H

2010-01-01

This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.

Fuzzy model-based servo and model following control for nonlinear systems.

Science.gov (United States)

Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

2009-12-01

This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

Contributions to fuzzy polynomial techniques for stability analysis and control

OpenAIRE

Pitarch Pérez, José Luis

2014-01-01

The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees...

Less Conservative ℋ∞ Fuzzy Control for Discrete-Time Takagi-Sugeno Systems

Directory of Open Access Journals (Sweden)

Leonardo Amaral Mozelli

2011-01-01

Full Text Available New analysis and control design conditions of discrete-time fuzzy systems are proposed. Using fuzzy Lyapunov's functions and introducing slack variables, less conservative conditions are obtained. The controller guarantees system stabilization and ℋ∞ performance. Numerical tests and a practical experiment in Chua's circuit are presented to show the effectiveness.

Financial Markets Analysis by Probabilistic Fuzzy Modelling

NARCIS (Netherlands)

J.H. van den Berg (Jan); W.-M. van den Bergh (Willem-Max); U. Kaymak (Uzay)

2003-01-01

textabstractFor successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno

Financial markets analysis by probabilistic fuzzy modelling

NARCIS (Netherlands)

Berg, van den J.; Kaymak, U.; Bergh, van den W.M.

2003-01-01

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS)

Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties.

Science.gov (United States)

Lam, H K; Leung, Frank H F

2005-04-01

This paper presents a fuzzy controller, which involves a fuzzy combination of local fuzzy and global switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties with known bounds. The nonlinear system is represented by a fuzzy combined Takagi-Sugeno-Kang model, which is a fuzzy combination of the global and local fuzzy plant models. By combining the local fuzzy and global switching state-feedback controllers using fuzzy logic techniques, the advantages of both controllers can be retained and the undesirable chattering effect introduced by the global switching state-feedback controller can be eliminated. The steady-state error introduced by the global switching state-feedback controller when a saturation function is used can also be removed. Stability conditions, which are related to the system matrices of the local and global closed-loop systems, are derived to guarantee the closed-loop system stability. An application example will be given to demonstrate the merits of the proposed approach.

Dissipativity-Based Reliable Control for Fuzzy Markov Jump Systems With Actuator Faults.

Science.gov (United States)

Tao, Jie; Lu, Renquan; Shi, Peng; Su, Hongye; Wu, Zheng-Guang

2017-09-01

This paper is concerned with the problem of reliable dissipative control for Takagi-Sugeno fuzzy systems with Markov jumping parameters. Considering the influence of actuator faults, a sufficient condition is developed to ensure that the resultant closed-loop system is stochastically stable and strictly ( Q, S,R )-dissipative based on a relaxed approach in which mode-dependent and fuzzy-basis-dependent Lyapunov functions are employed. Then a reliable dissipative control for fuzzy Markov jump systems is designed, with sufficient condition proposed for the existence of guaranteed stability and dissipativity controller. The effectiveness and potential of the obtained design method is verified by two simulation examples.

Robust H(infinity) tracking control of boiler-turbine systems.

Science.gov (United States)

Wu, J; Nguang, S K; Shen, J; Liu, G; Li, Y G

2010-07-01

In this paper, the problem of designing a fuzzy H(infinity) state feedback tracking control of a boiler-turbine is solved. First, the Takagi and Sugeno fuzzy model is used to model a boiler-turbine system. Next, based on the Takagi and Sugeno fuzzy model, sufficient conditions for the existence of a fuzzy H(infinity) nonlinear state feedback tracking control are derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that it does not involve feedback linearization technique and complicated adaptive scheme. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearized approach. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive fuzzy observer based synchronization design and secure communications of chaotic systems

International Nuclear Information System (INIS)

Hyun, Chang-Ho; Kim, Jae-Hun; Kim, Euntai; Park, Mignon

2006-01-01

This paper proposes a synchronization design scheme based on an alternative indirect adaptive fuzzy observer and its application to secure communication of chaotic systems. It is assumed that their states are unmeasurable and their parameters are unknown. Chaotic systems and the structure of the fuzzy observer are represented by the Takagi-Sugeno fuzzy model. Using Lyapunov stability theory, an adaptive law is derived to estimate the unknown parameters and the stability of the proposed system is guaranteed. Through this process, the asymptotic synchronization of chaotic systems is achieved. The proposed observer is applied to secure communications of chaotic systems and some numerical simulation results show the validity of theoretical derivations and the performance of the proposed observer

Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions

OpenAIRE

Bernal Reza, Miguel Ángel; Sala, Antonio; JAADARI, ABDELHAFIDH; Guerra, Thierry-Marie

2011-01-01

In this paper, the stability of continuous-time polynomial fuzzy models by means of a polynomial generalization of fuzzy Lyapunov functions is studied. Fuzzy Lyapunov functions have been fruitfully used in the literature for local analysis of Takagi-Sugeno models, a particular class of the polynomial fuzzy ones. Based on a recent Taylor-series approach which allows a polynomial fuzzy model to exactly represent a nonlinear model in a compact set of the state space, it is shown that a refinemen...

Método de Agrupamiento en Línea para la Identificación de Modelos Borrosos Takagi-Sugeno

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Boris Martínez

2008-07-01

Full Text Available Resumen: En este trabajo se presenta un método de obtención de modelos borrosos Takagi-Sugeno. Este método actualiza en línea tanto la estructura como los parámetros del modelo mediante la combinación de un nuevo algoritmo de agrupamiento en línea con técnicas de mínimos cuadrados. El algoritmo de agrupamiento propuesto se utiliza para la identificación de la estructura del modelo borroso, generando las clases de las cuales se obtienen los antecedentes de las reglas. La actualización de los parámetros del consecuente se logra mediante estimadores de mínimos cuadrados. Palabras clave: identificación en línea, modelo borroso Takagi–Sugeno, agrupamiento en línea, adaptación del modelo

Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy Inference

Science.gov (United States)

Walden, Maria A.; Bikdash, Marwan; Homaifar, Abdollah

1998-01-01

Finding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.

Periodicity of a class of nonlinear fuzzy systems with delays

International Nuclear Information System (INIS)

Yu Jiali; Yi Zhang; Zhang Lei

2009-01-01

The well known Takagi-Sugeno (T-S) model gives an effective method to combine some simple local systems with their linguistic description to represent complex nonlinear dynamic systems. By using the T-S method, a class of local nonlinear systems having nice dynamic properties can be employed to represent some global complex nonlinear systems. This paper proposes to study the periodicity of a class of global nonlinear fuzzy systems with delays by using T-S method. Conditions for guaranteeing periodicity are derived. Examples are employed to illustrate the theory.

An Application of Fuzzy Inference System by Clustering Subtractive Fuzzy Method for Estimating of Product Requirement

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Fajar Ibnu Tufeil

2009-06-01

Full Text Available Model fuzzy memiliki kemampuan untuk menjelaskan secara linguistik suatu sistem yang terlalu kompleks. Aturan-aturan dalam model fuzzy pada umumnya dibangun berdasarkan keahlian manusia dan pengetahuan heuristik dari sistem yang dimodelkan. Teknik ini selanjutnya dikembangkan menjadi teknik yang dapat mengidentifikasi aturan-aturan dari suatu basis data yang telah dikelompokkan berdasarkan persamaan strukturnya. Dalam hal ini metode pengelompokan fuzzy berfungsi untuk mencari kelompok-kelompok data. Informasi yang dihasilkan dari metode pengelompokan ini, yaitu informasi tentang pusat kelompok, digunakan untuk membentuk aturan-aturan dalam sistem penalaran fuzzy. Dalam skripsi ini dibahas mengenai penerapan fuzzy infereance system dengan metode pengelompokan fuzzy subtractive clustering, yaitu untuk membentuk sistem penalaran fuzzy dengan menggunakan model fuzzy Takagi-Sugeno orde satu. Selanjutnya, metode pengelompokan fuzzy subtractive clustering diterapkan dalam memodelkan masalah dibidang pemasaran, yaitu untuk memprediksi permintaan pasar terhadap suatu produk susu. Aplikasi ini dibangun menggunakan Borland Delphi 6.0. Dari hasil pengujian diperoleh tingkat error prediksi terkecil yaitu dengan Error Average 0.08%.

Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

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Hamdy, M; Hamdan, I

2015-07-01

In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Model dynamic behaviour analysis with chaotic noise using fuzzy logic based control

International Nuclear Information System (INIS)

Silva, Glauco Antonio Santos da

2002-01-01

This work presents an application of fuzzy control on dynamical system models. It has been observed that fuzzy controllers maybe used as a good alternative to the classical PI controller, once it incorporates human line behavior. Three implication relationships were used for the fussy controllers, namely, Mamdani Min, Larsen and Takagi-Sugeno. Performance comparisons were made aiming at achieving the best performance for each model used. The PI controller was used as a minimum standard, once it has been present in the industry for many years, giving acceptable performances and some degree of reliability . Two kinds of perturbations were introduced in the models to test the controllers: a ramp and chaotic perturbations. The first one is a monotonic, standard increase of an input parameter. The second one presents non-periodicity and irregularity in such a way to be quite rough to the controllers. The chaotic signal, as an analysis tool to dynamical systems, is an interesting contribution of this work. As a general conclusion it can be said the best performance, in this work, was achieved by the Takagi-Sugeno fuzzy controller. (author)

Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS

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Jani Kusanti

2015-07-01

Abstract             The use of Adaptive Neuro Fuzzy Inference System (ANFIS methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS. Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66%   Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

Impulsive synchronization for Takagi-Sugeno fuzzy model and its application to continuous chaotic system

International Nuclear Information System (INIS)

Wang Yanwu; Guan Zhihong; Wang, Hua O.

2005-01-01

Recently, chaos synchronization based on T-S fuzzy model has attracted much attention because of the applicability in the case of uncertainty. In the fuzzy control scheme, linear and adaptive control methods have been introduced to solve the control problem. In this Letter, an impulsive synchronization scheme for T-S fuzzy model is developed. The proposed impulsive control scheme seems to have a simple control structure and may need less control energy than the normal continuous ones for the synchronization of T-S fuzzy system. Sufficient conditions for the impulsive synchronization are derived. The method is illustrated by applications to continuous chaotic systems and the simulation results demonstrate the effectiveness of the proposed control method

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

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Jinjun Tang

Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

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Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Fuzzy Stabilization for Nonlinear Discrete Ship Steering Stochastic Systems Subject to State Variance and Passivity Constraints

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Wen-Jer Chang

2014-01-01

Full Text Available For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.

Local Model Predictive Control for T-S Fuzzy Systems.

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Lee, Donghwan; Hu, Jianghai

2017-09-01

In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.

Fuzzy Control Model and Simulation for Nonlinear Supply Chain System with Lead Times

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Songtao Zhang

2017-01-01

Full Text Available A new fuzzy robust control strategy for the nonlinear supply chain system in the presence of lead times is proposed. Based on Takagi-Sugeno fuzzy control system, the fuzzy control model of the nonlinear supply chain system with lead times is constructed. Additionally, we design a fuzzy robust H∞ control strategy taking the definition of maximal overlapped-rules group into consideration to restrain the impacts such as those caused by lead times, switching actions among submodels, and customers’ stochastic demands. This control strategy can not only guarantee that the nonlinear supply chain system is robustly asymptotically stable but also realize soft switching among subsystems of the nonlinear supply chain to make the less fluctuation of the system variables by introducing the membership function of fuzzy system. The comparisons between the proposed fuzzy robust H∞ control strategy and the robust H∞ control strategy are finally illustrated through numerical simulations on a two-stage nonlinear supply chain with lead times.

TS Fuzzy Model-Based Controller Design for a Class of Nonlinear Systems Including Nonsmooth Functions

DEFF Research Database (Denmark)

Vafamand, Navid; Asemani, Mohammad Hassan; Khayatiyan, Alireza

2018-01-01

This paper proposes a novel robust controller design for a class of nonlinear systems including hard nonlinearity functions. The proposed approach is based on Takagi-Sugeno (TS) fuzzy modeling, nonquadratic Lyapunov function, and nonparallel distributed compensation scheme. In this paper, a novel...... criterion, new robust controller design conditions in terms of linear matrix inequalities are derived. Three practical case studies, electric power steering system, a helicopter model and servo-mechanical system, are presented to demonstrate the importance of such class of nonlinear systems comprising...

Fuzzy modeling and control of rotary inverted pendulum system using LQR technique

International Nuclear Information System (INIS)

Fairus, M A; Mohamed, Z; Ahmad, M N

2013-01-01

Rotary inverted pendulum (RIP) system is a nonlinear, non-minimum phase, unstable and underactuated system. Controlling such system can be a challenge and is considered a benchmark in control theory problem. Prior to designing a controller, equations that represent the behaviour of the RIP system must be developed as accurately as possible without compromising the complexity of the equations. Through Takagi-Sugeno (T-S) fuzzy modeling technique, the nonlinear system model is then transformed into several local linear time-invariant models which are then blended together to reproduce, or approximate, the nonlinear system model within local region. A parallel distributed compensation (PDC) based fuzzy controller using linear quadratic regulator (LQR) technique is designed to control the RIP system. The results show that the designed controller able to balance the RIP system

Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures

NARCIS (Netherlands)

Jamshidi, A.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.

2017-01-01

This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different

Improved Stabilization Conditions for Nonlinear Systems with Input and State Delays via T-S Fuzzy Model

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Chang Che

2018-01-01

Full Text Available This paper focuses on the problem of nonlinear systems with input and state delays. The considered nonlinear systems are represented by Takagi-Sugeno (T-S fuzzy model. A new state feedback control approach is introduced for T-S fuzzy systems with input delay and state delays. A new Lyapunov-Krasovskii functional is employed to derive less conservative stability conditions by incorporating a recently developed Wirtinger-based integral inequality. Based on the Lyapunov stability criterion, a series of linear matrix inequalities (LMIs are obtained by using the slack variables and integral inequality, which guarantees the asymptotic stability of the closed-loop system. Several numerical examples are given to show the advantages of the proposed results.

The Accuracy Of Fuzzy Sugeno Method With Antropometry On Determination Natural Patient Status

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Syahputra, Dinur; Tulus; Sawaluddin

2017-12-01

Anthropometry is one of the processes that can be used to assess nutritional status. In general anthropometry is defined as body size in terms of nutrition, then anthropometry is reviewed from various age levels and nutritional levels. Nutritional status is a description of the balance between nutritional intake with the needs of the body individually. Fuzzy logic is a logic that has a vagueness between right and wrong or between 0 and 1. Sugeno method is used because in the process of calculating nutritional status so far is still done by anthropometry. Currently information technology is growing in any aspect, one of them in the aspect of calculation with data taken from anthropometry. In this case the calculation can use the Fuzzy Sugeno Method, in order to know the great accuracy obtained. Then the results obtained using fuzzy sugeno integrated with anthropometry has an accuracy of 81.48%.

Fault Estimation for Fuzzy Delay Systems: A Minimum Norm Least Squares Solution Approach.

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Huang, Sheng-Juan; Yang, Guang-Hong

2017-09-01

This paper mainly focuses on the problem of fault estimation for a class of Takagi-Sugeno fuzzy systems with state delays. A minimum norm least squares solution (MNLSS) approach is first introduced to establish a fault estimation compensator, which is able to optimize the fault estimator. Compared with most of the existing fault estimation methods, the MNLSS-based fault estimation method can effectively decrease the effect of state errors on the accuracy of fault estimation. Finally, three examples are given to illustrate the effectiveness and merits of the proposed method.

Impulsive control for a Takagi–Sugeno fuzzy model with time-delay and its application to chaotic systems

International Nuclear Information System (INIS)

Shi-Guo, Peng; Si-Min, Yu

2009-01-01

A control approach where the fuzzy logic methodology is combined with impulsive control is developed for controlling some time-delay chaotic systems in this paper. We first introduce impulses into each subsystem with delay of the Takagi–Sugeno (TS) fuzzy IF–THEN rules and then present a unified TS impulsive fuzzy model with delay for chaos control. Based on the new model, a simple and unified set of conditions for controlling chaotic systems is derived by the Lyapunov–Razumikhin method, and a design procedure for estimating bounds on control matrices is also given. Several numerical examples are presented to illustrate the effectiveness of this method

Control Synthesis of Discrete-Time T-S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach.

Science.gov (United States)

Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Xue, Yusheng

2016-03-01

This paper deals with the problem of control synthesis of discrete-time Takagi-Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new multiinstant fuzzy control scheme and a new class of fuzzy Lyapunov functions, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions, are proposed for implementing the object of relaxed control synthesis. Then, relaxed stabilization conditions are derived with less conservatism than existing ones. Furthermore, the relaxation quality of obtained stabilization conditions is further ameliorated by developing an efficient slack variable approach, which presents a multipolynomial dependence on the normalized fuzzy weighting functions at the current and past instants of time. Two simulation examples are given to demonstrate the effectiveness and benefits of the results developed in this paper.

Fuzzy Stochastic Optimal Guaranteed Cost Control of Bio-Economic Singular Markovian Jump Systems.

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Li, Li; Zhang, Qingling; Zhu, Baoyan

2015-11-01

This paper establishes a bio-economic singular Markovian jump model by considering the price of the commodity as a Markov chain. The controller is designed for this system such that its biomass achieves the specified range with the least cost in a finite-time. Firstly, this system is described by Takagi-Sugeno fuzzy model. Secondly, a new design method of fuzzy state-feedback controllers is presented to ensure not only the regularity, nonimpulse, and stochastic singular finite-time boundedness of this kind of systems, but also an upper bound achieved for the cost function in the form of strict linear matrix inequalities. Finally, two examples including a practical example of eel seedling breeding are given to illustrate the merit and usability of the approach proposed in this paper.

Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem

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Xingjian Wang

2013-01-01

Full Text Available Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control, and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the backstepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem, and the result indicates that the closed-loop system is semiglobally uniformly ultimately bounded.

Synchronization and secure communication of chaotic systems via robust adaptive high-gain fuzzy observer

International Nuclear Information System (INIS)

Hyun, Chang-Ho; Park, Chang-Woo; Kim, Jae-Hun; Park, Mignon

2009-01-01

This paper proposes an alternative robust adaptive high-gain fuzzy observer design scheme and its application to synchronization and secure communication of chaotic systems. It is assumed that their states are immeasurable and their parameters are unknown. The structure of the proposed observer is represented by Takagi-Sugeno fuzzy model and has the integrator of the estimation error. It improves the performance of high-gain observer and makes the proposed observer robust against noisy measurements, uncertainties and parameter perturbations as well. Using Lyapunov stability theory, an adaptive law is derived to estimate the unknown parameters and the stability of the proposed observer is analyzed. Some simulation result of synchronization and secure communication of chaotic systems is given to present the validity of theoretical derivations and the performance of the proposed observer as an application.

Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer

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T. Youssef

2013-01-01

Full Text Available This paper presents an unknown input Proportional Multiple-Integral Observer (PIO for synchronization of chaotic systems based on Takagi-Sugeno (TS fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.

Guaranteed cost control of polynomial fuzzy systems via a sum of squares approach.

Science.gov (United States)

Tanaka, Kazuo; Ohtake, Hiroshi; Wang, Hua O

2009-04-01

This paper presents the guaranteed cost control of polynomial fuzzy systems via a sum of squares (SOS) approach. First, we present a polynomial fuzzy model and controller that are more general representations of the well-known Takagi-Sugeno (T-S) fuzzy model and controller, respectively. Second, we derive a guaranteed cost control design condition based on polynomial Lyapunov functions. Hence, the design approach discussed in this paper is more general than the existing LMI approaches (to T-S fuzzy control system designs) based on quadratic Lyapunov functions. The design condition realizes a guaranteed cost control by minimizing the upper bound of a given performance function. In addition, the design condition in the proposed approach can be represented in terms of SOS and is numerically (partially symbolically) solved via the recent developed SOSTOOLS. To illustrate the validity of the design approach, two design examples are provided. The first example deals with a complicated nonlinear system. The second example presents micro helicopter control. Both the examples show that our approach provides more extensive design results for the existing LMI approach.

5th International Conference on Fuzzy and Neuro Computing

CERN Document Server

Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai

2015-01-01

This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...

Intelligent Control of UPFC for Enhancing Transient Stability on Multi-Machine Power Systems

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Hassan Barati

2010-01-01

Full Text Available One of the benefit of FACTS devices is increase of stability in power systems with control active and reactive power at during the fault in power system. Although, the power system stabilizers (PSSs have been one of the most common controls used to damp out oscillations, this device may not produce enough damping especially to inter-area mode and therefore, there is an increasing interest in using FACTS devices to aid in damping of these oscillations. In This paper, UPFC is used for damping oscillations and to enhance the transient stability performance of power systems. The controller parameters are designed using an efficient version of the Takagi-Sugeno fuzzy control scheme. The function based Takagi-Sugeno-Kang (TSK fuzzy controller uses. For optimization parameters of fuzzy PI controller, the GA, PSO and HGAPSO algorithms are used. The computer simulation results, the effect of UPFC with conventional PI controller, fuzzy PI controller and intelligent controllers (GA, PSO and HGAPSO for damping the local-mode and inter-area mode of under large and small disturbances in the four-machine two-area power system evaluated and compared.

T-S Fuzzy Model Based Control Strategy for the Networked Suspension Control System of Maglev Train

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Guang He

2015-01-01

Full Text Available The control problem for the networked suspension control system of maglev train with random induced time delay and packet dropouts is investigated. First, Takagi-Sugeno (T-S fuzzy models are utilized to represent the discrete-time nonlinear networked suspension control system, and the parameters uncertainties of the nonlinear model have also been taken into account. The controllers take the form of parallel distributed compensation. Then, a sufficient condition for the stability of the networked suspension control system is derived. Based on the criteria, the state feedback fuzzy controllers are obtained, and the controller gains can be computed by using MATLAB LMI Toolbox directly. Finally, both the numerical simulations and physical experiments on the full-scale single bogie of CMS-04 maglev train have been accomplished to demonstrate the effectiveness of this proposed method.

Type-2 fuzzy logic uncertain systems’ modeling and control

CERN Document Server

Antão, Rómulo

2017-01-01

This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

Control of a hydraulic system by means of a fuzzy approach

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Mohamed Ksantini

2013-07-01

Full Text Available Non linear models can be represented conveniently by Takagi-Sugeno fuzzy models when nonlinearities are bounded. This approach uses a collection of linear models which are interpolated by non linear functions. Then the global control law is the interpolation by the same functions of each feedback associated to each linear model. A Lyapunov approach enables to compute these feedback gains. The number of linear models depends directly on the number of nonlinearities the system has. The more models there are, the more difficult it is to guarantee the stability of the closed loop. This paper proposes a method to reduce the number of linear models by assuming a number of nonlinearities considered as uncertainties and to guarantee the global exponential stability of the system. This method is applied on a hydraulic system.

Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2009-01-01

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.

Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

Science.gov (United States)

Jiang, Yizhang; Chung, Fu-Lai; Ishibuchi, Hisao; Deng, Zhaohong; Wang, Shitong

2015-03-01

The classical fuzzy system modeling methods implicitly assume data generated from a single task, which is essentially not in accordance with many practical scenarios where data can be acquired from the perspective of multiple tasks. Although one can build an individual fuzzy system model for each task, the result indeed tells us that the individual modeling approach will get poor generalization ability due to ignoring the intertask hidden correlation. In order to circumvent this shortcoming, we consider a general framework for preserving the independent information among different tasks and mining hidden correlation information among all tasks in multitask fuzzy modeling. In this framework, a low-dimensional subspace (structure) is assumed to be shared among all tasks and hence be the hidden correlation information among all tasks. Under this framework, a multitask Takagi-Sugeno-Kang (TSK) fuzzy system model called MTCS-TSK-FS (TSK-FS for multiple tasks with common hidden structure), based on the classical L2-norm TSK fuzzy system, is proposed in this paper. The proposed model can not only take advantage of independent sample information from the original space for each task, but also effectively use the intertask common hidden structure among multiple tasks to enhance the generalization performance of the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multitask fuzzy system model in multitask regression learning scenarios.

