WorldWideScience

Sample records for fuzzy controller based

  1. Fuzzy Based composition Control of Distillation Column

    Directory of Open Access Journals (Sweden)

    Guru.R

    2013-04-01

    Full Text Available This paper proposed a control scheme based on fuzzy logic for a methanol - water system of bubble cap distillation column. Fuzzy rule base and Inference System of fuzzy (FIS is planned to regulatethe reflux ratio (manipulated variable to obtain the preferred product composition (methanol for a distillation column. Comparisons are made with conventional controller and the results confirmed the potentials of the proposed strategy of fuzzy control.

  2. Fuzzy logic based robotic controller

    Science.gov (United States)

    Attia, F.; Upadhyaya, M.

    1994-01-01

    Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

  3. Adaptive fuzzy controllers based on variable universe

    Institute of Scientific and Technical Information of China (English)

    李洪兴

    1999-01-01

    Adaptive fuzzy controllers by means of variable universe are proposed based on interpolation forms of fuzzy control. First, monotonicity of control rules is defined, and it is proved that the monotonicity of interpolation functions of fuzzy control is equivalent to the monotonicity of control rules. This means that there is not any contradiction among the control rules under the condition for the control rules being monotonic. Then structure of the contraction-expansion factor is discussed. At last, three models of adaptive fuzzy control based on variable universe are given which are adaptive fuzzy control model with potential heredity, adaptive fuzzy control model with obvious heredity and adaptive fuzzy control model with successively obvious heredity.

  4. Fuzzy controllers based on some fuzzy implication operators and their response functions

    Institute of Scientific and Technical Information of China (English)

    LI Hongxing; YOU Fei; PENG Jiayin

    2004-01-01

    The fuzzy controllers constructed by 23 fuzzy implication operators based on CRI algorithm and their response functions are discussed.The conclusions show that the fuzzy controllers constructed by 9 fuzzy implication operators are universal approximators to continuous functions and can be used in practical fuzzy control systems.And these 9 fuzzy implication operators except for Einstein operator intersection are all the adjoint pairs of some fuzzy implication operators.Besides, there are 3 other fuzzy controllers formed by fuzzy implication operators being regarded approximately as fitted functions.

  5. Z Number Based Fuzzy Inference System for Dynamic Plant Control

    Directory of Open Access Journals (Sweden)

    Rahib H. Abiyev

    2016-01-01

    Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.

  6. Fuzzy cascade control based on control's history for superheated temperature

    Institute of Scientific and Technical Information of China (English)

    WANG Guangjun; LI Gang; SHEN Shuguang

    2007-01-01

    To address the characteristics of the large delay and uncertainty of superheated temperature,a new cascade control system is presented based on control's history.Based on the analysis of the control objects' dynamic characteristics,historical control information (substituting for the deviation change rate) is used as the basis for decision-making of the fuzzy control.Therefore,the changing trend of the controlled variable can be accurately reflected.Furthermore,a proportional component is introduced,the advantages of PID and fuzzy controllers are integrated,and the structure weaknesses of conventional fuzzy controllers are overcome.Simulation shows that this control method can effectively reduce the adverse impact of the delay on control effects and,therefore,exhibit strong adaptability by comparing the superheated temperature control system by this controller with PID and conventional fuzzy controllers.

  7. A Direct Feedback Control Based on Fuzzy Recurrent Neural Network

    Institute of Scientific and Technical Information of China (English)

    李明; 马小平

    2002-01-01

    A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .

  8. Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.

    Science.gov (United States)

    Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il

    2017-09-01

    It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Fuzzy control of power converters based on quasilinear modelling

    Science.gov (United States)

    Li, C. K.; Lee, W. L.; Chou, Y. W.

    1995-03-01

    Unlike feedback control by the fuzzy PID method, a new fuzzy control algorithm based on quasilinear modelling of the DC-DC converter is proposed. Investigation is carried out using a buck-boost converter. Simulation results demonstrated that the converter can be regulated with improved performance even when subjected to input disturbance and load variation.

  10. SOFC temperature evaluation based on an adaptive fuzzy controller

    Institute of Scientific and Technical Information of China (English)

    Xiao-juan WU; Xin-jian ZHU; Guang-yi CAO; Heng-yong TU

    2008-01-01

    The operating temperature of a solid oxide fuel cell (SOFC) stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative (PID) control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.