Stability Analysis and Stabilization of T-S Fuzzy Delta Operator Systems with Time-Varying Delay via an Input-Output Approach

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Zhixiong Zhong

2013-01-01

Full Text Available The stability analysis and stabilization of Takagi-Sugeno (T-S fuzzy delta operator systems with time-varying delay are investigated via an input-output approach. A model transformation method is employed to approximate the time-varying delay. The original system is transformed into a feedback interconnection form which has a forward subsystem with constant delays and a feedback one with uncertainties. By applying the scaled small gain (SSG theorem to deal with this new system, and based on a Lyapunov Krasovskii functional (LKF in delta operator domain, less conservative stability analysis and stabilization conditions are obtained. Numerical examples are provided to illustrate the advantages of the proposed method.

Model-Based Evolution of a Fast Hybrid Fuzzy Adaptive Controller for a Pneumatic Muscle Actuator

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Alexander Hošovský

2012-07-01

Full Text Available Pneumatic artificial muscle-based robotic systems usually necessitate the use of various nonlinear control techniques in order to improve their performance. Their robustness to parameter variation, which is generally difficult to predict, should also be tested. Here a fast hybrid adaptive control is proposed, where a conventional PD controller is placed into the feedforward branch and a fuzzy controller is placed into the adaptation branch. The fuzzy controller compensates for the actions of the PD controller under conditions of inertia moment variation. The fuzzy controller of Takagi-Sugeno type is evolved through a genetic algorithm using the dynamic model of a pneumatic muscle actuator. The results confirm the capability of the designed system to provide robust performance under the conditions of varying inertia.

Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T-S Fuzzy Observer-Based Implementation.

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Li, Linlin; Ding, Steven X; Qiu, Jianbin; Yang, Ying

2017-02-01

This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an L ∞ / L 2 type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the L ∞ / L 2 observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

Estimaación del dominio de atracción de sistemas no lineales mediante modelos borrosos polinomiales

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J.L. Pitarch

2012-04-01

Full Text Available Resumen: La mayor parte de referencias de la literatura en control borroso plantean condiciones LMI para un modelo Takagi-Sugeno y dan por terminado el problema una vez se obtienen resultados factibles. No obstante, dejan sin estudiar la región de atracción obtenida. Este tra-baajo propone probar que una zona, de forma prefijada, lo más grande posible, peertenece al dominio de atracción del origen de un sistema no lineal. Para ello, se usan modelos borrosos polinomiales cuyo análisis puede ser llevado a cabo mediante optimización convexa (su-mas de cuadrados. Asimismo, se utiliza información de la forma de las funciones de pertenencia para realizar iteraciones con la región de modelado borroso, maximizando la región de atracción probada, lo cual reduce el conservadurismo sobre otras propuestas. Abstract: Many approaches in fuzzy systems literature express LMI conditions for a Takagi-Sugeno model and finish the problem once those conditions are feasible. However, studying the obtained region of attraction and its relationship with the original nonlinear problem is forgotten. This paper proposes to obtain a predefined-shape zone, as large as possible, belonging to the local domain of attraction of the origin of a nonlinear system. In order to do this, local fuzzy polynomial models are used whose analysis can be carried out by convex optimization (sum of squares. Moreover membership information is used in order to do iterations with the fuzzy modeling region, maximizing the size of the proven domain of attraction, which reduces conservatism over existing results. Palabras clave: Función de Lyapunov, dominio de atracción, sistemas borrosos, Takagi-Sugeno, sistemas polinomiales, estabilidad local, sumas de cuadrados, conservadurismo, Keywords: Lyapunov function, domain of attracion, fuzzy systems, Takagi-Sugeno, polinomial systems, local stabilty, sum of squares, conservatism

Fuzzy logic controller for stabilization of biped robot gait

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Ryadchikov I.V.

2018-01-01

Full Text Available The article centers round the problem of stabilization of biped robot gait through smoothing out the jumps of first and second order derivatives of a biped robot control vector using the fuzzy logic approach. The structure of a composite Takagi-Sugeno fuzzy logic controller developed by the authors is presented. The simulation study of a robot gait with climbing an obstacle is carried out and the results provided in the article showed that the developed controller performed significantly better than the analytical formula model in terms of smoothing out the derivatives of the control vector.

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

The Feedback Control Strategy of the Takagi-Sugeno Fuzzy Car-Following Model with Two Delays

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Cong Zhai

2016-01-01

Full Text Available Considering the driver’s sensing the headway and velocity the different time-varying delays exist, respectively, and the sensitivity of drivers changes with headway and speed. Introducing the fuzzy control theory, a new fuzzy car-following model with two delays is presented, and the feedback control strategy of the new fuzzy car-following model is studied. Based on the Lyapunov function theory and linear matrix inequality (LMI approach, the sufficient condition that the existence of the fuzzy controller is given making the closed-loop system is asymptotic, stable; namely, traffic congestion phenomenon can effectively be suppressed, and the controller gain matrix can be obtained via solving linear matrix inequality. Finally, the simulation examples verify that the method which suppresses traffic congestion and reduces fuel consumption and exhaust emissions is effective.

Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi-Sugeno-Kang fuzzy model

International Nuclear Information System (INIS)

Galdi, V.; Piccolo, A.; Siano, P.

2009-01-01

Nowadays, incentives and financing options for developing renewable energy facilities and the new development in variable speed wind technology make wind energy a competitive source if compared with conventional generation ones. In order to improve the effectiveness of variable speed wind systems, adaptive control systems able to cope with time variances of the system under control are necessary. On these basis, a data driven designing methodology for TSK fuzzy models design is presented in this paper. The methodology, on the basis of given input-output numerical data, generates the 'best' TSK fuzzy model able to estimate with high accuracy the maximum extractable power from a variable speed wind turbine. The design methodology is based on fuzzy clustering methods for partitioning the input-output space combined with genetic algorithms (GA), and recursive least-squares (LS) optimization methods for model parameter adaptation

SOS based robust H(∞) fuzzy dynamic output feedback control of nonlinear networked control systems.

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Chae, Seunghwan; Nguang, Sing Kiong

2014-07-01

In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

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Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2008-01-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB

Analysis, control and design of a non-inverting buck-boost converter: A bump-less two-level T-S fuzzy PI control.

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Almasi, Omid Naghash; Fereshtehpoor, Vahid; Khooban, Mohammad Hassan; Blaabjerg, Frede

2017-03-01

In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi-Sugeno-Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

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Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

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Adel Taieb

2017-01-01

Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

Linear Matrix Inequality Based Fuzzy Synchronization for Fractional Order Chaos

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Bin Wang

2015-01-01

Full Text Available This paper investigates fuzzy synchronization for fractional order chaos via linear matrix inequality. Based on generalized Takagi-Sugeno fuzzy model, one efficient stability condition for fractional order chaos synchronization or antisynchronization is given. The fractional order stability condition is transformed into a set of linear matrix inequalities and the rigorous proof details are presented. Furthermore, through fractional order linear time-invariant (LTI interval theory, the approach is developed for fractional order chaos synchronization regardless of the system with uncertain parameters. Three typical examples, including synchronization between an integer order three-dimensional (3D chaos and a fractional order 3D chaos, anti-synchronization of two fractional order hyperchaos, and the synchronization between an integer order 3D chaos and a fractional order 4D chaos, are employed to verify the theoretical results.

Система управления электроприводом обжиговой машины на основе нечеткого регулятора с эталонной моделью

OpenAIRE

Чумак, А.; Данилова, М.

2012-01-01

Mathematical modeling of the control system of burning kiln synchronous electric drive on the basis of fuzzy model reference controller is considered. A mathematical model of the plant was developed. To approximate non-linear functions Takagi – Sugeno fuzzy algorithm is used. The Fuzzy controller was designed on the programmable logic controller Simatic S7-400

Fuzzy attitude control of solar sail via linear matrix inequalities

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Baculi, Joshua; Ayoubi, Mohammad A.

2017-09-01

This study presents a fuzzy tracking controller based on the Takagi-Sugeno (T-S) fuzzy model of the solar sail. First, the T-S fuzzy model is constructed by linearizing the existing nonlinear equations of motion of the solar sail. Then, the T-S fuzzy model is used to derive the state feedback controller gains for the Twin Parallel Distributed Compensation (TPDC) technique. The TPDC tracks and stabilizes the attitude of the solar sail to any desired state in the presence of parameter uncertainties and external disturbances while satisfying actuator constraints. The performance of the TPDC is compared to a PID controller that is tuned using the Ziegler-Nichols method. Numerical simulation shows the TPDC outperforms the PID controller when stabilizing the solar sail to a desired state.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

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El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

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Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

A new type of simplified fuzzy rule-based system

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Angelov, Plamen; Yager, Ronald

2012-02-01

Over the last quarter of a century, two types of fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi-Sugeno type. They use the same type of scalar fuzzy sets defined per input variable in their antecedent part which are aggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed. Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

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Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

2013-02-01

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

International Nuclear Information System (INIS)

Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

2008-01-01

The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

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El-Sebakhy, Emad A.

2009-09-01

Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

Analysis prediction of Indonesian banks (BCA, BNI, MANDIRI) using adaptive neuro-fuzzy inference system (ANFIS) and investment strategies

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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.

Comparison of intelligent fuzzy based AGC coordinated PID controlled and PSS controlled AVR system

Energy Technology Data Exchange (ETDEWEB)

Mukherjee, V. [Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal (India); Ghoshal, S.P. [Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal (India)

2007-11-15

This paper attempts to investigate the performance of intelligent fuzzy based coordinated control of the Automatic Generation Control (AGC) loop and the excitation loop equipped with Proportional Integral Derivative (PID) controlled Automatic Voltage Regulator (AVR) system and Power System Stabilizer (PSS) controlled AVR system. The work establishes that PSS controlled AVR system is much more robust in dynamic performance of the system over a wide range of system operating configurations. Thus, it is revealed that PSS equipped AVR is much more superior than PID equipped AVR in damping the oscillation resulting in improved transient response. The paper utilizes a novel class of Particle Swarm Optimization (PSO) termed as Craziness based Particle Swarm Optimization (CRPSO) as optimizing tool to get optimal tuning of PSS parameters as well as the gains of PID controllers. For on-line, off-nominal operating conditions Takagi Sugeno Fuzzy Logic (TSFL) has been applied to obtain the off-nominal optimal gains of PID controllers and parameters of PSS. Implementation of TSFL helps to achieve very fast dynamic response. Fourth order model of generator with AVR and high gain thyristor excitation system is considered for PSS controlled system while normal gain exciter is considered for PID controlled system. Simulation study also reveals that with high gain exciter, PID control is not at all effective. Transient responses are achieved by using modal analysis. (author)

Application of multi-model control with fuzzy switching to a micro hydro-electrical power plant

Energy Technology Data Exchange (ETDEWEB)

Salhi, Issam; Doubabi, Said [Laboratory of Electric Systems and Telecommunications (LEST), Faculty of Sciences and Technologies of Marrakesh, Cadi Ayyad University, BP 549, Av Abdelkarim Elkhattabi, Gueliz, Marrakesh (Morocco); Essounbouli, Najib; Hamzaoui, Abdelaziz [CReSTIC, Reims University, 9, rue de Quebec B.P. 396, F-10026 Troyes cedex (France)

2010-09-15

Modelling hydraulic turbine generating systems is not an easy task because they are non-linear and uncertain where the operating points are time varying. One way to overcome this problem is to use Takagi-Sugeno (TS) models, which offer the possibility to apply some tools from linear control theory, whereas those models are composed of linear models connected by a fuzzy activation function. This paper presents an approach to model and control a micro hydro power plant considered as a non-linear system using TS fuzzy systems. A TS fuzzy system with local models is used to obtain a global model of the studied plant. Then, to combine efficiency and simplicity of design, PI controllers are synthesised for each considered operating point to be used as conclusion of an electrical load TS Fuzzy controller. The latter ensures the global stability and desired performance despite the change of operating point. The proposed approach (model and controller) is tested on a laboratory prototype, where the obtained results show their efficiency and their capability to ensure good performance despite the non-linear nature of the plant. (author)

Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

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Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

2016-07-01

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

Science.gov (United States)

Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

2013-04-01

SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

T-S Fuzzy Model-Based Approximation and Filter Design for Stochastic Time-Delay Systems with Hankel Norm Criterion

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Yanhui Li

2014-01-01

Full Text Available This paper investigates the Hankel norm filter design problem for stochastic time-delay systems, which are represented by Takagi-Sugeno (T-S fuzzy model. Motivated by the parallel distributed compensation (PDC technique, a novel filtering error system is established. The objective is to design a suitable filter that guarantees the corresponding filtering error system to be mean-square asymptotically stable and to have a specified Hankel norm performance level γ. Based on the Lyapunov stability theory and the Itô differential rule, the Hankel norm criterion is first established by adopting the integral inequality method, which can make some useful efforts in reducing conservativeness. The Hankel norm filtering problem is casted into a convex optimization problem with a convex linearization approach, which expresses all the conditions for the existence of admissible Hankel norm filter as standard linear matrix inequalities (LMIs. The effectiveness of the proposed method is demonstrated via a numerical example.

A Takagi–Sugeno fuzzy power-distribution method for a prototypical advanced reactor considering pump degradation

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Yue Yuan

2017-08-01

Full Text Available Advanced reactor designs often feature longer operating cycles between refueling and new concepts of operation beyond traditional baseload electricity production. Owing to this increased complexity, traditional proportional–integral control may not be sufficient across all potential operating regimes. The prototypical advanced reactor (PAR design features two independent reactor modules, each connected to a single dedicated steam generator that feeds a common balance of plant for electricity generation and process heat applications. In the current research, the PAR is expected to operate in a load-following manner to produce electricity to meet grid demand over a 24-hour period. Over the operational lifetime of the PAR system, primary and intermediate sodium pumps are expected to degrade in performance. The independent operation of the two reactor modules in the PAR may allow the system to continue operating under degraded pump performance by shifting the power production between reactor modules in order to meet overall load demands. This paper proposes a Takagi–Sugeno (T–S fuzzy logic-based power distribution system. Two T–S fuzzy power distribution controllers have been designed and tested. Simulation shows that the devised T–S fuzzy controllers provide improved performance over traditional controls during daily load-following operation under different levels of pump degradation.

Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

Science.gov (United States)

Syed Ali, M; Vadivel, R; Saravanakumar, R

2018-06-01

This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays.

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Shi, Peng; Zhang, Yingqi; Chadli, Mohammed; Agarwal, Ramesh K

2016-04-01

In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.

Polynomial fuzzy observer designs: a sum-of-squares approach.

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Tanaka, Kazuo; Ohtake, Hiroshi; Seo, Toshiaki; Tanaka, Motoyasu; Wang, Hua O

2012-10-01

This paper presents a sum-of-squares (SOS) approach to polynomial fuzzy observer designs for three classes of polynomial fuzzy systems. The proposed SOS-based framework provides a number of innovations and improvements over the existing linear matrix inequality (LMI)-based approaches to Takagi-Sugeno (T-S) fuzzy controller and observer designs. First, we briefly summarize previous results with respect to a polynomial fuzzy system that is a more general representation of the well-known T-S fuzzy system. Next, we propose polynomial fuzzy observers to estimate states in three classes of polynomial fuzzy systems and derive SOS conditions to design polynomial fuzzy controllers and observers. A remarkable feature of the SOS design conditions for the first two classes (Classes I and II) is that they realize the so-called separation principle, i.e., the polynomial fuzzy controller and observer for each class can be separately designed without lack of guaranteeing the stability of the overall control system in addition to converging state-estimation error (via the observer) to zero. Although, for the last class (Class III), the separation principle does not hold, we propose an algorithm to design polynomial fuzzy controller and observer satisfying the stability of the overall control system in addition to converging state-estimation error (via the observer) to zero. All the design conditions in the proposed approach can be represented in terms of SOS and are symbolically and numerically solved via the recently developed SOSTOOLS and a semidefinite-program solver, respectively. To illustrate the validity and applicability of the proposed approach, three design examples are provided. The examples demonstrate the advantages of the SOS-based approaches for the existing LMI approaches to T-S fuzzy observer designs.

Further studies on stability analysis of nonlinear Roesser-type two-dimensional systems

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Dai, Xiao-Lin

2014-04-01

This paper is concerned with further relaxations of the stability analysis of nonlinear Roesser-type two-dimensional (2D) systems in the Takagi-Sugeno fuzzy form. To achieve the goal, a novel slack matrix variable technique, which is homogenous polynomially parameter-dependent on the normalized fuzzy weighting functions with arbitrary degree, is developed and the algebraic properties of the normalized fuzzy weighting functions are collected into a set of augmented matrices. Consequently, more information about the normalized fuzzy weighting functions is involved and the relaxation quality of the stability analysis is significantly improved. Moreover, the obtained result is formulated in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed result.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

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Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Reliable fuzzy H∞ control for active suspension of in-wheel motor driven electric vehicles with dynamic damping

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Shao, Xinxin; Naghdy, Fazel; Du, Haiping

2017-03-01

A fault-tolerant fuzzy H∞ control design approach for active suspension of in-wheel motor driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The controller is designed based on the quarter-car active suspension model with a dynamic-damping-in-wheel-motor-driven-system, in which the suspended motor is operated as a dynamic absorber. The Takagi-Sugeno (T-S) fuzzy model is used to model this suspension with possible sprung mass variation. The parallel-distributed compensation (PDC) scheme is deployed to derive a fault-tolerant fuzzy controller for the T-S fuzzy suspension model. In order to reduce the motor wear caused by the dynamic force transmitted to the in-wheel motor, the dynamic force is taken as an additional controlled output besides the traditional optimization objectives such as sprung mass acceleration, suspension deflection and actuator saturation. The H∞ performance of the proposed controller is derived as linear matrix inequalities (LMIs) comprising three equality constraints which are solved efficiently by means of MATLAB LMI Toolbox. The proposed controller is applied to an electric vehicle suspension and its effectiveness is demonstrated through computer simulation.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

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Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

2017-07-03

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

Stabilization Using a Discrete Fuzzy PDC Control with PID Controllers and Pole Placement: Application to an Experimental Greenhouse

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Amine Chouchaine

2011-01-01

Full Text Available This paper proposes a control strategy for complex and nonlinear systems, based on a parallel distributed compensation (PDC controller. A solution is presented to solve a stability problem that arises when dealing with a Takagi-Sugeno discrete system with great numbers of rules. The PDC controller will use a classical controller like a PI, PID, or RST in each rule with a pole placement strategy to avoid causing instability. The fuzzy controller presented combines the multicontrol approach and the performance of the classical controllers to obtain a robust nonlinear control action that can also deal with time-variant systems. The presented method was applied to a small greenhouse to control its inside temperature by variation in ventilation rate inside the process. The results obtained will show the efficiency of the adopted method to control the nonlinear and complex systems.

The first order fuzzy predicate logic (I)

International Nuclear Information System (INIS)

Sheng, Y.M.

1986-01-01

Some analysis tools of fuzzy measures, Sugeno's integrals, etc. are introduced into the semantic of the first order predicate logic to explain the concept of fuzzy quantifiers. The truth value of a fuzzy quantification proposition is represented by Sugeno's integral. With this framework, several important notions of formation rules, fuzzy valutions and fuzzy validity are discussed

A Comparative Study on Sugeno Integral Approach to Aggregation of Experts' Opinion

International Nuclear Information System (INIS)

Kim, Seong Ho; Kim, Kil Yoo; Kim, Tae Woon

2007-01-01

A multicriteria decision-making (MCDM) problem of preference ranking of various alternatives is common in science and engineering fields. Usually, the MCDM problem is characterized in terms of two factors: relative importance of each evaluation criterion and appropriateness of each alternative. The ranking is determined by a relative degree of appropriateness of decision alternatives. In reality, there are different grades of interaction among decision criteria. One of well-known approaches to aggregation of those two factors is the weighted arithmetic mean (WAM) approach. Here, importance weights for criteria are viewed as probability measures. The weights are linearly aggregated with appropriateness values. In the present work, the main objective is to study an aggregation model with various grades of interactions among the decision elements. The successful applications of fuzzy integral aggregation operators to subjective MCDM problems have been motivating this work. On the basis of λ-fuzzy measures and Sugeno integral (SI), the SI aggregation approach is proposed. Here, interaction among criteria is dealt with λ-fuzzy measures. Aggregation of these measures and appropriateness values is implemented, especially, by the Sugeno integral as one of fuzzy integrals. Aggregated values obtained by the SI approach are viewed as decision maker's pessimistic (or conservative) attitude towards information aggregation, compared to the WAM approach. Firstly, the concepts of the λ-fuzzy measure and the Sugeno fuzzy integral are introduced. Then, as an application of the SI approach, an illustrative example is given

Multivariable fuzzy modelling and command: application to the thermic engine control; Modelisation et commande multivariables floues: application a la commande d`un moteur thermique

Energy Technology Data Exchange (ETDEWEB)

Bortolet, P.

1998-12-11

During these last two decades, the growing awareness of the contribution of the automobile to the degradation of the environment has forces different figures from the transportation world to put automobiles under more and more severe controls. Fuzzy logic is a technique which allows for the taking into account of experts knowledge; the most recent research work has moreover shown interest in associating fuzzy logic with algorithmic control techniques (adaptive control, robust control...). Our research work can be broken down into three distinct parts: a theoretical approach concerning the methods of fuzzy modeling permitting one to achieve models of the type Takagi-Sugeno and to use them in the synthesis of controls; the work of physical modeling of a four-stroke direct injection gas motor in collaboration with the development teams from Siemens Automotive SA; the simulated application of fuzzy modeling techniques and of fuzzy control developed on a theoretical level to a four-stroke direct injection gas motor. (author) 105 refs.

Modeling and Control of Multivariable Process Using Intelligent Techniques

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Subathra Balasubramanian

2010-10-01

Full Text Available For nonlinear dynamic systems, the first principles based modeling and control is difficult to implement. In this study, a fuzzy controller and recurrent fuzzy controller are developed for MIMO process. Fuzzy logic controller is a model free controller designed based on the knowledge about the process. In fuzzy controller there are two types of rule-based fuzzy models are available: one the linguistic (Mamdani model and the other is Takagi–Sugeno model. Of these two, Takagi-Sugeno model (TS has attracted most attention. The fuzzy controller application is limited to static processes due to their feedforward structure. But, most of the real-time processes are dynamic and they require the history of input/output data. In order to store the past values a memory unit is needed, which is introduced by the recurrent structure. The proposed recurrent fuzzy structure is used to develop a controller for the two tank heating process. Both controllers are designed and implemented in a real time environment and their performance is compared.

Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

International Nuclear Information System (INIS)

Yong, Li; Ying-Gan, Tang

2010-01-01

A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

Robust stability analysis of Takagi—Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Ali, M. Syed

2011-01-01

In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. (general)

Perancangan Kendali Robot pada Smartphone Menggunakan Sensor Accelerometer Berbasis Metode Fuzzy Logic

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Mohamad Agung Prawira Negara

2017-08-01

Full Text Available Telecommunications and robotics technology is being developed to assist and facilitate the work of a human. In the field of telecommunications particularly smartphone has reached the planting of operating systems like android until planting sensors such as an accelerometer, gyro, proximity, etc. We would like to take advantage of the accelerometer sensor on a smartphone as robot control. We will compare the use of Sugeno Fuzzy Logic and Mamdani Fuzzy Logic to determine the best control method. The basic components of the robot are the Bluetooth module HC-05 as a medium of communication with the android, arduino as the control system and actuators such as DC motors drive the rear wheels to adjust the speed of the robot, and servo motor drives the front wheels to adjust the degree of turn robot. In robot’s movement test, 4 of 8 trials or approximately 50% stated better Sugeno Fuzzy Logic than Mamdani Fuzzy Logic in terms of linearity. In robot's controller response test, for Sugeno Fuzzy Logic method the average delay is 0.41 seconds, and for Mamdani Fuzzy Logic method the average delay is 10.80 seconds.

Control Synthesis of Discrete-Time T-S Fuzzy Systems: Reducing the Conservatism Whilst Alleviating the Computational Burden.

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Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Peng, Chen

2017-09-01

The augmented multi-indexed matrix approach acts as a powerful tool in reducing the conservatism of control synthesis of discrete-time Takagi-Sugeno fuzzy systems. However, its computational burden is sometimes too heavy as a tradeoff. Nowadays, reducing the conservatism whilst alleviating the computational burden becomes an ideal but very challenging problem. This paper is toward finding an efficient way to achieve one of satisfactory answers. Different from the augmented multi-indexed matrix approach in the literature, we aim to design a more efficient slack variable approach under a general framework of homogenous matrix polynomials. Thanks to the introduction of a new extended representation for homogeneous matrix polynomials, related matrices with the same coefficient are collected together into one sole set and thus those redundant terms of the augmented multi-indexed matrix approach can be removed, i.e., the computational burden can be alleviated in this paper. More importantly, due to the fact that more useful information is involved into control design, the conservatism of the proposed approach as well is less than the counterpart of the augmented multi-indexed matrix approach. Finally, numerical experiments are given to show the effectiveness of the proposed approach.

Perancangan Prediktor Cuaca Maritim Berbasis Logika Fuzzy Menggunakan User Interface Android

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Muhammad Kahfi Anshari

2013-09-01

Full Text Available Pola iklim dan cuaca di Indonesia yang tidak ber¬aturan dan eskstrim akan mengganggu transportasi laut. Pada penelitian ini telah dilakukan perancangan prediktor cuaca maritim berbasis logika fuzzy takagi sugeno menggunakan user interface smartphone android.  User interface smar¬t¬phone an¬droid dipilih karena android banyak digunakan masy¬arakat indonesia. Data yang digunakan untuk membangun basis atu¬ran dan fungsi keanggotaan berasal dari data BMKG II Perak yang direkam perjam selama 6 tahun yaitu dari januari 2007 hingga desember 2012. Digunakan data cuaca maritim dari tahun 2007 hingga 2012 untuk membangun basis aturan dan fungsi keanggotaan logika fuzzy.  Validasi prediksi cuaca maritim di¬lakukan dengan menggunakan data BMKG bulan februari 2013. Selain menggunakan data BMKG juga  dilakukan validasi real¬time menggunakan data maritim buoyweather. Hasil pene¬litian didapatkan akurasi prediksi cuaca maritim tertinggi, yaitu: suhu udara, kelembaban udara, kecepatan arus laut, tinggi gelombang dan curah hujan adalah 83%, 84.5%, 87 %, 85.7% dan 95%.

Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

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Xi Wang

2016-01-01

Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.

Design of PDC Controllers by Matrix Reversibility for Synchronization of Yin and Yang Chaotic Takagi-Sugeno Fuzzy Henon Maps

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Chun-Yen Ho

2012-01-01

Full Text Available This paper investigates the synchronization of Yin and Yang chaotic T-S fuzzy Henon maps via PDC controllers. Based on the Chinese philosophy, Yin is the decreasing, negative, historical, or feminine principle in nature, while Yang is the increasing, positive, contemporary, or masculine principle in nature. Yin and Yang are two fundamental opposites in Chinese philosophy. The Henon map is an invertible map; so the Henon maps with increasing and decreasing argument can be called the Yang and Yin Henon maps, respectively. Chaos synchronization of Yin and Yang T-S fuzzy Henon maps is achieved by PDC controllers. The design of PDC controllers is based on the linear invertible matrix theory. The T-S fuzzy model of Yin and Yang Henon maps and the design of PDC controllers are novel, and the simulation results show that the approach is effective.

Further development of Chebyshev type inequalities for Sugeno integrals and T-(S-)evaluators

Czech Academy of Sciences Publication Activity Database

Agahi, H.; Mesiar, Radko; Ouyang, Y.

2010-01-01

Roč. 46, č. 1 (2010), s. 83-95 ISSN 0023-5954 R&D Projects: GA ČR GA402/08/0618 Institutional research plan: CEZ:AV0Z10750506 Keywords : Sugeno integral * fuzzy measure * comonotone functions * Chebyshev's inequality Subject RIV: BA - General Mathematics Impact factor: 0.461, year: 2010 http://library.utia.cas.cz/separaty/2010/E/mesiar-further development of chebyshev type inequalities for sugeno integrals and t-(s-)evaluators.pdf

Adaptive Controller for 6-DOF Parallel Robot Using T-S Fuzzy Inference

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Xue Jian

2013-02-01

Full Text Available 6-DOF parallel robot always appears in the form of Stewart platform. It has been widely used in industry for the benefits such as strong structural stiffness, high movement accuracy and so on. Space docking technology makes higher requirements of motion accuracy and dynamic performance to the control method on 6-DOF parallel robot. In this paper, a hydraulic 6-DOF parallel robot was used to simulate the docking process. Based on this point, this paper gave a thorough study on the design of an adaptive controller to eliminate the asymmetric of controlled plant and uncertain load force interference. Takagi-Sugeno (T-S fuzzy inference model was used to build the fuzzy adaptive controller. With T-S model, the controller directly imposes adaptive control signal on the plant to make sure that the output of plant could track the reference model output. The controller has simple structure and is easy to implement. Experiment results show that the controller can eliminate asymmetric and achieve good dynamic performance, and has good robustness to load interference.

Robust Fault Estimation Design for Discrete-Time Nonlinear Systems via A Modified Fuzzy Fault Estimation Observer.

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Xie, Xiang-Peng; Yue, Dong; Park, Ju H

2018-02-01

The paper provides relaxed designs of fault estimation observer for nonlinear dynamical plants in the Takagi-Sugeno form. Compared with previous theoretical achievements, a modified version of fuzzy fault estimation observer is implemented with the aid of the so-called maximum-priority-based switching law. Given each activated switching status, the appropriate group of designed matrices can be provided so as to explore certain key properties of the considered plants by means of introducing a set of matrix-valued variables. Owing to the reason that more abundant information of the considered plants can be updated in due course and effectively exploited for each time instant, the conservatism of the obtained result is less than previous theoretical achievements and thus the main defect of those existing methods can be overcome to some extent in practice. Finally, comparative simulation studies on the classical nonlinear truck-trailer model are given to certify the benefits of the theoretic achievement which is obtained in our study. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

African Journals Online (AJOL)

The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate ...

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.

Science.gov (United States)

Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

2012-08-01

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

Developing a multipurpose sun tracking system using fuzzy control

Energy Technology Data Exchange (ETDEWEB)

Alata, Mohanad [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)]. E-mail: alata@just.edu.jo; Al-Nimr, M.A. [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan); Qaroush, Yousef [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)

2005-05-01

The present work demonstrates the design and simulation of time controlled step sun tracking systems that include: one axis sun tracking with the tilted aperture equal to the latitude angle, equatorial two axis sun tracking and azimuth/elevation sun tracking. The first order Sugeno fuzzy inference system is utilized for modeling and controller design. In addition, an estimation of the insolation incident on a two axis sun tracking system is determined by fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm, along with least square estimation (LSE), generates the fuzzy rules that describe the relationship between the input/output data of solar angles that change with time. The fuzzy rules are tuned by an adaptive neuro-fuzzy inference system (ANFIS). Finally, an open loop control system is designed for each of the previous types of sun tracking systems. The results are shown using simulation and virtual reality. The site of application is chosen at Amman, Jordan (32 deg. North, 36 deg. East), and the period of controlling and simulating each type of tracking system is the year 2003.

Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults

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Jin Wang

2017-03-01

Full Text Available This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi–Sugeno fuzzy model. An interval type-II Takagi–Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of such method are verified by a series of the comparison of numerical simulation results.

Analysing the strength of friction stir welded dissimilar aluminium alloys using Sugeno Fuzzy model

Science.gov (United States)

Barath, V. R.; Vaira Vignesh, R.; Padmanaban, R.

2018-02-01

Friction stir welding (FSW) is a promising solid state joining technique for aluminium alloys. In this study, FSW trials were conducted on two dissimilar plates of aluminium alloy AA2024 and AA7075 by varying the tool rotation speed (TRS) and welding speed (WS). Tensile strength (TS) of the joints were measured and a Sugeno - Fuzzy model was developed to interconnect the FSW process parameters with the tensile strength. From the developed model, it was observed that the optimum heat generation at WS of 15 mm.min-1 and TRS of 1050 rpm resulted in dynamic recovery and dynamic recrystallization of the material. This refined the grains in the FSW zone and resulted in peak tensile strength among the tested specimens. Crest parabolic trend was observed in tensile strength with variation of TRS from 900 rpm to 1200 rpm and TTS from 10 mm.min-1 to 20 mm.min-1.

Robust Power Management Control for Stand-Alone Hybrid Power Generation System

International Nuclear Information System (INIS)

Kamal, Elkhatib; Adouane, Lounis; Aitouche, Abdel; Mohammed, Walaa

2017-01-01

This paper presents a new robust fuzzy control of energy management strategy for the stand-alone hybrid power systems. It consists of two levels named centralized fuzzy supervisory control which generates the power references for each decentralized robust fuzzy control. Hybrid power systems comprises: a photovoltaic panel and wind turbine as renewable sources, a micro turbine generator and a battery storage system. The proposed control strategy is able to satisfy the load requirements based on a fuzzy supervisor controller and manage power flows between the different energy sources and the storage unit by respecting the state of charge and the variation of wind speed and irradiance. Centralized controller is designed based on If-Then fuzzy rules to manage and optimize the hybrid power system production by generating the reference power for photovoltaic panel and wind turbine. Decentralized controller is based on the Takagi-Sugeno fuzzy model and permits us to stabilize each photovoltaic panel and wind turbine in presence of disturbances and parametric uncertainties and to optimize the tracking reference which is given by the centralized controller level. The sufficient conditions stability are formulated in the format of linear matrix inequalities using the Lyapunov stability theory. The effectiveness of the proposed Strategy is finally demonstrated through a SAHPS (stand-alone hybrid power systems) to illustrate the effectiveness of the overall proposed method. (paper)

A Comparative Analysis of Fuzzy Inference Engines in Context of ...

African Journals Online (AJOL)

PROF. O. E. OSUAGWU

Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from ... is known as the inference, or rule definition portion, of fuzzy .... minimal set of decision rules based on input- ... The study uses Mamdani FIS model and. Sugeno FIS ... control of induction motor drive. [18] study.

A (star)-BASED MINKOWSKI'S INEQUALITY FOR SUGENO FRACTIONAL INTEGRAL OF ORDER alpha > 0

Czech Academy of Sciences Publication Activity Database

Babkhani, A.; Agahi, H.; Mesiar, Radko

2015-01-01

Roč. 18, č. 4 (2015), s. 862-874 ISSN 1311-0454 Institutional support: RVO:67985556 Keywords : fuzzy integral * Sugeno fractional integral * Minkowski's inequality Subject RIV: BA - General Mathematics Impact factor: 2.246, year: 2015 http://library.utia.cas.cz/separaty/2015/E/mesiar-0446629.pdf

Metodología para la implementación de controlador difuso tipo Takagi-Sugeno en PLC s7-300

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Cristian Guarnizo Lemus

2011-12-01

Full Text Available This paper presents a methodology for implementing fuzzy logic controllers based on a S7-300 PLC using the programming language SCL(Structured Control Language in STEP 7. We present the design of the fuzzy function, variable declarations, and the evaluation design of membership functions and rules of the fuzzy system. Since this scheme can be implemented more complex fuzzy models, such as adaptive or Autotuner. We present an application example for a discrete time simulated in the PLC using a PI fuzzy controller.

Decision of Lead-Time Compression and Stable Operation of Supply Chain

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Songtao Zhang

2017-01-01

Full Text Available A cost optimization strategy and a robust control strategy were studied to realize the low-cost robust operation of the supply chain with lead times. Firstly, for the multiple production lead times which existed in the supply chain, a corresponding inventory state model and a supply chain cost model were constructed based on the Takagi-Sugeno fuzzy control system. Then, by considering the actual inventory level, the lead-time compression cost, and the stock-out cost, a cost optimization strategy was proposed. Furthermore, a fuzzy robust control strategy was proposed to realize the flexible switching among the models. Finally, the simulation results show that the total cost of the supply chain could be reduced effectively by the cost optimization strategy, and the stable operation of the supply chain could be realized by the proposed fuzzy robust control strategy.

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).

Science.gov (United States)

Hosseini, Seyed Abolfazl; Esmaili Paeen Afrakoti, Iman

2018-01-17

The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were

A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning.

Science.gov (United States)

Kumar, Anupam; Kumar, Vijay

2017-05-01

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal control of a CSTR process

Directory of Open Access Journals (Sweden)

A. Soukkou

2008-12-01

Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

Science.gov (United States)

Castañón-Puga, Manuel; Salazar, Abby Stephanie; Aguilar, Leocundo; Gaxiola-Pacheco, Carelia; Licea, Guillermo

2015-12-02

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

Driver's Behavior Modeling Using Fuzzy Logic

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Sehraneh Ghaemi

2010-01-01

Full Text Available In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model called Model I is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules called Model II and Model III by using Sugeno fuzzy inference. Model II and Model III have less linguistic terms than Model I for the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

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G Khalili-Zadeh-Mahani

2016-07-01

Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

Science.gov (United States)

Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

2016-05-01

Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. Published by Elsevier Ltd.

Polynomial fuzzy model-based approach for underactuated surface vessels

DEFF Research Database (Denmark)

Khooban, Mohammad Hassan; Vafamand, Navid; Dragicevic, Tomislav

2018-01-01

The main goal of this study is to introduce a new polynomial fuzzy model-based structure for a class of marine systems with non-linear and polynomial dynamics. The suggested technique relies on a polynomial Takagi–Sugeno (T–S) fuzzy modelling, a polynomial dynamic parallel distributed compensation...... surface vessel (USV). Additionally, in order to overcome the USV control challenges, including the USV un-modelled dynamics, complex nonlinear dynamics, external disturbances and parameter uncertainties, the polynomial fuzzy model representation is adopted. Moreover, the USV-based control structure...... and a sum-of-squares (SOS) decomposition. The new proposed approach is a generalisation of the standard T–S fuzzy models and linear matrix inequality which indicated its effectiveness in decreasing the tracking time and increasing the efficiency of the robust tracking control problem for an underactuated...

Knowledge extraction from evolving spiking neural networks with rank order population coding.

Science.gov (United States)

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Design of a bio-inspired controller for dynamic soaring in a simulated unmanned aerial vehicle.

Science.gov (United States)

Barate, Renaud; Doncieux, Stéphane; Meyer, Jean-Arcady

2006-09-01

This paper is inspired by the way birds such as albatrosses are able to exploit wind gradients at the surface of the ocean for staying aloft for very long periods while minimizing their energy expenditure. The corresponding behaviour has been partially reproduced here via a set of Takagi-Sugeno-Kang fuzzy rules controlling a simulated glider. First, the rules were hand-designed. Then, they were optimized with an evolutionary algorithm that improved their efficiency at coping with challenging conditions. Finally, the robustness properties of the controller generated were assessed with a view to its applicability to a real platform.

Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering

Directory of Open Access Journals (Sweden)

Maulana Erwin Saputra

2017-06-01

Full Text Available In the first journal, I made this attempt to analyze things that affect the achievement of students in each school of course vary. Because students are one of the goals of achieving the goals of successful educational organizations. The mental influence of students’ emotions and behaviors themselves in relation to learning performance. Fuzzy logic can be used in various fields as well as Clustering for grouping, as in Learning Development analyzes. The process will be performed on students based on the symptoms that exist. In this research will use fuzzy logic and clustering. Fuzzy is an uncertain logic but its excess is capable in the process of language reasoning so that in its design is not required complicated mathematical equations. However Clustering method is K-Means method is method where data analysis is broken down by group k (k = 1,2,3, .. k. To know the optimal number of Performance group. The results of the research is with a questionnaire entered into matlab will produce a value that means in generating the graph. And simplify the school in seeing Student performance in the learning process by using certain criteria. So from the system that obtained the results for a decision-making required by the school.

Fuzzy logic controllers and chaotic natural convection loops

International Nuclear Information System (INIS)

Theler, German

2007-01-01

The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop [es

Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms

Science.gov (United States)

Jough, Fooad Karimi Ghaleh; Şensoy, Serhan

2016-12-01

Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing modelling uncertainties, record to record (RTR) variations and cognitive uncertainties for moment resisting steel frames of various heights is discussed in detail. RTR uncertainty is used by incremental dynamic analysis (IDA), modelling uncertainties are considered through backbone curves and hysteresis loops of component, and cognitive uncertainty is presented in three levels of material quality. IDA is used to evaluate RTR uncertainty based on strong ground motion records selected by the k-means algorithm, which is favoured over Monte Carlo selection due to its time saving appeal. Analytical equations of the Response Surface Method are obtained through IDA results by the Cuckoo algorithm, which predicts the mean and standard deviation of the collapse fragility curve. The Takagi-Sugeno-Kang model is used to represent material quality based on the response surface coefficients. Finally, collapse fragility curves with the various sources of uncertainties mentioned are derived through a large number of material quality values and meta variables inferred by the Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. It is concluded that a better risk management strategy in countries where material quality control is weak, is to account for cognitive uncertainties in fragility curves and the mean annual frequency.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

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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.

A novel human-machine interface based on recognition of multi-channel facial bioelectric signals

International Nuclear Information System (INIS)

Razazadeh, Iman Mohammad; Firoozabadi, S. Mohammad; Golpayegani, S.M.R.H.; Hu, H.

2011-01-01

Full text: This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multichannel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Fron-tails and Temporalis facial muscles. The acquired signals are passes through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs. rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. (author)

Metodología formal de análisis del comportamiento dinámico de sistemas no lineales mediante lógica borrosa

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Antonio Javier Barragán

2015-10-01

comportamiento dinámico del sistema. Abstract: Having the ability to analyze a system from a dynamic point of view can be very useful in many circumstances (industrial systems, biological, economical, . . .. The dynamic analysis of a system allows to understand its behavior and response to different inputs, open loop stability, both locally and globally, or if it is affected by nonlinear phenomena, such as limit cycles, or bifurcations, among others. If the system is unknown or its dynamic is complex enough to obtain its mathematical model, in principle it would not be possible to make a formal dynamic analysis of the system. In these cases, fuzzy logic, and more specifically fuzzy TS models is presented as a powerful tool for analysis and design. The TS fuzzy models are universal approximators both of a function and its derivative, so it allows modeling highly nonlinear systems based on input/output data. Since a fuzzy model is a mathematical model formally speaking, it is possible to study the dynamic aspects of the real system that it models such as in the theory of nonlinear control.This article describes a methodology for obtaining the equilibrium states of a generic nonlinear system, the exact linearization of a completely general fuzzy model, and the use of the Poincaré’s methodology for the study of periodic orbits in fuzzy models. From this information it is possible to study the local stability of the equilibrium states, the dynamics of the system in its environment, and the presence of oscillations, yielding valuable information on the dynamic behavior of the system. Palabras clave: Análisis dinámico, estabilidad, estado de equilibrio, linealización, metodología de Poincaré, modelado borroso, sistemas dinámicos, Takagi-Sugeno (TS model, Keywords: Dynamic analysis dynamic systems equilibrium state fuzzy control linearization fuzzy modeling Poincaré’s methodology stability Takagi-Sugeno (TS model

Increase of the Integration Degree of Wind Power Plants into the Energy System Using Wind Forecasting and Power Consumption Predictor Models by Transmission System Operator

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Manusov V.Z.

2017-12-01

Full Text Available Wind power plants’ (WPPs high penetration into the power system leads to various inconveniences in the work of system operators. This fact is associated with the unpredictable nature of wind speed and generated power, respectively. Due to these factors, such source of electricity must be connected to the power system to avoid detrimental effects on the stability and quality of electricity. The power generated by the WPPs is not regulated by the system operator. Accurate forecasting of wind speed and power, as well as power load can solve this problem, thereby making a significant contribution to improving the power supply systems reliability. The article presents a mathematical model for the wind speed prediction, which is based on autoregression and fuzzy logic derivation of Takagi-Sugeno. The new model of wavelet transform has been developed, which makes it possible to include unnecessary noise from the model, as well as to reveal the cycling of the processes and their trend. It has been proved, that the proposed combination of methods can be used simultaneously to predict the power consumption and the wind power plant potential power at any time interval, depending on the planning horizon. The proposed models support a new scientific concept for the predictive control system of wind power stations and increase their degree integration into the electric power system.

A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

Science.gov (United States)

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

Prototyping qualitative controllers for fuzzy-logic controller design

International Nuclear Information System (INIS)

Bakhtiari, S.; Jabedar-Maralani, P.

1999-05-01

Qualitative controls can be designed for linear and nonlinear models with the same computational complexity. At the same time they show the general form of the proper control. These properties can help ease the design process for quantitative controls. In this paper qualitative controls are used as prototypes for the design of linear or nonlinear, and in particular Sugeno-type fuzzy, controls. The LMS identification method is used to approximate the qualitative control with the nearest fuzzy control. The method is applied to the problem of position control in a permanent magnet synchronous motor; moreover, the performance and the robustness of the two controllers are compared

Probabilistic fuzzy systems as additive fuzzy systems

NARCIS (Netherlands)

Almeida, R.J.; Verbeek, N.; Kaymak, U.; Costa Sousa, da J.M.; Laurent, A.; Strauss, O.; Bouchon-Meunier, B.; Yager, R.

2014-01-01

Probabilistic fuzzy systems combine a linguistic description of the system behaviour with statistical properties of data. It was originally derived based on Zadeh’s concept of probability of a fuzzy event. Two possible and equivalent additive reasoning schemes were proposed, that lead to the

Fuzzy Impulsive Control of Permanent Magnet Synchronous Motors

International Nuclear Information System (INIS)

Dong, Li; Shi-Long, Wang; Xiao-Hong, Zhang; Dan, Yang; Hui, Wang

2008-01-01

The permanent magnet synchronous motors (PMSMs) may experience chaotic behaviours with systemic parameters falling into a certain area or under certain working conditions, which threaten the secure and stable operation of motor-driven. Hence, it is important to study the methods of controlling or suppressing chaos in PMSMs. In this work, the Takagi–Sugeno (T-S) fuzzy impulsive control model for PMSMs is established via the T-S modelling methodology and impulsive technology. Based on the new model, the control conditions of asymptotical stability and exponential stability for PMSMs have been derived by the Lyapunov method. Finally, an illustrated example is also given to show the effectiveness of the obtained results

Multimodel Predictive Control Approach for UAV Formation Flight

Directory of Open Access Journals (Sweden)

Chang-jian Ru

2014-01-01

Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.

Introduction to fuzzy systems

CERN Document Server

Chen, Guanrong

2005-01-01

Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th

eFSM--a novel online neural-fuzzy semantic memory model.

Science.gov (United States)

Tung, Whye Loon; Quek, Chai

2010-01-01

Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This

The gap values in the profile matching method by fuzzy logic

Science.gov (United States)

Sitepu, S. A.; Efendi, S.; Situmorang, Z.

2018-03-01

In this research, the determination of the appropriate values of Gap for the assessment of promotion criteria of position in an institution / company. In this study the authors use Fuzzy Sugeno logic on the determination of Gap values used in Profile Matching method. Test results of 5 employees obtained the eligibility of promotion with the position of Z* values between in 3.20 to 4.11.

An opposition-based harmony search algorithm for engineering optimization problems

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Abhik Banerjee

2014-03-01

Full Text Available Harmony search (HS is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. The proposed opposition-based HS (OHS of the present work employs opposition-based learning for harmony memory initialization and also for generation jumping. The concept of opposite number is utilized in OHS to improve the convergence rate of the HS algorithm. The potential of the proposed algorithm is assessed by means of an extensive comparative study of the numerical results on sixteen benchmark test functions. Additionally, the effectiveness of the proposed algorithm is tested for reactive power compensation of an autonomous power system. For real-time reactive power compensation of the studied model, Takagi Sugeno fuzzy logic (TSFL is employed. Time-domain simulation reveals that the proposed OHS-TSFL yields on-line, off-nominal model parameters, resulting in real-time incremental change in terminal voltage response profile.

Saturated Switching Systems

CERN Document Server

Benzaouia, Abdellah

2012-01-01

Saturated Switching Systems treats the problem of actuator saturation, inherent in all dynamical systems by using two approaches: positive invariance in which the controller is designed to work within a region of non-saturating linear behaviour; and saturation technique which allows saturation but guarantees asymptotic stability. The results obtained are extended from the linear systems in which they were first developed to switching systems with uncertainties, 2D switching systems, switching systems with Markovian jumping and switching systems of the Takagi-Sugeno type. The text represents a thoroughly referenced distillation of results obtained in this field during the last decade. The selected tool for analysis and design of stabilizing controllers is based on multiple Lyapunov functions and linear matrix inequalities. All the results are illustrated with numerical examples and figures many of them being modelled using MATLAB®. Saturated Switching Systems will be of interest to academic researchers in con...

Diseño y sintonización de una ley de control borrosa proporcional retardada: enfoque frecuencial

Directory of Open Access Journals (Sweden)

R. Villafuerte

2015-10-01

Full Text Available Resumen: Los retardos son por lo general un fenómeno indeseable en los procesos de control, debido a que estos pueden inestabilizar o producir un desempeño deficiente en la respuesta de un sistema. Sin embargo, los retardos también tienen la propiedad de coadyuvar a su estabilización. El presente artículo se beneficia de esta propiedad al proponer el diseño y sintonización de una ley de control borrosa proporcional retardada (BPR para estabilizar una clase de sistemas no lineales. En este marco, la estabilidad del sistema no lineal se garantiza mediante la σ-estabilización de su modelo borroso del tipo Takagi-Sugeno (TS en lazo cerrado con la ley de control BPR. El diseño del controlador BPR se propone a partir de la inclusión de una acción retardada en la estructura clásica, mientras que la sintonización del mismo se realiza asegurando σ-estabilidad sobre cada uno de los susbsistemas del modelo borroso empleando el método D-particiones. La σ-estabilización del sistema TS-BPR se garantiza mediante un análisis del lugar geométrico de las ráıces de su cuasipolinomio característico. La metodología sólo es aplicable a sistemas no lineales que se puedan modelar mediante subsistemas borrosos lineales de segundo orden. El diseño y la sintonización del controlador BPR se ejemplifican sobre una plataforma experimental carro-péndulo. El desempeño del BPR es comparado con una clásica compensación paralela Abstract: The time delays are usually an undesirable phenomenon in the control processes, because these can induce instability or a poor performance in the system. However, the time delays have the property of assisting in stabilizing. This article benefiting from this property to propose the design and tuning of a fuzzy proportional retarded controller (BPR to stabilize a class of nonlinear systems. In this frame, the stabilization of a nonlinear system is

Stability Analysis of Interconnected Fuzzy Systems Using the Fuzzy Lyapunov Method

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Ken Yeh

2010-01-01

Full Text Available The fuzzy Lyapunov method is investigated for use with a class of interconnected fuzzy systems. The interconnected fuzzy systems consist of J interconnected fuzzy subsystems, and the stability analysis is based on Lyapunov functions. Based on traditional Lyapunov stability theory, we further propose a fuzzy Lyapunov method for the stability analysis of interconnected fuzzy systems. The fuzzy Lyapunov function is defined in fuzzy blending quadratic Lyapunov functions. Some stability conditions are derived through the use of fuzzy Lyapunov functions to ensure that the interconnected fuzzy systems are asymptotically stable. Common solutions can be obtained by solving a set of linear matrix inequalities (LMIs that are numerically feasible. Finally, simulations are performed in order to verify the effectiveness of the proposed stability conditions in this paper.

Sugeno integral ranking of release scenarios in a low and intermediate waste repository

International Nuclear Information System (INIS)

Kim, S. Ho; Kim, Tae Woon; Ha, Jae Joo

2004-01-01

In the present study, a multi criteria decision-making (MCDM) problem of ranking of important radionuclide release scenarios in a low and intermediate radioactive waste repository is to treat on the basis of λ-fuzzy measures and Sugeno integral. Ranking of important scenarios can lead to the provision of more effective safety measure in a design stage of the repository. The ranking is determined by a relative degree of appropriateness of scenario alternatives. To demonstrate a validation of the proposed approach to ranking of release scenarios, results of the previous AHP study are used and compared with them of the present SIAHP approach. Since the AHP approach uses importance weight based on additive probability measures, the interaction among criteria is ignored. The comparison of scenarios ranking obtained from these two approaches enables us to figure out the effect of different models for interaction among criteria

Fuzzy control techniques applied to a three phase synchronous rectifier current loop

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Alberto Berzoy-Lleren

2015-01-01

Full Text Available Este trabajo presenta tres técnicas de control difuso de estructura variable (FVSC aplicadas al lazo de control de corriente en un rectificador síncrono trifásico. Estas técnicas se basan en un controlador PI difuso Takagi-Sugeno (T-S. El primer controlador es un FVSC de primer orden que se usa como referencia. A continuación se presentan y prueban dos estrategias FVSC, FVSC de segundo y tercer orden. Estos esquemas de control se simulan primero y luego se prueban en un rectificador síncrono de propósito general de laboratorio. Los resultados experimentales muestran que el rendimiento del control es bueno en los tres esquemas, logrando compensación de las corrientes armónicas y corrección del factor de potencia.

Podemos prever a taxa de cambio brasileira? Evidência empírica utilizando inteligência computacional e modelos econométricos Can we forecast Brazilian exchange rates? Empirical evidences using computational intelligence and econometric models

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Leandro dos Santos Coelho

2008-12-01

Full Text Available As abordagens de inteligência computacional, tais como sistemas nebulosos e redes neurais artificiais, têm-se gradualmente estabelecido como ferramentas robustas para a tarefa de aproximação de sistemas não-lineares complexos e previsão de séries temporais. Em aplicações envolvendo a área de Finanças, evidências empíricas anteriores indicam que modelos de inteligência computacional são mais precisos, dada sua maior capacidade em capturar não-linearidades e outros fatos estilizados presentes em séries financeiras. Nesse sentido, este artigo investiga a hipótese de que os modelos matemáticos de redes neurais perceptron multicamadas, redes neurais função de base radial e o sistema nebuloso Takagi-Sugeno (TAKAGI; SUGENO, 1985 são capazes de fornecer uma previsão fora-da-amostra mais acurada que os modelos auto-regressivos de médias móveis (ARMA e auto-regressivo de médias móveis supondo heterocedasticidade condicional auto-regressiva (ARMA-GARCH. O desempenho de previsão um-passo-à-frente dos modelos foi comparado utilizando-se séries de retorno da taxa de câmbio real/dólar (R$/US$ com freqüências de 15 minutos, 60 minutos, 120 minutos, diária e semanal. Resultados indicam que o desempenho dos modelos está diretamente relacionado à freqüência observada das séries. Além disso, os modelos de redes neurais obtiveram um desempenho superior em relação aos demais modelos considerados. A avaliação da estratégia de negociação estabelecida com base nas previsões geradas pelos modelos indicou que estratégias baseadas em modelos de redes neurais forneceram retornos superiores em relação àquelas baseadas em modelos ARMA e ARMA-GARCH e também em relação à estratégia buy-and-hold.Computational intelligence approaches, such as artificial neural networks and fuzzy systems, have become popular tools in approximating complicated nonlinear systems and time series forecasting. In Finance applications, there is

Bio indication of water quality in the Bogota Sabane using fuzzy logic sagging and aquatic micro invertebrates

International Nuclear Information System (INIS)

Gutierrez, Juan David; Riss Wolfgang; Ospina Rodolfo

2002-01-01

An application of the Sagging-type fuzzy logic to calculate biological water quality in Bogota, Colombia is presented 28 sites corresponding to 9 watersheds in the Bogota area were used. The organisms selected were: Leptoceridae and Hidrobiosidae as indicators of clean waters, Planariidae and Amphipoda as indicators of polluted waters and Psychodida and Syrphidae as indicators of highly polluted waters Chironomids were also included. In order to prove the degree of reliability of Sugeno-type fuzzy logic, the results obtained were compared with values for the Cfq index, and a highly significant correlation was obtained

Simulation of Intelligent Single Wheel Mobile Robot

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Maki K. Rashid

2008-11-01

Full Text Available Stabilization of a single wheel mobile robot attracted researcher attentions in robotic area. However, the budget requirements for building experimental setups capable in investigating isolated parameters and implementing others encouraged the development of new simulation methods and techniques that beat such limitations. In this work we have developed a simulation platform for testing different control tactics to stabilize a single wheel mobile robot. The graphic representation of the robot, the dynamic solution, and, the control scheme are all integrated on common computer platform using Visual Basic. Simulation indicates that we can control such robot without knowing the detail of it's internal structure or dynamics behaviour just by looking at it and using manual operation tactics. Twenty five rules are extracted and implemented using Takagi-Sugeno's fuzzy controller with significant achievement in controlling robot motion during the dynamic simulation. The resulted data from the successful implementation of the fuzzy model are used to utilize and train a neurofuzzy controller using ANFIS scheme to produce further improvement in robot performance

Aplikasi Sistem Pendukung Keputusan Stadium Penyakit Kanker Payudara Menggunakan Logika Fuzzy Berbasis Android

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Rico Adrial

2017-08-01

Full Text Available Kanker payudara dapat lebih mudah diatasi saat masih stadium dini. Langkah awal dari pengobatan kanker payudara adalah dengan mendeteksi apakah gejala yang muncul pada tubuh pasien benar-benar merupakan sel kanker. Stadium kanker adalah hasil diagnosis dari fisikawan medis sehingga memudahkan pemahaman tentang penyakit pasien. Pada penelitian ini digunakan Fuzzy Logic sebagai metode yang membantu pada sistem pendukung keputusan dalam menentukan stadium kanker payudara. Saat ini banyak aplikasi android yang dibuat sebagai penyedia informasi di bidang kesehatan. Penggabungan ilmu di bidang kesehatan. Fuzzy logic dan android diharapkan menjadi sebuah aplikasi mutakhir dalam membuat keputusan di bidang kesehatan. Sistem pendukung keputusan stadium kanker payudara telah dirancang. Dapat disimpulkan bahwa aplikasi ini dapat mengkalkulasi berdasarkan fuzzy logic metode Sugeno dan telah di uji baik secara penghitungan pada matlab maupun manual.

Comparison between genetic fuzzy system and neuro fuzzy system to select oil wells for hydraulic fracturing; Comparacao entre genetic fuzzy system e neuro fuzzy system para selecao de pocos de petroleo para fraturamento hidraulico

Energy Technology Data Exchange (ETDEWEB)

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)

Fuzzy controller of speed-power of a synchronous micro generator; Controlador difuso de velocidad-potencia de un microgenerador sincrono

Energy Technology Data Exchange (ETDEWEB)

Martinez Alvarado, Raziel

2002-11-01

This thesis shows the design and implementation of a speed-power fuzzy-logic controller. The controller implementation was carried out on the Schrage motor-synchronous generator set. The synchronous alternator is rated 7 kVA, 220 V, 1800 rpm, 60 Hz. Two PI like fuzzy-logic controllers were developed with 9 and 25 rules. The controllers use the speed or power error and its integral as input variables and as an output the control signal from the brush-positioner of the Schrage motor. At the controller design stage, the anfis (adaptive-network-based fuzzy inference system) learning and structure procedure was used for tuning up parameters of the membership functions used on the designed fuzzy controllers. These controllers are first-order Sugeno-type. The designed controllers were tested on the motor-generator set under loaded and no-loaded conditions. It was found that PI-9 rules fuzzy-logic controller had better performance on both operating conditions. [Spanish] En este trabajo de tesis se presenta el diseno e implementacion de un controlador difuso de velocidad-potencia, para un grupo motor Schrage-generador sincrono de 7 kVA, 220 V, 1800 rpm, 60 Hz. Se implementaron controladores difusos del tipo PI de 9 y 25 reglas. Estos controladores utilizan como variables de entrada el error y la integral del error, de velocidad o potencia segun corresponda, y como variable de salida la senal de control del posicionador de las escobillas del motor Schrage. En la etapa de diseno de los controladores, se utilizo la estructura y el procedimiento de aprendizaje anfis (Sistema de Inferencia Difuso Basado en Redes Adaptables, por sus siglas en ingles) para sintonizar los parametros de las funciones de membresia de los controladores difusos, los cuales son del tipo Sugeno de primer orden. Con la finalidad de validar los controladores disenados, se realizaron pruebas experimentales al grupo motor-generador en condiciones de vacio y carga. Se encontro que el controlador difuso tipo

Relative evaluation on decommissioning accident scenarios of nuclear facilities

International Nuclear Information System (INIS)

Jeong, Kwan-Seong; Choi, Byung-Seon; Moon, Jei-Kwon; Hyun, Dong-Jun; Kim, Geun-Ho; Kim, Tae-Hyoung; Jo, Kyung-Hwa; Seo, Jae-Seok; Jeong, Seong-Young; Lee, Jung-Jun

2012-01-01

Highlights: ► This paper suggests relative importance on accident scenarios during decommissioning of nuclear facilities. ► The importance of scenarios can be performed by using AHP and Sugeno fuzzy method. ► The AHP and Sugeno fuzzy method guarantee reliability of the importance evaluation. -- Abstract: This paper suggests the evaluation method of relative importance on accident scenarios during decommissioning of nuclear facilities. The evaluation method consists of AHP method and Sugeno fuzzy integral method. This method will guarantee the reliability of relative importance evaluation for decommissioning accident scenarios.

Application of fuzzy logic in nuclear reactor control Part I: An assessment of state-of-the-art

International Nuclear Information System (INIS)

Herger, A.S.; Jamshidl, M.; Alang-Rashid, N.K.

1995-01-01

This article discusses the application of fuzzy logic to nuclear reactor control. The method has been suggested by many investigators in many control applications. Reviews of the application of fuzzy logic in process control are given by Tong and Sugeno. Because fuzzy logic control (FLC) provides a pathway for transforming human abstractions into the numerical domain, it has the potential to assist nuclear reactor operators in the control room. With this transformation, linguistically expressed control principles can be coded into the fuzzy controller rule base. Having acquired the skill of the operators, the FLC can assist an operator in controlling the complex system. The thrust of FLC is to derive a conceptual model of the control operation, without expressing the process as mathematical equations, to assist the human operator in interpreting incoming plant variables and arriving at a proper control action. To introduce the concept of FLC in nuclear reactor operation, an overview of the mythology and a review of its application in both nuclear and nonnuclear control application domains are presented along with subsequent discussion of fuzzy logic controllers, their structures, and their method of information processing. The article concludes with the application of a tunable FLC to a typical reactor control problem

Application of fuzzy logic in nuclear reactor control: Part 1: An assessment of state-of-the-art

International Nuclear Information System (INIS)

Heger, A.S.; Alang-Rashid, N.K.; Jamshidi, M.

1995-01-01

This article discusses the application of fuzzy logic of nuclear reactor control. The method has been suggested by many investigators in many control applications. Reviews of the application of fuzzy logic in process control are given by Tong and Sugeno. Because fuzzy logic control (FLC) provides a pathway for transforming human abstractions into the numerical domain, it has the potential to assist nuclear reactor operators in the control room. With this transformation, linguistically expressed control principles can be coded into the fuzzy controller rule base. Having acquired the skill of he operators, the FLC can assist an operator in controlling the complex system. The thrust of FLC is to derive a conceptual model of the control operation, without expressing the process as mathematical equations, to assist the human operator in interpreting incoming plant variables and arriving at a proper control action. To introduce the concept of FLC in nuclear reactor operation, an overview of the mythology and a review of its application in both nuclear and nonnuclear control application domains are presented along with subsequent discussion of fuzzy logic controllers, their structures, and their method of information processing. The article concludes with the application of a tunable FLC to a typical reactor control problem. 49 refs., 9 figs., 3 tabs

Design of interpretable fuzzy systems

CERN Document Server

Cpałka, Krzysztof

2017-01-01

This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.

Approximations of Fuzzy Systems

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Vinai K. Singh

2013-03-01

Full Text Available A fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. Such results can be viewed as an existence of optimal fuzzy systems. Li-Xin Wang discussed a similar problem using Gaussian membership function and Stone-Weierstrass Theorem. He established that fuzzy systems, with product inference, centroid defuzzification and Gaussian functions are capable of approximating any real continuous function on a compact set to arbitrary accuracy. In this paper we study a similar approximation problem by using exponential membership functions

Estudo comparativo de controladores de Mamdani e Sugeno para controle de tráfego em interseções isoladas

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Michelle Andrade

2009-10-01

Full Text Available

Na definição de um controlador

fuzzy que modele o conhecimento do especialista e promova o controle desejado de um determinado sistema, o projetista precisa selecionar, inicialmente, o tipo básico de controlador a ser projetado. Isto é, precisa definir se o controlador será do tipo proposto por Mamdani ou Sugeno. Na literatura são encontrados controladores semafóricos fuzzy dos dois tipos sem que, no entanto, referência seja feita às razões que levaram à adoção de um tipo em detrimento do outro. Visando cobrir essa lacuna, este trabalho analisa as principais características dos dois tipos de controladores, e compara as respostas e os efeitos dos mesmos no controle de diferentes volumes de tráfego em uma interseção isolada. Os resultados mostram que o controlador de Sugeno apresenta nítidas vantagens sobre o de Mamdani para a aplicação considerada.

Control predictivo basado en predictores borrosos

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MIGUEL ANGEL RODRIGUEZ BORROTO

2007-01-01

Full Text Available En el presente artículo se expone un algoritmo para aplicar las técnicas de control predictivo lineal basadas en el modelo (MPC al caso de procesos no lineales utilizando un predictor borroso (fuzzy, en base a la estructura Takagi_Sugeno_Kang_dinámica y bajo el principio de utilización de modelos lineales locales por tramos. Ello conduce inherentemente, a un proceso de adaptación de la matriz dinámica del sistema en cada periodo de muestreo, lo cual se considera novedoso en relación al MPC clásico. Se exponen los resultados de su aplicación al caso de un control de posición que utiliza un servo-motor de corriente continua con zona muerta y a un reactor continuo de tanque con agitador con reacción exotérmica. Los resultados son satisfactorios

CF-Pursuit: A Pursuit Method with a Clothoid Fitting and a Fuzzy Controller for Autonomous Vehicles

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Yunxiao Shan

2015-09-01

Full Text Available Simple and efficient geometric controllers, like Pure-Pursuit, have been widely used in various types of autonomous vehicles to solve tracking problems. In this paper, we have developed a new pursuit method, named CF-Pursuit, which has been based on Pure-Pursuit but with certain differences. In CF-Pursuit, in order to reduce fitting errors, we used a clothoid C1 curve to replace the circle employed in Pure-Pursuit. This improvement to the fitting method helps the Pursuit method to decrease tracking errors. As regards the selection of look-ahead distance, we employed a fuzzy system to directly consider the path's curvature. There are three input variables in this fuzzy system, 6mcurvature, 9mcurvature and 12mcurvature, calculated from the clothoid fit with the current position and the goal position on the defined path. A Sugeno fuzzy model was adapted to output a reasonable look-ahead distance using the experiences of human drivers as well as our own tests. Compared with some other geometric controllers, CF-Pursuit performs better in robustness, cross track errors and stability. The results from field tests have proven the CF-Pursuit is a practical and efficient geometric method for the path tracking problems of autonomous vehicles.

Fuzzy logic control and optimization system

Science.gov (United States)

Lou, Xinsheng [West Hartford, CT

2012-04-17

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Further studies on stability analysis of nonlinear Roesser-type two-dimensional systems

International Nuclear Information System (INIS)

Dai Xiao-Lin

2014-01-01

This paper is concerned with further relaxations of the stability analysis of nonlinear Roesser-type two-dimensional (2D) systems in the Takagi–Sugeno fuzzy form. To achieve the goal, a novel slack matrix variable technique, which is homogenous polynomially parameter-dependent on the normalized fuzzy weighting functions with arbitrary degree, is developed and the algebraic properties of the normalized fuzzy weighting functions are collected into a set of augmented matrices. Consequently, more information about the normalized fuzzy weighting functions is involved and the relaxation quality of the stability analysis is significantly improved. Moreover, the obtained result is formulated in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed result. (general)

Combined heuristic with fuzzy system to transmission system expansion planning

Energy Technology Data Exchange (ETDEWEB)

Silva Sousa, Aldir; Asada, Eduardo N. [University of Sao Paulo, Sao Carlos School of Engineering, Department of Electrical Engineering Av. Trabalhador Sao-carlense, 400, 13566-590 Sao Carlos, SP (Brazil)

2011-01-15

A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (author)

Kompensasi Kesalahan Sensor Berbasis Descriptor dengan Performa H_inf pada Winding Machine

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Hendra Antomy

2015-12-01

Full Text Available Kesalahan pada sensor dapat terjadi pada sistem kontrol dengan umpan balik sehingga mengakibatkan sistem mengalami penurunan stabilitas dan performa. Fault Tolerant Control (FTC adalah metode untuk mengkompensasi kesalahan pada komponen sistem, salah satunya adalah kesalahan sensor. FTC dapat disusun dengan cara mendesain estimator untuk mengestimasi besarnya kesalahan sensor yang terjadi. Kompensasi dilakukan dengan cara mengurangkan estimasi kesalahan sensor dengan keluaran sistem. Pada makalah ini, FTC untuk kesalahan sensor diterapkan pada sistem winding machine. Estimator dirancang menggunakan pendekatan sistem descriptor dan didesain memenuhi performa H_inf. Permasalahan dalam desain estimator dirumuskan dalam bentuk Linear Matrix Inequality (LMI. Untuk merancang kontroler nominal, sistem winding machine direpresentasikan sebagai model fuzzy Takagi-Sugeno (T-S. Berdasarkan model tersebut, aturan kontroler disusun menggunakan konsep Parallel Distributed Compensation (PDC dengan struktur kontrol servo tipe 1. Hasil simulasi menunjukkan bahwa kompensasi yang diberikan dapat menjaga performa dan stabilitas sistem saat terjadi kesalahan sensor. Selain itu, estimator memenuhi performa H_inf dengan L2-Gain kurang dari tingkat pelemahan yang ditentukan.

Designing PID-Fuzzy Controller for Pendubot System

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Ho Trong Nguyen

2017-12-01

Full Text Available In the paper, authors analize dynamic equation of a pendubot system. Familiar kinds of controller – PID, fuzzy controllers – are concerned. Then, a structure of PID-FUZZY is presented. The comparison of three kinds of controllers – PID, fuzzy and PID-FUZZY shows the better response of system under PID-FUZZY controller. Then, the experiments on the real model also prove the better stabilization of the hybrid controller which is combined between linear and intelligent controller.

Fuzzy expert systems using CLIPS

Science.gov (United States)

Le, Thach C.

1994-01-01

This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.

Stability analysis of nonlinear Roesser-type two-dimensional systems via a homogenous polynomial technique

Science.gov (United States)

Zhang, Tie-Yan; Zhao, Yan; Xie, Xiang-Peng

2012-12-01

This paper is concerned with the problem of stability analysis of nonlinear Roesser-type two-dimensional (2D) systems. Firstly, the fuzzy modeling method for the usual one-dimensional (1D) systems is extended to the 2D case so that the underlying nonlinear 2D system can be represented by the 2D Takagi—Sugeno (TS) fuzzy model, which is convenient for implementing the stability analysis. Secondly, a new kind of fuzzy Lyapunov function, which is a homogeneous polynomially parameter dependent on fuzzy membership functions, is developed to conceive less conservative stability conditions for the TS Roesser-type 2D system. In the process of stability analysis, the obtained stability conditions approach exactness in the sense of convergence by applying some novel relaxed techniques. Moreover, the obtained result is formulated in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, a numerical example is also given to demonstrate the effectiveness of the proposed approach.

Stability analysis of nonlinear Roesser-type two-dimensional systems via a homogenous polynomial technique

International Nuclear Information System (INIS)

Zhang Tie-Yan; Zhao Yan; Xie Xiang-Peng

2012-01-01

This paper is concerned with the problem of stability analysis of nonlinear Roesser-type two-dimensional (2D) systems. Firstly, the fuzzy modeling method for the usual one-dimensional (1D) systems is extended to the 2D case so that the underlying nonlinear 2D system can be represented by the 2D Takagi—Sugeno (TS) fuzzy model, which is convenient for implementing the stability analysis. Secondly, a new kind of fuzzy Lyapunov function, which is a homogeneous polynomially parameter dependent on fuzzy membership functions, is developed to conceive less conservative stability conditions for the TS Roesser-type 2D system. In the process of stability analysis, the obtained stability conditions approach exactness in the sense of convergence by applying some novel relaxed techniques. Moreover, the obtained result is formulated in the form of linear matrix inequalities, which can be easily solved via standard numerical software. Finally, a numerical example is also given to demonstrate the effectiveness of the proposed approach. (general)

Análisis de Estabilidad de Sistemas Borrosos

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Basil M. Al-Hadithi

2007-04-01

Full Text Available Resumen: En este trabajo se revisa el estado del arte sobre estabilidad de sistemas borrosos, poniéndose de manifiesto las dificultades para su análisis, debido a la característica falta de linealidad de los mismos. Se revisan los estudios basados en el criterio del círculo, las técnicas para calcular Índices de estabilidad, así como técnicas basadas en aplicación del teorema de estabilidad de Lyapunov, que permite utilizar métodos númericos de búsqueda de soluciones. Además, se revisan los trabajos de estabilidad mediante el uso del modelo borroso de Takagi-Sugeno (T-S, el enfoque de las Desigualdades Matriciales Lineales (LMI, que ha tenido un interés creciente en los últimos años, así como otra línea de investigación basada en estabilidad energética. Palabras clave: Sistemas borrosos, Estabilidad global y asintótica, Modelo borroso afín de Takagi-Sugeno

Fuzzy systems for process identification and control

International Nuclear Information System (INIS)

Gorrini, V.; Bersini, H.

1994-01-01

Various issues related to the automatic construction and on-line adaptation of fuzzy controllers are addressed. A Direct Adaptive Fuzzy Control (this is an adaptive control methodology requiring a minimal knowledge of the processes to be coupled with) derived in a way reminiscent of neurocontrol methods, is presented. A classical fuzzy controller and a fuzzy realization of a PID controller is discussed. These systems implement a highly non-linear control law, and provide to be quite robust, even in the case of noisy inputs. In order to identify dynamic processes of order superior to one, we introduce a more complex architecture, called Recurrent Fuzzy System, that use some fuzzy internal variables to perform an inferential chaining.I

Fuzzy logic control for camera tracking system

Science.gov (United States)

Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

1992-01-01

A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

Modeling and control of an unstable system using probabilistic fuzzy inference system

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Sozhamadevi N.

2015-09-01

Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

New approach to solve symmetric fully fuzzy linear systems

Indian Academy of Sciences (India)

concepts of fuzzy set theory and then define a fully fuzzy linear system of equations. .... To represent the above problem as fully fuzzy linear system, we represent x .... Fully fuzzy linear systems can be solved by Linear programming approach, ...

A neuro-fuzzy inference system for sensor monitoring

International Nuclear Information System (INIS)

Na, Man Gyun

2001-01-01

A neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods has been developed to monitor the relevant sensor using the information of other sensors. The paramters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors

Design of fuzzy systems using neurofuzzy networks.

Science.gov (United States)

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Solving Fully Fuzzy Linear System of Equations in General Form

Directory of Open Access Journals (Sweden)

A. Yousefzadeh

2012-06-01

Full Text Available In this work, we propose an approach for computing the positive solution of a fully fuzzy linear system where the coefficient matrix is a fuzzy $nimes n$ matrix. To do this, we use arithmetic operations on fuzzy numbers that introduced by Kaffman in and convert the fully fuzzy linear system into two $nimes n$ and $2nimes 2n$ crisp linear systems. If the solutions of these linear systems don't satisfy in positive fuzzy solution condition, we introduce the constrained least squares problem to obtain optimal fuzzy vector solution by applying the ranking function in given fully fuzzy linear system. Using our proposed method, the fully fuzzy linear system of equations always has a solution. Finally, we illustrate the efficiency of proposed method by solving some numerical examples.

Fuzzy logic for structural system control

Directory of Open Access Journals (Sweden)

Herbert Martins Gomes

Full Text Available This paper provides some information and numerical tests that aims to investigate the use of a Fuzzy Controller applied to control systems. Some advantages are reported regarding the use of this controller, such as the characteristic ease of implementation due to its semantic feature in the statement of the control rules. On the other hand, it is also hypothesized that these systems have a lower performance loss when the system to be controlled is nonlinear or has time varying parameters. Numerical tests are performed using modal LQR optimal control and Fuzzy control of non-collocated systems with full state feedback in a two-dimensional structure. The paper proposes a way of designing a controller that may be a supervisory Fuzzy controller for a traditional controller or even a fuzzy controller independent from the traditional control, consisting on individual mode controllers. Some comments are drawn regarding the performance of these proposals in a number of arrangements.

Fuzzy control for optimal operation of complex chilling systems; Betriebsoptimierung von komplexen Kaelteanlagen mit Fuzzy-Control

Energy Technology Data Exchange (ETDEWEB)

Talebi-Daryani, R. [Fachhochschule Koeln (Germany). Lehrgebiet und Lab. fuer Regelungs- und Gebaeudeleittechnik; Luther, C. [JCI Regelungstechnik GmbH, Koeln (Germany)

1998-05-01

The optimization potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. The aim of this project is to replace inefficient PLC-solutions for the operation of chilling system by a Fuzzy control system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation- and consumption costs. Analysis of the thermal behaviour of the building and the chilling system is necessary, in order to find the current efficient cooling potentials and cooling methods during the operation. Three different Fuzzy controller have been developed with a total rule number of just 70. This realized Fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. This new Fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. Comparison of the system behaviour before and after the implementation of Fuzzy control system proved the benefits of the Fuzzy logic based operation system realized here. The system described here is a joint project between the University of applied sciences Cologne, and Johnson Controls International Cologne. The Fuzzy software tool used here (SUCO soft Fuzzy TECH 4.0), was provided by Kloeckner Moeller Bonn. (orig.) [Deutsch] Die Betriebsoptimierung von Kaelteanlagen innerhalb von Gebaeudeleitsystemen ist auf die Faehigkeiten von logischen Steuerverknuepfungen der Digitaltechnik begrenzt. In diesem Zusammenhang kann nur ein geringer Anteil der Information ueber das thermische Speicherverhalten des jeweiligen Gebaeudes herangezogen werden. Ziel des vorliegenden Projektes war es, die unzureichenden logischen Steuerverknuepfungen durch ein Fuzzy-Control-System zu ersetzen, um die Arbeitsweise der Kaelteanlage zu optimieren. Die Optimierungskriterien dieses

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

International Nuclear Information System (INIS)

Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

2012-01-01

The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

On-line tuning of a fuzzy-logic power system stabilizer

International Nuclear Information System (INIS)

Hossein-Zadeh, N.; Kalam, A.

2002-01-01

A scheme for on-line tuning of a fuzzy-logic power system stabilizer is presented. firstly, a fuzzy-logic power system stabilizer is developed using speed deviation and accelerating power as the controller input variables. The inference mechanism of fuzzy-logic controller is represented by a decision table, constructed of linguistic IF-THEN rules. The Linguistic rules are available from experts and the design procedure is based on these rules. It assumed that an exact model of the plant is not available and it is difficult to extract the exact parameters of the power plant. Thus, the design procedure can not be based on an exact model. This is an advantage of fuzzy logic that makes the design of a controller possible without knowing the exact model of the plant. Secondly, two scaling parameters are introduced to tune the fuzzy-logic power system stabilizer. These scaling parameters are the outputs of another fuzzy-logic system, which gets the operating conditions of power system as inputs. These mechanism of tuning the fuzzy-logic power system stabilizer makes the fuzzy-logic power system stabilizer adaptive to changes in the operating conditions. Therefore, the degradation of the system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. The tuned stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The responses are compared with a fixed parameters fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. It is shown that the tuned fuzzy-logic power system stabilizer is superior to both of them

Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems

Directory of Open Access Journals (Sweden)

Vasile MAZILESCU

2010-12-01

Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.

IMPLEMENTASI SISTEM NAVIGASI ROBOT WALL FOLLOWING DENGAN METODE FUZZY LOGIC UNTUK ROBOT PEMADAM API DIVISI BERKAKI ONIX II PADA KRPAI TAHUN 2017

Directory of Open Access Journals (Sweden)

Yusuf - Hasyim

2017-05-01

Full Text Available Fuzzy logic adalah suatu logika yang memiliki derajat keanggotaan dalam rentan 0 sampai 1, logikafuzzy di gunakan untuk menerjemahkan suatu besaran yang diekspresikan menggunakan bahasa (linguistik.Navigasi Wall following merupakan salah satu sistem navigasi robot yang digunakan dalam perlombaanseperti Kontes Robot Pemadam Api Indonesia (KRPAI dimana robot tipe wall follower ini diharuskan dapatmengikuti kontur dinding arena. Robot tipe ini dipilih karena arena perlombaan dari Kontes Robot PemadamApi Indonesia (KRPAI terdiri dari dinding-dinding yang membentuk lorong dan ruangan. Penelitian inimerancang dan mengimplementasikan algoritma kendali pada robot wall follower bekaki ONIX II KRPAI 2017dengan menggunakan metode fuzzy logic model Sugeno, sehingga robot dapat bernavigasi dengan mengambilkeputusan bergerak lurus, belok kanan, belok kiri, berhenti, mundur. Sehingga robot dapat melaksanakantugasnya dengan optimal .

Fuzzy logic approach for energetic and economic evaluation of hydroelectric projects

International Nuclear Information System (INIS)

Iliev, Atanas M.

2003-01-01

A mathematical model for energetic and economic evaluation of hydroelectric projects is developed. The main advantage of the proposed methodology is that the model considers uncertainty and vagueness which appears during the decision making process. Due to modeling of variables that are non statistical in their character, fuzzy logic approach is fully incorporated in the model. The first step in energetic evaluation of the hydro power projects is determination of the characteristic of the efficiency of the units to be installed in hydro power plants. For this purpose the model which uses the best characteristics of Artificial Network Fuzzy Inference System (ANFIS) is applied. The method is tested on real systems: HPP Tikves- the power plant in operation and HPP Kozjak - the power plant in construction. The results obtained from practical implementation show that the proposed approach gives superior results than classical polynomial approximation. The model for determining the consumption characteristic of hydro power plant is developed by Sugeno Fuzzy Logic System with polynomials in the consequent part of the rules. Model takes into account the variable gross head of HPP, as well as, the number of units which will be in operation for given output. Modeling of the gross head and power output are performed by expert's design membership functions. This model is practically applied on HPP Tikves for determination of the consumption characteristic for several gross head. The plausible yearly production of electricity from hydro power project, which is important for estimation of the benefit from the project, is calculated by mixed fuzzy-statistical model. hi this approach fuzzy set of the inflow is constructed according to the statistical parameters. The calculation of the production of electricity is realized for a several hydrological conditions which are described by linguistic variables. Finally, Mamdani Fuzzy Inference System with fuzzy number in consequent part

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

Science.gov (United States)

Lam, H K

2012-02-01

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

Fuzzy production planning models for an unreliable production system with fuzzy production rate and stochastic/fuzzy demand rate

Directory of Open Access Journals (Sweden)

K. A. Halim

2011-01-01

Full Text Available In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.

Fuzzy systems and soft computing in nuclear engineering

International Nuclear Information System (INIS)

Ruan, D.

2000-01-01

This book is an organized edited collection of twenty-one contributed chapters covering nuclear engineering applications of fuzzy systems, neural networks, genetic algorithms and other soft computing techniques. All chapters are either updated review or original contributions by leading researchers written exclusively for this volume. The volume highlights the advantages of applying fuzzy systems and soft computing in nuclear engineering, which can be viewed as complementary to traditional methods. As a result, fuzzy sets and soft computing provide a powerful tool for solving intricate problems pertaining in nuclear engineering. Each chapter of the book is self-contained and also indicates the future research direction on this topic of applications of fuzzy systems and soft computing in nuclear engineering. (orig.)

Efficient fuzzy logic controller for magnetic levitation systems | Shu ...

African Journals Online (AJOL)

In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC) is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input ...

Advances in type-2 fuzzy sets and systems theory and applications

CERN Document Server

Mendel, Jerry; Tahayori, Hooman

2013-01-01

This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet.  The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.

Uncertain rule-based fuzzy systems introduction and new directions

CERN Document Server

Mendel, Jerry M

2017-01-01

The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy se...

Expert system driven fuzzy control application to power reactors

International Nuclear Information System (INIS)

Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.

1990-01-01

For the purpose of nonlinear control and uncertainty/imprecision handling, fuzzy controllers have recently reached acclaim and increasing commercial application. The fuzzy control algorithms often require a ''supervisory'' routine that provides necessary heuristics for interface, adaptation, mode selection and other implementation issues. Performance characteristics of an on-line fuzzy controller depend strictly on the ability of such supervisory routines to manipulate the fuzzy control algorithm and enhance its control capabilities. This paper describes an expert system driven fuzzy control design application to nuclear reactor control, for the automated start-up control of the Experimental Breeder Reactor-II. The methodology is verified through computer simulations using a valid nonlinear model. The necessary heuristic decisions are identified that are vitally important for the implemention of fuzzy control in the actual plant. An expert system structure incorporating the necessary supervisory routines is discussed. The discussion also includes the possibility of synthesizing the fuzzy, exact and combined reasoning to include both inexact concepts, uncertainty and fuzziness, within the same environment

A study of fuzzy control in nuclear scale system

International Nuclear Information System (INIS)

Wang Yu; Zhang Yongming; Wu Ruisheng; Du Xianbin; Liu Shixing

2001-01-01

The new development of the nuclear scale system which uses fuzzy control strategy is presented. Good results have been obtained in using fuzzy control to solve the problems, such as un-linearities, instabilities, time delays, which are difficultly described by formula, etc. The fuzzy variance, membership function and fuzzy rules are given, and the noise disturbances of fuzzy control and PID control are also given

Novel Fuzzy-Modeling-Based Adaptive Synchronization of Nonlinear Dynamic Systems

Directory of Open Access Journals (Sweden)

Shih-Yu Li

2017-01-01

Full Text Available In this paper, a novel fuzzy-model-based adaptive synchronization scheme and its fuzzy update laws of parameters are proposed to address the adaptive synchronization problem. The proposed fuzzy controller does not share the same premise of fuzzy system, and the numbers of fuzzy controllers is reduced effectively through the novel modeling strategy. In addition, based on the adaptive synchronization scheme, the error dynamic system can be guaranteed to be asymptotically stable and the true values of unknown parameters can be obtained. Two identical complicated dynamic systems, Mathieu-Van der pol system (M-V system with uncertainties, are illustrated for numerical simulation example to show the effectiveness and feasibility of the proposed novel adaptive control strategy.

A Recursive Fuzzy System for Efficient Digital Image Stabilization

Directory of Open Access Journals (Sweden)

Nikolaos Kyriakoulis

2008-01-01

Full Text Available A novel digital image stabilization technique is proposed in this paper. It is based on a fuzzy Kalman compensation of the global motion vector (GMV, which is estimated in the log-polar plane. The GMV is extracted using four local motion vectors (LMVs computed on respective subimages in the logpolar plane. The fuzzy Kalman system consists of a fuzzy system with the Kalman filter's discrete time-invariant definition. Due to this inherited recursiveness, the output results into smoothed image sequences. The proposed stabilization system aims to compensate any oscillations of the frame absolute positions, based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system, and thus the advantages of both the fuzzy Kalman system and the log-polar transformation are exploited. The described technique produces optimal results in terms of the output quality and the level of compensation.

APLIKASI FIS MODEL SUGENO PADA PENGENDALIAN VALVE UNTUK MANGATUR TINGGI LEVEL AIR

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Wahyudi Wahyudi

2012-02-01

Full Text Available Fuzzy control is one of the controller alternative using expert system ( human being logic as controller sothat do not need knowledge concerning parameter of systems. Fuzzy control also have ability of lightcomputing. This paper goal is making a Fuzzy control and observe the response of the plant had beencontrolled. The experiment’s purpose is to show the performance of Fuzzy control in control valve systemin order to liquid level controlling on-linely. Water level controlled by opening the load valve in the holdtank. The project experiment’s include examination conduct logic of Fuzzy program of Codevision AVR,examination of system at some references, examination the changing of liquid level reference, andexamination of system with the influence of disturbance. From the test, it is known that Fuzzy control withAVR ATmega8535 can yield eliminate offset in arranging opening of valve. Offset in examination ofsystem at some references are 0,4 cm , 0,02 cm , 0,17 cm, 0,11 cm, and 0,54 cm. Offset in examination thechanging of liquid levels are 0,07 cm, 0,02 cm, and 0,04 cm.

Fuzzy logic based variable speed wind generation system

Energy Technology Data Exchange (ETDEWEB)

Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.

1996-12-31

This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.

On Modeling the Behavior of Comparators for Complex Fuzzy Objects in a Fuzzy Object-Relational Database Management System

Directory of Open Access Journals (Sweden)

JuanM. Medina

2012-08-01

Full Text Available This paper proposes a parameterized definition for fuzzy comparators on complex fuzzy datatypes like fuzzy collections with conjunctive semantics and fuzzy objects. This definition and its implementation on a Fuzzy Object-Relational Database Management System (FORDBMS provides the designer with a powerful tool to adapt the behavior of these operators to the semantics of the considered application.

New approach to solve fully fuzzy system of linear equations using ...

Indian Academy of Sciences (India)

This paper proposes two new methods to solve fully fuzzy system of linear equations. The fuzzy system has been converted to a crisp system of linear equations by using single and double parametric form of fuzzy numbers to obtain the non-negative solution. Double parametric form of fuzzy numbers is defined and applied ...

Fuzzy Logic Based Autonomous Traffic Control System

Directory of Open Access Journals (Sweden)

Muhammad ABBAS

2012-01-01

Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.

Robust synthetic biology design: stochastic game theory approach.

Science.gov (United States)

Chen, Bor-Sen; Chang, Chia-Hung; Lee, Hsiao-Ching

2009-07-15

Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology. A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by non-linear stochastic game method directly, the Takagi-Sugeno (T-S) fuzzy model is proposed to approximate the non-linear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method. http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

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.

(VTEC) in the Ionosphere for GPS Observations

African Journals Online (AJOL)

Michael

2017-12-02

Dec 2, 2017 ... This paper focuses on the use of two different models for predicting ... has been one of the most attractive methods of ... of training algorithm and its associated parameters ... Sugeno-type fuzzy inference system in which the.

Intelligent control-II: review of fuzzy systems and theory of approximate reasoning

International Nuclear Information System (INIS)

Nagrial, M.H.

2004-01-01

Fuzzy systems are knowledge-based or rule-based systems. The heart of a fuzzy systems knowledge base consisting of the so-called fuzzy IF -THEN rules. This paper reviews various aspects of fuzzy IF-THEN rules. The theory of approximate reasoning, which provides a powerful framework for reasoning the imprecise and uncertain information, , is also reviewed. Additional properties of fuzzy systems are also discussed. (author)

A fuzzy expert system based on relations

International Nuclear Information System (INIS)

Hall, L.O.; Kandel, A.

1986-01-01

The Fuzzy Expert System (FESS) is an expert system which makes use of the theory of fuzzy relations to perform inference. Relations are very general and can be used for any application, which only requires different types of relations be implemented and used. The incorporation of fuzzy reasoning techniques enables the expert system to deal with imprecision in a well-founded manner. The knowledge is represented in relational frames. FESS may operate in either a forward chaining or backward chaining manner. It uses primarily implication and factual relations. A unique methodology for combination of evidence has been developed. It makes uses of a blackboard for communication between the various knowledge sources which may operate in parallel. The expert system has been designed in such a manner that it may be used for diverse applications

Entropy Based Feature Selection for Fuzzy Set-Valued Information Systems

Science.gov (United States)

Ahmed, Waseem; Sufyan Beg, M. M.; Ahmad, Tanvir

2018-06-01

In Set-valued Information Systems (SIS), several objects contain more than one value for some attributes. Tolerance relation used for handling SIS sometimes leads to loss of certain information. To surmount this problem, fuzzy rough model was introduced. However, in some cases, SIS may contain some real or continuous set-values. Therefore, the existing fuzzy rough model for handling Information system with fuzzy set-values needs some changes. In this paper, Fuzzy Set-valued Information System (FSIS) is proposed and fuzzy similarity relation for FSIS is defined. Yager's relative conditional entropy was studied to find the significance measure of a candidate attribute of FSIS. Later, using these significance values, three greedy forward algorithms are discussed for finding the reduct and relative reduct for the proposed FSIS. An experiment was conducted on a sample population of the real dataset and a comparison of classification accuracies of the proposed FSIS with the existing SIS and single-valued Fuzzy Information Systems was made, which demonstrated the effectiveness of proposed FSIS.

Uzawa method for fuzzy linear system

OpenAIRE

Ke Wang

2013-01-01

An Uzawa method is presented for solving fuzzy linear systems whose coefficient matrix is crisp and the right-hand side column is arbitrary fuzzy number vector. The explicit iterative scheme is given. The convergence is analyzed with convergence theorems and the optimal parameter is obtained. Numerical examples are given to illustrate the procedure and show the effectiveness and efficiency of the method.

A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

Directory of Open Access Journals (Sweden)

Mohammad Hossein Fazel Zarandi

2012-01-01

Full Text Available A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

Intelligent neural network and fuzzy logic control of industrial and power systems

Science.gov (United States)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

Adaptive fuzzy controller based MPPT for photovoltaic systems

International Nuclear Information System (INIS)

Guenounou, Ouahib; Dahhou, Boutaib; Chabour, Ferhat

2014-01-01

Highlights: • We propose a fuzzy controller with adaptive output scaling factor as a maximum power point tracker of photovoltaic system. • The proposed controller integrates two different rule bases defined on error and change of error. • Our controller can track the maximum power point with better performances when compared to its conventional counterpart. - Abstract: This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart

Classification of EEG Signals by Radial Neuro-Fuzzy Systems

Czech Academy of Sciences Publication Activity Database

Coufal, David

2006-01-01

Roč. 5, č. 2 (2006), s. 415-423 ISSN 1109-2777 R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z10300504 Keywords : neuro-fuzzy systems * radial fuzzy systems * data mining * hybrid systems Subject RIV: BA - General Mathematics

Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

Directory of Open Access Journals (Sweden)

H. Q. Hou

2014-06-01

Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.

DESCRIBING FUNCTION METHOD FOR PI-FUZZY CONTROLLED SYSTEMS STABILITY ANALYSIS

Directory of Open Access Journals (Sweden)

Stefan PREITL

2004-12-01

Full Text Available The paper proposes a global stability analysis method dedicated to fuzzy control systems containing Mamdani PI-fuzzy controllers with output integration to control SISO linear / linearized plants. The method is expressed in terms of relatively simple steps, and it is based on: the generalization of the describing function method for the considered fuzzy control systems to the MIMO case, the approximation of the describing functions by applying the least squares method. The method is applied to the stability analysis of a class of PI-fuzzy controlled servo-systems, and validated by considering a case study.

Diseño de un controlador difuso supervisor para la regulación de un convertidor conmutado elevador

Directory of Open Access Journals (Sweden)

Spartacus Gomáriz

2008-11-01

Full Text Available En este trabajo, se diseña un controlador difuso del tipo Takagi-Sugeno-Kang (TSK para un regulador elevador conmu­tado DC-DC que opera a diferentes tensiones en estado estacionario. Este controlador realiza tareas de supervisión mediante su característica zonal. Por un lado, el controlador asigna diferentes leyes lineales de control acorde al valor de tensión de régimen estacionario y de los límites de aplicación a perturbación en pequeña señal. Por otra parte, el controlador asegura un arranque correcto para alcanzar el valor deseado de tensión de estado estacionario, por medio de una adecuada saturación del modulador de ancho de pulso (PWM de acuerdo con los principios de control en modo deslizamiento. Como resultado, se logra un comportamiento ecualizado en pequeña señal y un arranque controlado para diferentes tensiones de estado estacionario; por tanto, se amplían las características del regulador. Se presentan resultados de simulación y experimen­tales realizados sobre un regulador elevador controlado por corriente, operando sobre dos valores de tensión diferentes de estado estacionario que validan el diseño. / A complete design of a Takagi-Sugeno-Kang (TSK fuzzy controller for a PWM boost DC-DC switching regulator operating at different steady-state voltages is presented. This controller performs a supervisory role through its region characteristic. On the one hand, the controller assigns different linear control laws according to the steady-state voltage value and limits their application to small-signal perturbations. On the other hand, the controller ensures a proper start-up to reach the desired steady-state voltage by means of properly saturating the PWM according to sliding-mode control principles. As a result, both an equalized small-signal behavior and a controlled start-up are achieved for different steady-state voltages, thus extending the features of the regulator. Simulation and experimental results

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

Keller, James M; Fogel, David B

2016-01-01

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

Hybrid Type II fuzzy system & data mining approach for surface finish

Directory of Open Access Journals (Sweden)

Tzu-Liang (Bill Tseng

2015-07-01

Full Text Available In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

Fuzzy fault diagnosis system of MCFC

Institute of Scientific and Technical Information of China (English)

Wang Zhenlei; Qian Feng; Cao Guangyi

2005-01-01

A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

A fuzzy logic pitch angle controller for power system stabilization

Energy Technology Data Exchange (ETDEWEB)

Jauch, Clemens; Cronin, Tom; Sorensen, Poul [Wind Energy Department, Riso National Laboratory, PO Box 49, DK-4000 Roskilde, (Denmark); Jensen, Birgitte Bak [Institute of Energy Technology, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg East, (Denmark)

2006-07-12

In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active-stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large-signal control of active-stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set-up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short-circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non-linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. (Author).

Fuzzy inference system for evaluating and improving nuclear power plant operating performance

International Nuclear Information System (INIS)

Guimaraes, Antonio Cesar F.; Lapa, Celso Marcelo Franklin

2003-01-01

This paper presents a fuzzy inference system (FIS) as an approach to estimate Nuclear Power Plant (NPP) performance indicators. The performance indicators for this study are the energy availability factor (EAF) and the planned (PUF) and unplanned unavailability factor (UUF). These indicators are obtained from a non analytical combination among the same operational parameters. Such parameters are, for example, environment impacts, industrial safety, radiological protection, safety indicators, scram rate, thermal efficiency, and fuel reliability. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF-THEN rules. The fuzzy inference engine uses these fuzzy IF-THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. The results demonstrated the potential of the fuzzy inference to generate a knowledge basis that correlate operations occurrences and NPP performance. The inference system became possible the development of the sensitivity studies, future operational condition previsions and may support the eventual corrections on operation of the plant

Row Reduced Echelon Form for Solving Fully Fuzzy System with Unknown Coefficients

Directory of Open Access Journals (Sweden)

Ghassan Malkawi

2014-08-01

Full Text Available This study proposes a new method for finding a feasible fuzzy solution in positive Fully Fuzzy Linear System (FFLS, where the coefficients are unknown. The fully fuzzy system is transferred to linear system in order to obtain the solution using row reduced echelon form, thereafter; the crisp solution is restricted in obtaining the positive fuzzy solution. The fuzzy solution of FFLS is included crisp intervals, to assign alternative values of unknown entries of fuzzy numbers. To illustrate the proposed method, numerical examples are solved, where the entries of coefficients are unknown in right or left hand side, to demonstrate the contributions in this study.

Coherence of Radial Implicative Fuzzy Systems with Nominal Consequents

Czech Academy of Sciences Publication Activity Database

Coufal, David

-, č. 4 (2006), s. 60-66 ISSN 1509-4553 R&D Projects: GA MŠk 1M0545 Institutional research plan: CEZ:AV0Z10300504 Keywords : implicative fuzzy system * radial fuzzy system * nominal output space * coherence Subject RIV: IN - Informatics, Computer Science

Fuzzy expert systems models for operations research and management science

Science.gov (United States)

Turksen, I. B.

1993-12-01

Fuzzy expert systems can be developed for the effective use of management within the domains of concern associated with Operations Research and Management Science. These models are designed with: (1) expressive powers of representation embedded in linguistic variables and their linguistic values in natural language expressions, and (2) improved methods of interference based on fuzzy logic which is a generalization of multi-valued logic with fuzzy quantifiers. The results of these fuzzy expert system models are either (1) approximately good in comparison with their classical counterparts, or (2) much better than their counterparts. Moreover, for fuzzy expert systems models, it is only necessary to obtain ordinal scale data. Whereas for their classical counterparts, it is generally required that data be at least on ratio and absolute scale in order to guarantee the additivity and multiplicativity assumptions.

Radial Fuzzy Systems

Czech Academy of Sciences Publication Activity Database

Coufal, David

2017-01-01

Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

International Nuclear Information System (INIS)

Poursamad, Amir; Markazi, Amir H.D.

2009-01-01

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.

Application of fuzzy control in cooling systems save energy design

Energy Technology Data Exchange (ETDEWEB)

Chen, M.L.; Liang, H.Y. [Chienkuo Technology Univ., Changhua, Taiwan (China). Dept. of Electrical Engineering

2005-07-01

A fuzzy logic programmable logic controller (PLC) was used to control the cooling systems of frigorific equipment. Frigorific equipment is used to move unwanted heat outside of building in order to control indoor temperatures. The aim of the fuzzy logic PLC was to improve the energy efficiency of the cooling system. Control of the cooling pump and cooling tower in the system was based on the water temperature of the condenser during frigorific system operation. A human computer design for the cooling system control was used to set speeds and to automate and adjust the motor according to the fuzzy logic controller. It was concluded that if fuzzy logic controllers are used with all components of frigorific equipment, energy efficiency will be significantly increased. 5 refs., 3 tabs., 9 figs.

Fuzzy self-learning control for magnetic servo system

Science.gov (United States)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Minimal solution of general dual fuzzy linear systems

International Nuclear Information System (INIS)

Abbasbandy, S.; Otadi, M.; Mosleh, M.

2008-01-01

Fuzzy linear systems of equations, play a major role in several applications in various area such as engineering, physics and economics. In this paper, we investigate the existence of a minimal solution of general dual fuzzy linear equation systems. Two necessary and sufficient conditions for the minimal solution existence are given. Also, some examples in engineering and economic are considered

Hierarchical type-2 fuzzy aggregation of fuzzy controllers

CERN Document Server

Cervantes, Leticia

2016-01-01

This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.

Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty

CERN Document Server

Starczewski, Janusz T

2013-01-01

This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory.            In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...

Solution of the fully fuzzy linear systems using iterative techniques

International Nuclear Information System (INIS)

Dehghan, Mehdi; Hashemi, Behnam; Ghatee, Mehdi

2007-01-01

This paper mainly intends to discuss the iterative solution of fully fuzzy linear systems which we call FFLS. We employ Dubois and Prade's approximate arithmetic operators on LR fuzzy numbers for finding a positive fuzzy vector x-tilde which satisfies A-tildex-tilde=b, where A-tilde and b-tilde are a fuzzy matrix and a fuzzy vector, respectively. Please note that the positivity assumption is not so restrictive in applied problems. We transform FFLS and propose iterative techniques such as Richardson, Jacobi, Jacobi overrelaxation (JOR), Gauss-Seidel, successive overrelaxation (SOR), accelerated overrelaxation (AOR), symmetric and unsymmetric SOR (SSOR and USSOR) and extrapolated modified Aitken (EMA) for solving FFLS. In addition, the methods of Newton, quasi-Newton and conjugate gradient are proposed from nonlinear programming for solving a fully fuzzy linear system. Various numerical examples are also given to show the efficiency of the proposed schemes

Study on Design of Control Module and Fuzzy Control System

International Nuclear Information System (INIS)

Lee, Chang Kyu; Sohn, Chang Ho; Kim, Jung Seon; Kim, Min Kyu

2005-01-01

Performance of control unit is improved by introduction of fuzzy control theory and compensation for input of control unit as FLC(Fuzzy Logic Controller). Here, FLC drives thermal control system by linguistic rule-base. Hence, In case of using compensative PID control unit, it doesn't need to revise or compensate for PID control unit. Consequently, this study shows proof that control system which implements H/W module and then uses fuzzy algorism in this system is stable and has reliable performance

Hierarchical fuzzy control of low-energy building systems

Energy Technology Data Exchange (ETDEWEB)

Yu, Zhen; Dexter, Arthur [Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ (United Kingdom)

2010-04-15

A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profile can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)

Fuzzy Logic Temperature Control System For The Induction Furnace

Directory of Open Access Journals (Sweden)

Lei Lei Hnin

2015-08-01

Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.

Combining fuzzy mathematics with fuzzy logic to solve business management problems

Science.gov (United States)

Vrba, Joseph A.

1993-12-01

Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.

Indirect fuzzy adaptive control of a class of SISO nonlinear systems

International Nuclear Information System (INIS)

Laboid, S.; Boucherit, M.S.

2006-01-01

This paper presents an adaptive fuzzy control scheme for a class of continuous-time single-input single-output nonlinear systems with unknown dynamics and disturbance. Within this scheme, the fuzzy systems are employed to approximate the unknown system's dynamics. The proposed controller is composed of a well-defined adaptive fuzzy control term that uses the adaptive fuzzy approximation errors and disturbance. Based on a Lyapunov synthesis method, it is shown that the proposed adaptive control scheme guarantees the convergence of the tracking error to zero and the global boundedness of all signals in the closed-loop system. Moreover, the proposed controller allows initialization by zero of all adjusted parameters in the fuzzy approximators, and does not require the knowledge of the lower bound of the control gain and upper bounds of the approximation errors and disturbance. Simulation results performed on an inverted pendulum system are given to point out the good performance of the developed adaptive controller. (author)

FUZZY MAPPING IN DATA SONIFICATION SYSTEM OF WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

Arseny A. Markhotin

2016-11-01

Full Text Available Problem Statement. This paper describes the modeling of sonification system with possible types of wireless sensor network data. Fuzzy logic is used for the data-to-sound mapping. Methods. Devised sonification system includes input data model and sound synthesis core. It was created in Pure Data. For fuzzy output of mapped data the Fuzzy Logic Toolboxof MATLABwas used. Moreover, the system model has an ability to send data to the side application via UDP protocol. Results. We offer the method of timbre space organization for sonification system output and the following output of control sound characteristics depending on the type of input data. Practical Relevance. The offered approach of using fuzzy logic in sonification systems can be applied in development of new applications when the formalization of data-to-sound mapping is difficult and also complicated timbal space organization is required.

Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification

Directory of Open Access Journals (Sweden)

M. Sarosa

2013-09-01

Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.

Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

Directory of Open Access Journals (Sweden)

Xiuyan Peng

2015-01-01

Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

New approach to solve fully fuzzy system of linear equations using ...

Indian Academy of Sciences (India)

Known example problems are solved to illustrate the efficacy and ... The concept of fuzzy set and fuzzy number were first introduced by Zadeh .... (iii) Fully fuzzy linear systems can be solved by linear programming approach, Gauss elim-.

A Proposed Method for Solving Fuzzy System of Linear Equations

Directory of Open Access Journals (Sweden)

Reza Kargar

2014-01-01

Full Text Available This paper proposes a new method for solving fuzzy system of linear equations with crisp coefficients matrix and fuzzy or interval right hand side. Some conditions for the existence of a fuzzy or interval solution of m×n linear system are derived and also a practical algorithm is introduced in detail. The method is based on linear programming problem. Finally the applicability of the proposed method is illustrated by some numerical examples.

Fuzzy interval Finite Element/Statistical Energy Analysis for mid-frequency analysis of built-up systems with mixed fuzzy and interval parameters

Science.gov (United States)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2016-10-01

This paper introduces mixed fuzzy and interval parametric uncertainties into the FE components of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model for mid-frequency analysis of built-up systems, thus an uncertain ensemble combining non-parametric with mixed fuzzy and interval parametric uncertainties comes into being. A fuzzy interval Finite Element/Statistical Energy Analysis (FIFE/SEA) framework is proposed to obtain the uncertain responses of built-up systems, which are described as intervals with fuzzy bounds, termed as fuzzy-bounded intervals (FBIs) in this paper. Based on the level-cut technique, a first-order fuzzy interval perturbation FE/SEA (FFIPFE/SEA) and a second-order fuzzy interval perturbation FE/SEA method (SFIPFE/SEA) are developed to handle the mixed parametric uncertainties efficiently. FFIPFE/SEA approximates the response functions by the first-order Taylor series, while SFIPFE/SEA improves the accuracy by considering the second-order items of Taylor series, in which all the mixed second-order items are neglected. To further improve the accuracy, a Chebyshev fuzzy interval method (CFIM) is proposed, in which the Chebyshev polynomials is used to approximate the response functions. The FBIs are eventually reconstructed by assembling the extrema solutions at all cut levels. Numerical results on two built-up systems verify the effectiveness of the proposed methods.

Application of ANNs approach for solving fully fuzzy polynomials system

Directory of Open Access Journals (Sweden)

R. Novin

2017-11-01

Full Text Available In processing indecisive or unclear information, the advantages of fuzzy logic and neurocomputing disciplines should be taken into account and combined by fuzzy neural networks. The current research intends to present a fuzzy modeling method using multi-layer fuzzy neural networks for solving a fully fuzzy polynomials system. To clarify the point, it is necessary to inform that a supervised gradient descent-based learning law is employed. The feasibility of the method is examined using computer simulations on a numerical example. The experimental results obtained from the investigation of the proposed method are valid and delivers very good approximation results.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

Science.gov (United States)

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

Directory of Open Access Journals (Sweden)

N. V. Kolesov

2013-01-01

Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR END MILLING

Directory of Open Access Journals (Sweden)

ANGELOS P. MARKOPOULOS

2016-09-01

Full Text Available Soft computing is commonly used as a modelling method in various technological areas. Methods such as Artificial Neural Networks and Fuzzy Logic have found application in manufacturing technology as well. NeuroFuzzy systems, aimed to combine the benefits of both the aforementioned Artificial Intelligence methods, are a subject of research lately as have proven to be superior compared to other methods. In this paper an adaptive neuro-fuzzy inference system for the prediction of surface roughness in end milling is presented. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables, while roughness parameter Ra as dependent output variable. Several variations are tested and the results of the optimum system are presented. Final results indicate that the proposed model can accurately predict surface roughness, even for input that was not used in training.

Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

Science.gov (United States)

Mazandarani, Mehran; Pariz, Naser

2018-05-01

This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics theory and applications

CERN Document Server

de Barros, Laécio Carvalho; Lodwick, Weldon Alexander

2017-01-01

This book provides an essential introduction to the field of dynamical models. Starting from classical theories such as set theory and probability, it allows readers to draw near to the fuzzy case. On one hand, the book equips readers with a fundamental understanding of the theoretical underpinnings of fuzzy sets and fuzzy dynamical systems. On the other, it demonstrates how these theories are used to solve modeling problems in biomathematics, and presents existing derivatives and integrals applied to the context of fuzzy functions. Each of the major topics is accompanied by examples, worked-out exercises, and exercises to be completed. Moreover, many applications to real problems are presented. The book has been developed on the basis of the authors’ lectures to university students and is accordingly primarily intended as a textbook for both upper-level undergraduates and graduates in applied mathematics, statistics, and engineering. It also offers a valuable resource for practitioners such as mathematical...

Fuzzy logic controller implementation for a solar air-conditioning system

International Nuclear Information System (INIS)

Lygouras, J.N.; Botsaris, P.N.; Vourvoulakis, J.; Kodogiannis, V.

2007-01-01

The implementation of a variable structure fuzzy logic controller for a solar powered air conditioning system and its advantages are investigated in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. Two different control schemes for the DC motors rotational speed adjustment are implemented and tested: the first one is a pure fuzzy controller, its output being the control signal for the DC motor driver. A 7 x 7 fuzzy matrix assigns the controller output with respect to the error value and the derivative of the error. The second scheme is a two-level controller. The lower level is a conventional PID controller, and the higher level is a fuzzy controller acting over the parameters of the low level controller. Step response of the two control loops are presented as experimental results. The contribution of this design is that in the control system, the fuzzy logic is implemented through software in a common, inexpensive, 16-bit microcontroller, which does not have special abilities for fuzzy control

Fuzzy logic controller implementation for a solar air-conditioning system

Energy Technology Data Exchange (ETDEWEB)

Lygouras, J.N.; Vourvoulakis, J. [Laboratory of Electronics, School of Electrical and Computer Engineering, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi (Greece); Botsaris, P.N. [Laboratory of Materials, Processes and Mechanical Design, School of Production and Management Engineering, Democritus University of Thrace 67100 Xanthi (Greece); Kodogiannis, V. [Centre for Systems Analysis, School of Computer Science, University of Westminster, London, HA1 3TP (United Kingdom)

2007-12-15

The implementation of a variable structure fuzzy logic controller for a solar powered air conditioning system and its advantages are investigated in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. Two different control schemes for the DC motors rotational speed adjustment are implemented and tested: the first one is a pure fuzzy controller, its output being the control signal for the DC motor driver. A 7 x 7 fuzzy matrix assigns the controller output with respect to the error value and the derivative of the error. The second scheme is a two-level controller. The lower level is a conventional PID controller, and the higher level is a fuzzy controller acting over the parameters of the low level controller. Step response of the two control loops are presented as experimental results. The contribution of this design is that in the control system, the fuzzy logic is implemented through software in a common, inexpensive, 16-bit microcontroller, which does not have special abilities for fuzzy control. (author)

Fuzzy controller for an uncertain dynamical system

DEFF Research Database (Denmark)

Kulczycki, P.; Wisniewski, Rafal

2002-01-01

The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The met......The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters....... The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance....

Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

Directory of Open Access Journals (Sweden)

Lili Zhang

2014-01-01

Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.

Fuzzy Modeling and Synchronization of a New Hyperchaotic Complex System with Uncertainties

Directory of Open Access Journals (Sweden)

Hadi Delavari

2015-07-01

Full Text Available In this paper, the synchronization of a new hyperchaotic complex system based on T-S fuzzy model is proposed. First the considered hyperchaotic system is represented by T-S fuzzy model equivalently. Then by using the parallel distributed compensation (PDC method and by applying linear system theory and exact linearization (EL technique, a fuzzy controller is designed to realize the synchronization. Finally, simulation results are carried out to demonstrate the performance of our proposed control scheme, and also the robustness of the designed fuzzy controller to uncertainties.

Quality regularities of dynamic X-ray diffraction in superlattices and films with variable gradient of deformation based on analysis of types of Takagi equation solutions

International Nuclear Information System (INIS)

Dyshekov, A.A.; Khapachev, Yu.P.

1997-01-01

It is proposed to use qualitative investigation methods of the differential Takagi equation solutions for the analysis of general properties of wave fields in deformed crystals. The physical interpretation of possible types of the Takagi equation solutions is considered briefly from the viewpoint of the stability theory. The type of solutions are defined by ratios between parameters involved in the equations set. For the Takagi equation these parameters are prescribed by the angular tuning from the precise Bragg angle as well as structural characteristics of the crystal and the deformation profile. The qualitative analysis for the problem of the dynamic X-ray diffraction is carried out for films with the variable deformation gradient and superlattices [ru

Hybridizing fuzzy control and timed automata for modeling variable structure fuzzy systems

NARCIS (Netherlands)

Acampora, G.; Loia, V.; Vitiello, A.

2010-01-01

During the past several years, fuzzy control has emerged as one of the most suitable and efficient methods for designing and developing complex systems in environments characterized by high level of uncertainty and imprecision. Nowadays, this methodology is used to model systems in several

H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

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Bomo W. Sanjaya

2014-07-01

Full Text Available This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simulation study is presented to show the effectiveness of the SOS-based H∞ control designfor nonlinear polynomial fuzzy systems.

Why fuzzy controllers should be fuzzy

International Nuclear Information System (INIS)

Nowe, A.

1996-01-01

Fuzzy controllers are usually looked at as crisp valued mappings especially when artificial intelligence learning techniques are used to build up the controller. By doing so the semantics of a fuzzy conclusion being a fuzzy restriction on the viable control actions is non-existing. In this paper the authors criticise from an approximation point of view using a fuzzy controller to express a crisp mapping does not seem the right way to go. Secondly it is illustrated that interesting information is contained in a fuzzy conclusion when indeed this conclusion is considered as a fuzzy restriction. This information turns out to be very valuable when viability problems are concerned, i.e. problems where the objective is to keep a system within predefined boundaries

Intelligent control-I: review of fuzzy logic and fuzzy set theory

International Nuclear Information System (INIS)

Nagrial, M.H.

2004-01-01

In the past decade or so, fuzzy systems have supplanted conventional technologies in many engineering systems, in particular in control systems and pattern recognition. Fuzzy logic has found applications in a variety of consumer products e.g. washing machines, camcorders, digital cameras, air conditioners, subway trains, cement kilns and many others. The fuzzy technology is also being applied in information technology, where it provides decision-support and expert systems with powerful reasoning capabilities. Fuzzy sets, introduced by Zadeh in 1965 as a mathematical way to represent vagueness in linguistics, can be considered a generalisation of classical set theory. Fuzziness is often confused with probability. This lecture will introduce the principal concepts and mathematical notions of fuzzy set theory. (author)

Adaptive fuzzy trajectory control for biaxial motion stage system

Directory of Open Access Journals (Sweden)

Wei-Lung Mao

2016-04-01

Full Text Available Motion control is an essential part of industrial machinery and manufacturing systems. In this article, the adaptive fuzzy controller is proposed for precision trajectory tracking control in biaxial X-Y motion stage system. The theoretical analyses of direct fuzzy control which is insensitive to parameter uncertainties and external load disturbances are derived to demonstrate the feasibility to track the reference trajectories. The Lyapunov stability theorem has been used to testify the asymptotic stability of the whole system, and all the signals are bounded in the closed-loop system. The intelligent position controller combines the merits of the adaptive fuzzy control with robust characteristics and learning ability for periodic command tracking of a servo drive mechanism. The simulation and experimental results on square, triangle, star, and circle reference contours are presented to show that the proposed controller indeed accomplishes the better tracking performances with regard to model uncertainties. It is observed that the convergence of parameters and tracking errors can be faster and smaller compared with the conventional adaptive fuzzy control in terms of average tracking error and tracking error standard deviation.

Application of Fuzzy Clustering in Modeling of a Water Hydraulics System

DEFF Research Database (Denmark)

Zhou, Jianjun; Kroszynski, Uri

2000-01-01

This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy...... model is extracted from the obtained partitions. The identified model has been evaluated by comparing measurements with simulation results. The evaluation shows that the identified model is capable of describing the system dynamics over a reasonably wide frequency range....

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

A Genetic Based Neuro-Fuzzy Controller System

International Nuclear Information System (INIS)

Mohamed, A.H.

2014-01-01

Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications

Introduction to Fuzzy Set Theory

Science.gov (United States)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Two-Dimensional Fuzzy Sliding Mode Control of a Field-Sensed Magnetic Suspension System

Directory of Open Access Journals (Sweden)

Jen-Hsing Li

2014-01-01

Full Text Available This paper presents the two-dimensional fuzzy sliding mode control of a field-sensed magnetic suspension system. The fuzzy rules include both the sliding manifold and its derivative. The fuzzy sliding mode control has advantages of the sliding mode control and the fuzzy control rules are minimized. Magnetic suspension systems are nonlinear and inherently unstable systems. The two-dimensional fuzzy sliding mode control can stabilize the nonlinear systems globally and attenuate chatter effectively. It is adequate to be applied to magnetic suspension systems. New design circuits of magnetic suspension systems are proposed in this paper. ARM Cortex-M3 microcontroller is utilized as a digital controller. The implemented driver, sensor, and control circuits are simpler, more inexpensive, and effective. This apparatus is satisfactory for engineering education. In the hands-on experiments, the proposed control scheme markedly improves performances of the field-sensed magnetic suspension system.

Recent Advances in Interval Type-2 Fuzzy Systems

CERN Document Server

Castillo, Oscar

2012-01-01

This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hy-brid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We con-sider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.

Stability analysis of fuzzy parametric uncertain systems.

Science.gov (United States)

Bhiwani, R J; Patre, B M

2011-10-01

In this paper, the determination of stability margin, gain and phase margin aspects of fuzzy parametric uncertain systems are dealt. The stability analysis of uncertain linear systems with coefficients described by fuzzy functions is studied. A complexity reduced technique for determining the stability margin for FPUS is proposed. The method suggested is dependent on the order of the characteristic polynomial. In order to find the stability margin of interval polynomials of order less than 5, it is not always necessary to determine and check all four Kharitonov's polynomials. It has been shown that, for determining stability margin of FPUS of order five, four, and three we require only 3, 2, and 1 Kharitonov's polynomials respectively. Only for sixth and higher order polynomials, a complete set of Kharitonov's polynomials are needed to determine the stability margin. Thus for lower order systems, the calculations are reduced to a large extent. This idea has been extended to determine the stability margin of fuzzy interval polynomials. It is also shown that the gain and phase margin of FPUS can be determined analytically without using graphical techniques. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment.

Science.gov (United States)

Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R; Sabahi, Ahmad

2012-12-01

In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach. Copyright © 2012 Elsevier Inc. All rights reserved.

Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System

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Żurek Józef

2014-12-01

Full Text Available The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference. The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.

An improved fuzzy Kalman filter for state estimation of nonlinear systems

International Nuclear Information System (INIS)

Zhou, Z-J; Hu, C-H; Chen, L; Zhang, B-C

2008-01-01

The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method

Model-based fuzzy control solutions for a laboratory Antilock Braking System

DEFF Research Database (Denmark)

Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan

2010-01-01

This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems...

Stabilizing periodic orbits of chaotic systems using fuzzy adaptive sliding mode control

Energy Technology Data Exchange (ETDEWEB)

Layeghi, Hamed [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: layeghi@mech.sharif.edu; Arjmand, Mehdi Tabe [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: arjmand@mech.sharif.edu; Salarieh, Hassan [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

2008-08-15

In this paper by using a combination of fuzzy identification and the sliding mode control a fuzzy adaptive sliding mode scheme is designed to stabilize the unstable periodic orbits of chaotic systems. The chaotic system is assumed to have an affine form x{sup (n)} = f(X) + g(X)u where f and g are unknown functions. Using only the input-output data obtained from the underlying dynamical system, two fuzzy systems are constructed for identification of f and g. Two distinct methods are utilized for fuzzy modeling, the least squares and the gradient descent techniques. Based on the estimated fuzzy models, an adaptive controller, which works through the sliding mode control, is designed to make the system track the desired unstable periodic orbits. The stability analysis of the overall closed loop system is presented in the paper and the effectiveness of the proposed adaptive scheme is numerically investigated. As a case of study, modified Duffing system is selected for applying the proposed method to stabilize its 2{pi} and 4{pi} periodic orbits. Simulation results show the high performance of the method for stabilizing the unstable periodic orbits of unknown chaotic systems.

Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

Directory of Open Access Journals (Sweden)

Mohand Achour Touat

2008-04-01

Full Text Available  This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

A genetic fuzzy system for unstable angina risk assessment.

Science.gov (United States)

Dong, Wei; Huang, Zhengxing; Ji, Lei; Duan, Huilong

2014-02-18

Unstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized. In order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information's vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment. The proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%). By comparing the results that are obtained through the proposed system with those resulting from the physician's decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.

Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

Institute of Scientific and Technical Information of China (English)

FENG Yu-hu

2005-01-01

By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.

Fuzzy multiobjective models for optimal operation of a hydropower system

Science.gov (United States)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Efficient Fuzzy Logic Controller for Magnetic Levitation Systems

African Journals Online (AJOL)

Akorede

ABSTRACT: Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system ... disturbance signal was applied to the input of the control system. Fuzzy ..... Automatic Control System, fifth edition.

A GA-fuzzy automatic generation controller for interconnected power system

CSIR Research Space (South Africa)

Boesack, CD

2011-10-01

Full Text Available This paper presents a GA-Fuzzy Automatic Generation Controller for large interconnected power systems. The design of Fuzzy Logic Controllers by means of expert knowledge have typically been the traditional design norm, however, this may not yield...

Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

Science.gov (United States)

Sinuk, V. G.; Panchenko, M. V.

2018-03-01

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

Science.gov (United States)

Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

Application of fuzzy logic control system for reactor feed-water control

International Nuclear Information System (INIS)

Iijima, T.; Nakajima, Y.

1994-01-01

The successful actual application of a fuzzy logic control system to the a nuclear Fugen nuclear power reactor is described. Fugen is a heavy-water moderated, light-water cooled reactor. The introduction of fuzzy logic control system has enabled operators to control the steam drum water level more effectively in comparison to a conventional proportional-integral (PI) control system

Using system dynamics for simulation and optimization of one coal industry system under fuzzy environment

Energy Technology Data Exchange (ETDEWEB)

Xu, J.P.; Li, X.F. [Sichuan University, Chengdu (China)

2011-09-15

In this paper, we have developed a model that integrates system dynamics with fuzzy multiple objective programming (SD-FMOP). This model can be used to study the complex interactions in a industry system. In the process of confirming sensitive parameters and fuzzy variables of the SD model, we made use of fuzzy multi-objective programming to help yield the solution. We adopted the chance-constraint programming model to convert the fuzzy variables into precise values. We use genetic algorithm to solve FMOP model, and obtain the Pareto solution through the programming models. It is evident that FMOP is effective in optimizing the given system to obtain the decision objectives of the SD model. The results recorded from the SD model are in our option, reasonable and credible. These results may help governments to establish more effective policy related to the coal industry development.

Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

Science.gov (United States)

Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

2017-03-01

Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems

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Habib Palizvan Zand

2017-02-01

Full Text Available Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki. Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM, cation exchange capacity (CEC, sodium adsorption ratio (SAR, EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11 Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as

Multi-stage fuzzy PID power system automatic generation controller in deregulated environments

International Nuclear Information System (INIS)

Shayeghi, H.; Shayanfar, H.A.; Jalili, A.

2006-01-01

In this paper, a multi-stage fuzzy proportional integral derivative (PID) type controller is proposed to solve the automatic generation control (AGC) problem in a deregulated power system that operates under deregulation based on the bilateral policy scheme. In each control area, the effects of the possible contracts are treated as a set of new input signals in a modified traditional dynamical model. The multi-stage controller uses the fuzzy switch to blend a proportional derivative (PD) fuzzy logic controller with an integral fuzzy logic input. The proposed controller operates on fuzzy values passing the consequence of a prior stage on to the next stage as fact. The salient advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter variations and system nonlinearities. This newly developed strategy leads to a flexible controller with simple structure that is easy to implement, and therefore, it can be useful for the real world power systems. The proposed method is tested on a three area power system with different contracted scenarios under various operating conditions. The results of the proposed controller are compared with those of the classical fuzzy PID type controller and classical PID controller through some performance indices to illustrate its robust performance

Effects analysis fuzzy inference system in nuclear problems using approximate reasoning

International Nuclear Information System (INIS)

Guimaraes, Antonio C.F.; Franklin Lapa, Celso Marcelo

2004-01-01

In this paper a fuzzy inference system modeling technique applied on failure mode and effects analysis (FMEA) is introduced in reactor nuclear problems. This method uses the concept of a pure fuzzy logic system to treat the traditional FMEA parameters: probabilities of occurrence, severity and detection. The auxiliary feed-water system of a typical two-loop pressurized water reactor (PWR) was used as practical example in this analysis. The kernel result is the conceptual confrontation among the traditional risk priority number (RPN) and the fuzzy risk priority number (FRPN) obtained from experts opinion. The set of results demonstrated the great potential of the inference system and advantage of the gray approach in this class of problems

Fuzzy Control of Cold Storage Refrigeration System with Dynamic Coupling Compensation

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Xiliang Ma

2018-01-01

Full Text Available Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling between the components was studied. Second, to reduce the adverse effects of the coupling, a fuzzy controller with dynamic coupling compensation was designed. As for the fuzzy controller, a self-tuning fuzzy controller was served as the primary controller, and an adaptive neural network was adopted to compensate the dynamic coupling. Finally, the proposed control strategy was employed to the cold storage refrigeration system, and simulations were carried out in the condition of start-up, variable load, and variable degree of superheat, respectively. The simulation results verify the effectiveness of the fuzzy control method with dynamic coupling compensation.

Application of fuzzy system theory in addressing the presence of uncertainties

Energy Technology Data Exchange (ETDEWEB)

Yusmye, A. Y. N. [Institute of Engineering Mathematics, Universiti Malaysia Perlis Kampus Pauh Putra, 02600, Arau, Perlis (Malaysia); Goh, B. Y.; Adnan, N. F.; Ariffin, A. K. [Department of Mechanical and Materials, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor (Malaysia)

2015-02-03

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.

Application of fuzzy system theory in addressing the presence of uncertainties

International Nuclear Information System (INIS)

Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.; Ariffin, A. K.

2015-01-01

In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method

Evaluation of Combined Heat and Power (CHP Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS

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Fausto Cavallaro

2016-06-01

Full Text Available Combined heat and power (CHP or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as “sustainable”, we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon’s entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS approach will be tested for this purpose. Shannon’s entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria—it does not require a decision-making (DM to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view.

Fuzzy Logic Based MPPT Controller for a PV System

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Carlos Robles Algarín

2017-12-01

Full Text Available The output power of a photovoltaic (PV module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

On Stochastic Finite-Time Control of Discrete-Time Fuzzy Systems with Packet Dropout

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Yingqi Zhang

2012-01-01

Full Text Available This paper is concerned with the stochastic finite-time stability and stochastic finite-time boundedness problems for one family of fuzzy discrete-time systems over networks with packet dropout, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, we present the dynamic model description studied, in which the discrete-time fuzzy T-S systems with packet loss can be described by one class of fuzzy Markovian jump systems. Then, the concepts of stochastic finite-time stability and stochastic finite-time boundedness and problem formulation are given. Based on Lyapunov function approach, sufficient conditions on stochastic finite-time stability and stochastic finite-time boundedness are established for the resulting closed-loop fuzzy discrete-time system with Markovian jumps, and state-feedback controllers are designed to ensure stochastic finite-time stability and stochastic finite-time boundedness of the class of fuzzy systems. The stochastic finite-time stability and stochastic finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the stochastic stability of the class of fuzzy T-S systems with packet loss. Finally, two illustrative examples are presented to show the validity of the developed methodology.

A Fuzzy Logic-Based Video Subtitle and Caption Coloring System

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Mohsen Davoudi

2012-01-01

Full Text Available An approach has been proposed for automatic adaptive subtitle coloring using fuzzy logic-based algorithm. This system changes the color of the video subtitle/caption to “pleasant” color according to color harmony and the visual perception of the image background colors. In the fuzzy analyzer unit, using RGB histograms of background image, the R, G, and B values for the color of the subtitle/caption are computed using fixed fuzzy IF-THEN rules fully driven from the color harmony theories to satisfy complementary color and subtitle-background color harmony conditions. A real-time hardware structure has been proposed for implementation of the front-end processing unit as well as the fuzzy analyzer unit.

Design of a self-adaptive fuzzy PID controller for piezoelectric ceramics micro-displacement system

Science.gov (United States)

Zhang, Shuang; Zhong, Yuning; Xu, Zhongbao

2008-12-01

In order to improve control precision of the piezoelectric ceramics (PZT) micro-displacement system, a self-adaptive fuzzy Proportional Integration Differential (PID) controller is designed based on the traditional digital PID controller combining with fuzzy control. The arithmetic gives a fuzzy control rule table with the fuzzy control rule and fuzzy reasoning, through this table, the PID parameters can be adjusted online in real time control. Furthermore, the automatic selective control is achieved according to the change of the error. The controller combines the good dynamic capability of the fuzzy control and the high stable precision of the PID control, adopts the method of using fuzzy control and PID control in different segments of time. In the initial and middle stage of the transition process of system, that is, when the error is larger than the value, fuzzy control is used to adjust control variable. It makes full use of the fast response of the fuzzy control. And when the error is smaller than the value, the system is about to be in the steady state, PID control is adopted to eliminate static error. The problems of PZT existing in the field of precise positioning are overcome. The results of the experiments prove that the project is correct and practicable.

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

Science.gov (United States)

Butt, Muhammad Arif; Akram, Muhammad

2016-01-01

We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

Variable structure TITO fuzzy-logic controller implementation for a solar air-conditioning system

Energy Technology Data Exchange (ETDEWEB)

Lygouras, J.N.; Pachidis, Th. [Laboratory of Electronics, School of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi (Greece); Kodogiannis, V.S. [Centre for Systems Analysis, School of Computer Science, University of Westminster, London HA1 3TP (United Kingdom); Tarchanidis, K.N. [Department of Petroleum Technology, Technological Education Institute of Kavala, GR-65404, Kavala (Greece); Koukourlis, C.S. [Laboratory of Telecommunications, School of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi (Greece)

2008-04-15

The design and implementation of a Two-Input/Two-Output (TITO) variable structure fuzzy-logic controller for a solar-powered air-conditioning system is described in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. The first affects the temperature in the generator of the solar air-conditioner, while the second, the pressure in the power loop. The difficulty of Multi-Input/Multi-Output (MIMO) systems control is how to overcome the coupling effects among each degree of freedom. First, a traditional fuzzy-controller has been designed, its output being one of the components of the control signal for each DC motor driver. Secondly, according to the characteristics of the system's dynamics coupling, an appropriate coupling fuzzy-controller (CFC) is incorporated into a traditional fuzzy-controller (TFC) to compensate for the dynamic coupling among each degree of freedom. This control strategy simplifies the implementation problem of fuzzy control, but can also improve the control performance. This mixed fuzzy controller (MFC) can effectively improve the coupling effects of the systems, and this control strategy is easy to design and implement. Experimental results from the implemented system are presented. (author)

A fuzzy recommendation system for daily water intake

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Bin Dai

2016-05-01

Full Text Available Water is one of the most important constituents of the human body. Daily consumption of water is thus necessary to protect human health. Daily water consumption is related to several factors such as age, ambient temperature, and degree of physical activity. These factors are generally difficult to express with exact numerical values. The main objective of this article is to build a daily water intake recommendation system using fuzzy methods. This system will use age, physical activity, and ambient temperature as the input factors and daily water intake values as the output factor. The reasoning mechanism of the fuzzy system can calculate the recommended value of daily water intake. Finally, the system will compare the actual recommended values with our system to determine the usefulness. The experimental results show that this recommendation system is effective in actual application.

A fuzzy control technique for a magnetically levitated system

Energy Technology Data Exchange (ETDEWEB)

Lo Verso, G [C.N.R., Ce.Ri.S.E.P., Palermo (Italy); Trapanese, M [Dipt. di Ingegneria Elettrica, Univ. di Palermo (Italy)

1996-12-31

This paper presents the results of some analytical and numerical investigations on a control approach for magnetically leviated systems. This approach is based on fuzzy logic. It has been widely demonstrated that traditional control systems consent to maintain a stiff control on the air gap length. However, the traditional approaches could cause at very high speed, a vertical acceleration of the vehicle cabin larger than the maximum value currently allowed by the ISO standard. It is aim of this work to investigate the possibilities that a fuzzy controller offer in order to solve this problem. In order set up the controller, every mechanical degree of freedom is modelled in terms of some linguistic variables. These linguistic variables are described by several fuzzy sets. It must be noted that, doing so, the disturbances can be described in terms of fuzzy sets, too. A single-mass-model of the vehicle is considered in the paper. The features of the controller are numerically simulated under several types of disturbances and they are compared with a traditional control approach. It is shown how some parameters (especially the vertical acceleration) improve their behaviour. (orig.)

PERFORMANCE EVALUATION OF PENSION FUNDS WITH FUZZY EXPERT SYSTEM

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SERDAR KORUKOĞLU

2013-06-01

Full Text Available Financial rating and ranking firms often use linguistic instead of numerical values. When input data are mostly qualitative and are based on subjective knowledge of experts, the Fuzzy Set Theory provides a solid mathematical model to represent and handle these data. The aim of this study is developing a fuzzy expert model to evaluate the performance of the pension funds by using their risk and return values. The method is used for evaluating the performance of the randomly selected of twenty seven Turkish pension funds. The obtained results proved that the fuzzy expert system is appropriate and consistent for performance evaluation.

A fuzzy expert system for predicting the performance of switched reluctance motor

International Nuclear Information System (INIS)

Mirzaeian, B.; Moallem, M.; Lucas, Caro

2001-01-01

In this paper a fuzzy expert system for predicting the performance of a switched reluctance motor has been developed. The design vector consists of design parameters, and output performance variables are efficiency and torque ripple. An accurate analysis program based on Improved Magnetic Equivalent Circuit method has been used to generate the input-output data. These input-output data is used to produce the initial fuzzy rules for predicting the performance of Switched Reluctance Motor. The initial set of fuzzy rules with triangular membership functions has been devised using a table look-up scheme. The initial fuzzy rules have been optimized to a set of fuzzy rules with Gaussian membership functions using gradient descent training scheme. The performance prediction results for a 6/8, 4 kw, Switched Reluctance Motor shows good agreement with the results obtained from Improved Magnetic Equivalent Circuit method or Finite Element analysis. The developed fuzzy expert system can be used for fast prediction of motor performance in the optimal design process or on-line control schemes of Switched Reluctance motor

Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach

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Rana Dinesh Singh

2015-01-01

Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

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Chan-Uk Yeom

2017-10-01

Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

A neural fuzzy controller learning by fuzzy error propagation

Science.gov (United States)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Fuzzy logic system for BBT based fertility prediction | Yazed | Journal ...

African Journals Online (AJOL)

... been obtained with the accuracy of 95 % and 80 respectively. Besides, this prediction system using fuzzy logic could improve the current practice in the FAM technique by integrating it with an Internet of Things (IoT) technology for automatic BBT charting and monitoring. Keywords: family planning; fertility; BBT; fuzzy logic.

A TSK neuro-fuzzy approach for modeling highly dynamic systems

NARCIS (Netherlands)

Acampora, G.

2011-01-01

This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an

Fuzzy logics acquisition and simulation modules for expert systems to assist operator's decision for nuclear power stations

International Nuclear Information System (INIS)

Averkin, A.A.

1994-01-01

A new type of fuzzy expert system for assisting the operator's decisions in nuclear power plant system in non-standard situations is proposed. This expert system is based on new approaches to fuzzy logics acquisition and to fuzzy logics testing. Fuzzy logics can be generated by a T-norms axiomatic system to choose the most suitable to operator's way of thinking. Then the chosen fuzzy logic is tested by simulation of inference process in expert system. The designed logic is the input of inference module of expert system

Towards a Fuzzy Expert System on Toxicological Data Quality Assessment.

Science.gov (United States)

Yang, Longzhi; Neagu, Daniel; Cronin, Mark T D; Hewitt, Mark; Enoch, Steven J; Madden, Judith C; Przybylak, Katarzyna

2013-01-01

Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation

Pemodelan Sistem Fuzzy Dengan Menggunakan Matlab

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Afan Galih Salman

2010-12-01

Full Text Available Fuzzy logic is a method in soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. Some methods in soft computing category besides fuzzy logic are artificial network nerve, probabilistic reasoning, and evolutionary computing. Fuzzy logic has the ability to develop fuzzy system that is intelligent system in uncertain environment. Some stages in fuzzy system formation process is input and output analysis, determining input and output variable, defining each fuzzy set member function, determining rules based on experience or knowledge of an expert in his field, and implementing fuzzy system. Overall, fuzzy logic uses simple mathematical concept, understandable, detectable uncertain and accurate data. Fuzzy system could create and apply expert experiences directly without exercise process and effort to decode the knowledge into a computer until becoming a modeling system that could be relied on decision making.

Internet and Fuzzy Based Control System for Rotary Kiln in Cement Manufacturing Plant

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Hanane Zermane

2017-01-01

Full Text Available This paper develops an Internet-based fuzzy control system for an industrial process plant to ensure the remote and fuzzy control in cement factories in Algeria. The remote process consists of control, diagnosing alarms occurs, maintaining and synchronizing different regulation loops. Fuzzy control of the kiln ensures that the system be operational at all times, with minimal downtime. Internet technology ensures remote control. The system reduces downtimes and can guided by operators in the main control room or via Internet.

Modified Levenberg-Marquardt Method for RÖSSLER Chaotic System Fuzzy Modeling Training

Science.gov (United States)

Wang, Yu-Hui; Wu, Qing-Xian; Jiang, Chang-Sheng; Xue, Ya-Li; Fang, Wei

Generally, fuzzy approximation models require some human knowledge and experience. Operator's experience is involved in the mathematics of fuzzy theory as a collection of heuristic rules. The main goal of this paper is to present a new method for identifying unknown nonlinear dynamics such as Rössler system without any human knowledge. Instead of heuristic rules, the presented method uses the input-output data pairs to identify the Rössler chaotic system. The training algorithm is a modified Levenberg-Marquardt (L-M) method, which can adjust the parameters of each linear polynomial and fuzzy membership functions on line, and do not rely on experts' experience excessively. Finally, it is applied to training Rössler chaotic system fuzzy identification. Comparing this method with the standard L-M method, the convergence speed is accelerated. The simulation results demonstrate the effectiveness of the proposed method.

Trans-skull ultrasonic Doppler system aided by fuzzy logic

Science.gov (United States)

Hata, Yutaka; Nakamura, Masato; Yagi, Naomi; Ishikawa, Tomomoto

2012-06-01

This paper describes a trans-skull ultrasonic Doppler system for measuring the blood flow direction in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the fuzzy degree of blood flow by Doppler Effect, thereby it locates blood vessel. This Doppler Effect is examined by the center of gravity shift of the frequency magnitudes. In in-vitro experiment, a cow bone was employed as the skull, and three silicon tubes were done as blood vessels, and bubble in water as blood. We received the ultrasonic waves through a protein, the skull and silicon tubes in order. In the system, fuzzy degrees are determined with respect to the Doppler shift, amplitude of the waves and attenuation of the tissues. The fuzzy degrees of bone and blood direction are calculated by them. The experimental results showed that the system successfully visualized the skull and flow direction, compared with the location and flow direction of the phantom. Thus, it detected the flow direction by Doppler Effect under skull, and automatically extracted the region of skull and blood vessel.

Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation

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Jin-Hong Jeon

2011-09-01

Full Text Available Recently, interest in microgrids, which are composed of distributed generation (DG, distributed storage (DS, and loads, has been growing as a potentially effective clean energy system to mitigate against climate change. The microgrid is operated in the grid-connected mode and the islanded mode according to the conditions of the upstream power grid. The role of the energy storage system (ESS is especially important to maintain constant the frequency and voltage of an islanded microgrid. For this reason, various approaches for ESS control have been put forth. In this paper, a fuzzy PID controller is proposed to improve the frequency control performance of the ESS. This fuzzy PID controller consists of a fuzzy logic controller and a conventional PI controller, connected in series. The fuzzy logic controller has two input signals, and then the output signal of the fuzzy logic controller is the input signal of the conventional PI controller. For comparison of control performance, gains of each PI controller and fuzzy PID controller are tuned by the particle swam optimization (PSO algorithm. In the simulation study, the control performance of the fuzzy PID was also tested under various operating conditions using the PSCAD/EMTDC simulation platform.

Wireless Intelligent Monitoring and Control System of Greenhouse Temperature Based on Fuzzy-PID

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Mei ZHAN

2014-03-01

Full Text Available Control effect is not ideal for traditional control method and wired control system, since greenhouse temperature has such characteristics as nonlinear and longtime lag. Therefore, Fuzzy- PID control method was introduced and radio frequency chip CC1110 was applied to design greenhouse wireless intelligent monitoring and control system. The design of the system, the component of nodes and the developed intelligent management software system were explained in this paper. Then describe the design of the control algorithm Fuzzy-PID. By simulating the new method in Matlab software, the results showed that Fuzzy-PID method small overshoot and better dynamic performance compared with general PID control. It has shorter settling time and no steady-state error compared with fuzzy control. It can meet requirements in greenhouse production.

Intelligent micro blood typing system using a fuzzy algorithm

International Nuclear Information System (INIS)

Kang, Taeyun; Cho, Dong-Woo; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung

2010-01-01

ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis

Supervisory System and Multivariable Control Applying Weighted Fuzzy-PID Logic in an Alcoholic Fermentation Process

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Márcio Mendonça

2015-10-01

Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.

Application of genetic algorithms to tuning fuzzy control systems

Science.gov (United States)

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

Design of a Polynomial Fuzzy Observer Controller With Sampled-Output Measurements for Nonlinear Systems Considering Unmeasurable Premise Variables

OpenAIRE

Liu, Chuang; Lam, H. K.

2015-01-01

In this paper, we propose a polynomial fuzzy observer controller for nonlinear systems, where the design is achieved through the stability analysis of polynomial-fuzzy-model-based (PFMB) observer-control system. The polynomial fuzzy observer estimates the system states using estimated premise variables. The estimated states are then employed by the polynomial fuzzy controller for the feedback control of nonlinear systems represented by the polynomial fuzzy model. The system stability of the P...

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

Science.gov (United States)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

Design of sewage treatment system by applying fuzzy adaptive PID controller

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Jin, Liang-Ping; Li, Hong-Chan

2013-03-01

In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.

Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

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Hamed Kharrati

2012-01-01

Full Text Available This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

A fuzzy intelligent system for land consolidation - a case study in Shunde, China

Science.gov (United States)

Wang, J.; Ge, A.; Hu, Y.; Li, C.; Wang, L.

2015-04-01

Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. We needed to select a group of indexes useful for land consolidation according to the concrete demand. In this paper, a fuzzy measure, which can describe the importance of a single feature or a group of features, is adopted to accomplish the selection of specific features. A fuzzy integral that is based on a fuzzy measure is a type of fusion tool. We obtained the optimal solution for a fuzzy measure by solving a fuzzy integral. The fuzzy integrals can be transformed to a set of linear equations. We applied the L1-norm regularization method to solve the linear equations, and we found a solution with the fewest nonzero elements for the fuzzy measure; this solution shows the contribution of corresponding features or the combinations of decisions. This algorithm provides a quick and optimal way to identify the land index system when preparing to conduct the research, such as we describe herein, on land consolidation. Shunde's "Three Old" consolidation project provides the data for this work. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove

Fuzzy Expert System to Characterize Students

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Van Hecke, T.

2011-01-01

Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…

H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

OpenAIRE

Bomo W. Sanjaya; Bambang Riyanto Trilaksono; Arief Syaichu-Rohman

2014-01-01

This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS) approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simul...

A computationally efficient fuzzy control s

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Abdel Badie Sharkawy

2013-12-01

Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.

Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

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Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

2017-02-01

In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

Fuzzy logic control for improved pressurizer systems in nuclear power plants

International Nuclear Information System (INIS)

Brown, Chris; Gabbar, Hossam A.

2014-01-01

Highlights: • Improved performance of the pressurizer system in a CANDU nuclear power plant (NPP). • Inventory control for the pressurizer system in NPP. • Compare fuzzy logic with PID in pressurizer system in NPP. • Develop a fuzzy controller to regulate the pressurizer inventory control. • Compare control performance with current proportional controller used at NPP. - Abstract: The pressurizer system in a CANDU nuclear power plant is responsible for maintaining the pressure of the primary heat transport system to ensure the plant is operated within its safe operating envelope. The inventory control for the pressurizer system use a combination of level sensors, feed valves and bleed valves to ensure that there is adequate room in the pressurizer to accommodate any swell or shrinkage in the PHT system. The Darlington Nuclear Generating Station (DNGS) in Ontario, Canada currently uses a proportional controller for the bleed and feed valves to regulate the pressurizer inventory control which can result in large coolant level overshoot along with excessive settling times. The purpose of this paper is to develop a fuzzy controller to regulate the pressurizer inventory control and compare its performance to the current proportional controller used at DNGS. The simulation of the pressurizer inventory control system shows the fuzzy controller performs better than the proportional controller in terms of settling time and overshoot

Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.

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Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng

2016-07-01

In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA

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Wang Kaiyu

2016-01-01

Full Text Available Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle’s body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA, and set up a 1:10 verification platform of smart car to verify the fuzzy garage parking system with real car. Verification results show that, the system can complete the parking task very well.

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

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Yeou-Jiunn Chen

2016-09-01

Full Text Available Subjects with amyotrophic lateral sclerosis (ALS consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

System control fuzzy neural sewage pumping stations using genetic algorithms

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Владлен Николаевич Кузнецов

2015-06-01

Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

STUDENT PREDICTION SYSTEM FOR PLACEMENT TRAINING USING FUZZY INFERENCE SYSTEM

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Ravi Kumar Rathore

2017-04-01

Full Text Available Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student’s data set for placement and non-placement classes.

Feasibility analysis of fuzzy logic control for ITER Poloidal field (PF) AC/DC converter system

Energy Technology Data Exchange (ETDEWEB)

Hassan, Mahmood Ul; Fu, Peng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Song, Zhiquan, E-mail: zhquansong@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Chen, Xiaojiao [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Zhang, Xiuqing [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Humayun, Muhammad [Shanghai Jiaotong University (China)

2017-05-15

Highlights: • The implementation of the Fuzzy controller for the ITER PF converter system is presented. • The comparison of the FLC and PI simulation are investigated. • The FLC single and parallel bridge operation are presented. • Fuzzification and Defuzzification algorithms are presented using FLC controller. - Abstract: This paper describes the feasibility analysis of the fuzzy logic control to increase the performance of the ITER poloidal field (PF) converter systems. A fuzzy-logic-based controller is designed for ITER PF converter system, using the traditional PI controller and Fuzzy controller (FC), the dynamic behavior and transient response of the PF converter system are compared under normal operation by analysis and simulation. The analysis results show that the fuzzy logic control can achieve better operation performance than PI control.

Fuzzy control. Fundamentals, stability and design of fuzzy controllers

Energy Technology Data Exchange (ETDEWEB)

Michels, K. [Fichtner GmbH und Co. KG, Stuttgart (Germany); Klawonn, F. [Fachhochschule Braunschweig/Wolfenbuettel (Germany). Fachbereich Informatik; Kruse, R. [Magdeburg Univ. (Germany). Fakultaet Informatik, Abt. Wiss.- und Sprachverarbeitung; Nuernberger, A. (eds.) [California Univ., Berkeley, CA (United States). Computer Science Division

2006-07-01

The book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphases are placed on topics that are of importance for industrial applications, like (self-) tuning of fuzzy controllers, optimisation and stability analysis. The book is written as a textbook for graduate students as well as a comprehensive reference book about fuzzy control for researchers and application engineers. Starting with a detailed introduction to fuzzy systems and control theory the reader is guided to up-to-date research results. (orig.)

Fuzzy Control Teaching Models

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Klaus-Dietrich Kramer

2016-05-01

Full Text Available Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool, and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

The Use of Fuzzy Systems for Forecasting the Hardenability of Steel

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Sitek W.

2016-06-01

Full Text Available The goal of the research carried out was to develop the fuzzy systems, allowing the determination of the Jominy hardenability curve based on the chemical composition of structural steels for quenching and tempering. Fuzzy system was created to calculate hardness of the steel, based on the alloying elements concentrations, and to forecast the hardenability curves. This was done based on information from the PN-EN 10083-3: 2008. Examples of hardenability curves calculated for exemplar steels were presented. Results of the research confirmed that fuzzy systems are a useful tool in evaluation the effect of alloying elements on the properties of materials compared to conventional methods. It has been demonstrated the practical usefulness of the developed models which allows forecasting the steels’ Jominy hardenability curve.

The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller

Science.gov (United States)

Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin

The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

Science.gov (United States)

Pan, Indranil; Das, Saptarshi

2016-05-01

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Speed control for a two-mass drive system using integrated fuzzy estimator and hybrid fuzzy PD/PI controller

International Nuclear Information System (INIS)

Pai, N-S; Kuo, Y-P

2008-01-01

This paper presents a novel speed control scheme for a 2- mass motor drive system. The speed controller is based on the estimated state feedback compensation. The integrated fuzzy observer can give a fast and accuracy estimation of the unmeasured states. Two kinds of hybrid fuzzy proportional-derivative and proportional-integral (HF PD/PI) are proposed to cope with this speed control problem. The first is the static HF PD/PI controller and the second is the dynamic one. Simulation results show that the developed integrated fuzzy observer provide the better estimation performance than that of the Kalman filter and the proposed control schemes can effectively track the desired speed in the presence of load disturbance

Fuzzy Controllers for a Gantry Crane System with Experimental Verifications

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Naif B. Almutairi

2016-01-01

Full Text Available The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane.

Design of a Fuzzy Rule Base Expert System to Predict and Classify ...

African Journals Online (AJOL)

The main objective of design of a rule base expert system using fuzzy logic approach is to predict and forecast the risk level of cardiac patients to avoid sudden death. In this proposed system, uncertainty is captured using rule base and classification using fuzzy c-means clustering is discussed to overcome the risk level, ...

Fuzzy Expert System for Heart Attack Diagnosis

Science.gov (United States)

Hassan, Norlida; Arbaiy, Nureize; Shah, Noor Aziyan Ahmad; Afizah Afif@Afip, Zehan

2017-08-01

Heart attack is one of the serious illnesses and reported as the main killer disease. Early prevention is significant to reduce the risk of having the disease. The prevention efforts can be strengthen through awareness and education about risk factor and healthy lifestyle. Therefore the knowledge dissemination is needed to play role in order to distribute and educate public in health care management and disease prevention. Since the knowledge dissemination in medical is important, there is a need to develop a knowledge based system that can emulate human intelligence to assist decision making process. Thereby, this study utilized hybrid artificial intelligence (AI) techniques to develop a Fuzzy Expert System for Diagnosing Heart Attack Disease (HAD). This system integrates fuzzy logic with expert system, which helps the medical practitioner and people to predict the risk and as well as diagnosing heart attack based on given symptom. The development of HAD is expected not only providing expert knowledge but potentially become one of learning resources to help citizens to develop awareness about heart-healthy lifestyle.

Behavior coordination of mobile robotics using supervisory control of fuzzy discrete event systems.

Science.gov (United States)

Jayasiri, Awantha; Mann, George K I; Gosine, Raymond G

2011-10-01

In order to incorporate the uncertainty and impreciseness present in real-world event-driven asynchronous systems, fuzzy discrete event systems (DESs) (FDESs) have been proposed as an extension to crisp DESs. In this paper, first, we propose an extension to the supervisory control theory of FDES by redefining fuzzy controllable and uncontrollable events. The proposed supervisor is capable of enabling feasible uncontrollable and controllable events with different possibilities. Then, the extended supervisory control framework of FDES is employed to model and control several navigational tasks of a mobile robot using the behavior-based approach. The robot has limited sensory capabilities, and the navigations have been performed in several unmodeled environments. The reactive and deliberative behaviors of the mobile robotic system are weighted through fuzzy uncontrollable and controllable events, respectively. By employing the proposed supervisory controller, a command-fusion-type behavior coordination is achieved. The observability of fuzzy events is incorporated to represent the sensory imprecision. As a systematic analysis of the system, a fuzzy-state-based controllability measure is introduced. The approach is implemented in both simulation and real time. A performance evaluation is performed to quantitatively estimate the validity of the proposed approach over its counterparts.

A new learning algorithm for a fully connected neuro-fuzzy inference system.

Science.gov (United States)

Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

2014-10-01

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

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Abidatul Izzah

2016-07-01

Full Text Available AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS. Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas  masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS. However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution

A fuzzy-logic-based approach to qualitative safety modelling for marine systems

International Nuclear Information System (INIS)

Sii, H.S.; Ruxton, Tom; Wang Jin

2001-01-01

Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF-THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach

Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control

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M. Boukhnifer

2012-11-01

Full Text Available This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.

Streamflow Forecasting Using Nuero-Fuzzy Inference System

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Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System

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Mahsa Vajedi

2014-10-01

Full Text Available In this study, an adaptive neuro-fuzzy inference system (ANFIS has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.

Suitability of a Consensual Fuzzy Inference System to Evaluate Suppliers of Strategic Products

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Nazario Garcia

2018-01-01

Full Text Available This paper designs a bidding and supplier evaluation model focused on strategic product procurement, and develops their respective evaluation knowledge bases. The model is built using the most relevant variables cited in the reviewed procurement literature and allows to compare two evaluation methods: a factor weighting method (WM and a fuzzy inference system (FIS. By consulting an expert panel and using a two-tuples symbolic translation system, strong fuzzy partitions for all model variables are built. The method, based on central symmetry, permits to obtain the fuzzy label borders from their cores, which have been previously agreed among experts. The system also allows to agree the fuzzy rules to embed in the FIS. The results show the FIS method’s superiority as it allows to better manage the non-linear behavior and the uncertainty inherent to the supplier evaluation process.

Systems control with generalized probabilistic fuzzy-reinforcement learning

NARCIS (Netherlands)

Hinojosa, J.; Nefti, S.; Kaymak, U.

2011-01-01

Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

Induction machine Direct Torque Control system based on fuzzy adaptive control

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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.

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

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Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

Reliable Decentralized Control of Fuzzy Discrete-Event Systems and a Test Algorithm.

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Liu, Fuchun; Dziong, Zbigniew

2013-02-01

A framework for decentralized control of fuzzy discrete-event systems (FDESs) has been recently presented to guarantee the achievement of a given specification under the joint control of all local fuzzy supervisors. As a continuation, this paper addresses the reliable decentralized control of FDESs in face of possible failures of some local fuzzy supervisors. Roughly speaking, for an FDES equipped with n local fuzzy supervisors, a decentralized supervisor is called k-reliable (1 ≤ k ≤ n) provided that the control performance will not be degraded even when n - k local fuzzy supervisors fail. A necessary and sufficient condition for the existence of k-reliable decentralized supervisors of FDESs is proposed by introducing the notions of M̃uc-controllability and k-reliable coobservability of fuzzy language. In particular, a polynomial-time algorithm to test the k-reliable coobservability is developed by a constructive methodology, which indicates that the existence of k-reliable decentralized supervisors of FDESs can be checked with a polynomial complexity.

Decomposition of fuzzy continuity and fuzzy ideal continuity via fuzzy idealization

International Nuclear Information System (INIS)

Zahran, A.M.; Abbas, S.E.; Abd El-baki, S.A.; Saber, Y.M.

2009-01-01

Recently, El-Naschie has shown that the notion of fuzzy topology may be relevant to quantum paretical physics in connection with string theory and E-infinity space time theory. In this paper, we study the concepts of r-fuzzy semi-I-open, r-fuzzy pre-I-open, r-fuzzy α-I-open and r-fuzzy β-I-open sets, which is properly placed between r-fuzzy openness and r-fuzzy α-I-openness (r-fuzzy pre-I-openness) sets regardless the fuzzy ideal topological space in Sostak sense. Moreover, we give a decomposition of fuzzy continuity, fuzzy ideal continuity and fuzzy ideal α-continuity, and obtain several characterization and some properties of these functions. Also, we investigate their relationship with other types of function.

Memory H ∞ performance control of a class T-S fuzzy system

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Wang, Yanhua; He, Xiqin; Wu, Zhihua; Kang, Xiulan; Xiu, Wei

2018-03-01

For much nonlinear system in the control system, both the stability of the system and certain performance indicators are required. The characteristics of T-S model in fuzzy system make it possible to illustrate a great amount of nonlinear system efficiently. First and foremost, the T-S model with uncertainties and external disturbance is utilized to interpret nonlinear system so as to implement H∞ performance control by means of fuzzy control theory. Meantime, owing to the tremendous existence of time delay phenomenon in the controlled, feedback controller with memory fuzzy state is fabricated. On the basis of Lyapunov Stability Theory, the closed-loop system becomes stable by establishing Lyapunov function. Gain matrix of the memory state feedback controller is obtained by applying linear matrix inequality methodology. And simultaneously it makes the system meet the requirement of the H∞ performance indicator. Ultimately, the efficiency of the above-mentioned method is exemplified by the numerical computation.

UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID

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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.

Time-dependent Takagi-Taupin eikonal theory and applications in the subpicosecond manipulation of X-rays

International Nuclear Information System (INIS)

Adams, Bernhard W.

2004-01-01

A time-dependent version of the Takagi-Taupin theory of X-ray diffraction is derived in a unified space-time approach, which is particularly applicable to X-ray diffraction in a crystal that is undergoing rapid change on the subpicosecond, and even few-femtosecond, time scale. The theory is applied to the proposal of a class of X-ray optical elements for the subpicosecond manipulation of X-rays

Analysis and synthesis for interval type-2 fuzzy-model-based systems

CERN Document Server

Li, Hongyi; Lam, Hak-Keung; Gao, Yabin

2016-01-01

This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.

Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS

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Khadijah Fahmi Hayati Holle

2016-09-01

Full Text Available The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta.

Chattering-free fuzzy sliding-mode control strategy for uncertain chaotic systems

International Nuclear Information System (INIS)

Yau, H.-T.; Chen, C.-L.

2006-01-01

This paper proposes a chattering-free fuzzy sliding-mode control (FSMC) strategy for uncertain chaotic systems. A fuzzy logic control is used to replace the discontinuous sign function of the reaching law in traditional sliding-mode control (SMC), and hence a control input without chattering is obtained in the chaotic systems with uncertainties. Base on the Lyapunov stability theory, we address the design schemes of integration fuzzy sliding-mode control, where the reaching law is proposed by a set of linguistic rules and the control input is chattering free. The Genesio chaotic system is used to test the proposed control strategy and the simulation results show the FSMC not only can control the uncertain chaotic behaviors to a desired state without oscillator very fast, but also the switching function is smooth without chattering. This result implies that this strategy is feasible and effective for chaos control

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

Energy Technology Data Exchange (ETDEWEB)

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.

Stabilizing periodic orbits of chaotic systems using fuzzy control of Poincare map

International Nuclear Information System (INIS)

Bonakdar, Mohammad; Samadi, Mostafa; Salarieh, Hassan; Alasty, Aria

2008-01-01

In this paper a fuzzy control algorithm is used to stabilize the fixed points of a chaotic system. No knowledge of the dynamic equations of the system is needed in this approach and the whole system is considered as a black box. Two main approaches have been investigated: fuzzy clustering and table look up methods. As illustrative examples these methods have been applied to Bonhoeffer van der Pol oscillator and the Henon chaotic system and the convergence toward fixed points is observed

Stabilizing periodic orbits of chaotic systems using fuzzy control of Poincare map

Energy Technology Data Exchange (ETDEWEB)

Bonakdar, Mohammad; Samadi, Mostafa [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of); Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of)

2008-05-15

In this paper a fuzzy control algorithm is used to stabilize the fixed points of a chaotic system. No knowledge of the dynamic equations of the system is needed in this approach and the whole system is considered as a black box. Two main approaches have been investigated: fuzzy clustering and table look up methods. As illustrative examples these methods have been applied to Bonhoeffer van der Pol oscillator and the Henon chaotic system and the convergence toward fixed points is observed.

Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

Science.gov (United States)

Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

2008-08-01

In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

Stability margin of linear systems with parameters described by fuzzy numbers.