  11. Adaptive Fuzzy Knowledge Based Controller for Autonomous Robot Motion Control

    Directory of Open Access Journals (Sweden)

    Mbaitiga Zacharie

    2010-01-01

    Full Text Available Problem statement: Research into robot motion control offers research opportunities that will change scientists and engineers for year to come. Autonomous robots are increasingly evident in many aspects of industry and everyday life and a robust robot motion control can be used for homeland security and many consumer applications. This study discussed the adaptive fuzzy knowledge based controller for robot motion control in indoor and outdoor environment. Approach: The proposed method consisted of two components: the process monitor that detects changes in the process characteristics and the adaptation mechanism that used information passed to it by the process monitor to update the controller parameters. Results: Experimental evaluation had been done in both indoor and outdoor environment where the robot communicates with the base station through its Wireless fidelity antenna and the performance monitor used a set of five performance criteria to access the fuzzy knowledge based controller. Conclusion: The proposed method had been found to be robust.

  12. Attribute Control Chart Construction Based on Fuzzy Score Number

    Directory of Open Access Journals (Sweden)

    Shiwang Hou

    2016-11-01

    Full Text Available There is much uncertainty and fuzziness in product quality attributes or quality parameters of a manufacturing process, so the traditional quality control chart can be difficult to apply. This paper proposes a fuzzy control chart. The plotted data was obtained by transforming expert scores into fuzzy numbers. Two types of nonconformity judgment rules—necessity and possibility measurement rules—are proposed. Through graphical analysis, the nonconformity judging method (i.e., assessing directly based on the shape feature of a fuzzy control chart is proposed. For four different widely used membership functions, control levels were analyzed and compared by observing gaps between the upper and lower control limits. The result of the case study validates the feasibility and reliability of the proposed approach.

  13. GA-Based Fuzzy Sliding Mode Controller for Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    W. L. Chiang

    2008-11-01

    Full Text Available Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC or an adaptive fuzzy sliding mode controller (AFSMC capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA-based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.

  14. Design and performance comparison of fuzzy logic based tracking controllers

    Science.gov (United States)

    Lea, Robert N.; Jani, Yashvant

    1992-01-01

    Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.

  15. Design of Fuzzy Controllers

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based...... on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design....

  16. The cycloconverter based on fuzzy controller for lift

    Institute of Scientific and Technical Information of China (English)

    WANG Gen-ping; YI Ling-zhi

    2005-01-01

    In order to ensure the lift can go up and down steadily and safely, a cycloconverter based on fuzzy control algorithm for lift was introduced, which can keep the output voltage to be symmetric sine wave. In this cycloconverter system,the main circuit structure was designed as circumfluence mode, with the strong DSP as the control unit, the fuzzy control policy of average weight accumulation decision was used to control the tuning of the triggering angle for thyristor in the main circuit, and then,the output voltage of the cycloconverter can be controlled. The experiment and simulation prove that the performance of the fuzzy cycloconverter is improved a lot and the output voltage is very similar to symmetric sine wave. This kind of cycloconverter can help the lift stop accurately, and the shock can be decreased.

  17. Advanced Fuzzy Logic Based Admission Control for UMTS System

    Directory of Open Access Journals (Sweden)

    P. Kejik

    2010-12-01

    Full Text Available The capacity of CDMA (Code Division Multiple Access systems is interference limited. Therefore radio resources management (RRM functions are used. They are responsible for supplying optimum coverage, ensuring efficient use of physical resources, and providing the maximum planned capacity. This paper deals with admission control techniques for UMTS (Universal Mobile Telecommunication System. A UMTS system model and four fuzzy logic based admission control algorithms are presented in this paper. Two new versions of fuzzy logic based admission control algorithms are presented there. All algorithms are mutually compared via simulations. Simulations show that the novel advanced fuzzy algorithm outperforms the other simulated algorithms (in terms of blocking probability, dropping probability and the number of active UEs in cell.

  18. A fuzzy behaviorist approach to sensor-based robot control

    Energy Technology Data Exchange (ETDEWEB)

    Pin, F.G.

    1996-05-01

    Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.

  19. Fuzzy Control Tutorial

    DEFF Research Database (Denmark)

    Dotoli, M.; Jantzen, Jan

    1999-01-01

    The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

  20. Fuzzy Control Tutorial

    DEFF Research Database (Denmark)

    Dotoli, M.; Jantzen, Jan

    1999-01-01

    The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control, and t......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

  1. DSP-based fuzzy implementation of indirect vector controlled induction motor

    Energy Technology Data Exchange (ETDEWEB)

    Radwan, T.S.; Uddin, M.N.; Rahman, M.A. [Memorial University of Newfoundland, Faculty of Engineering and Applied Science, St John' s, NF (Canada)

    2000-08-01

    In this paper, the fuzzy logic speed controller for high performance induction motor drive is proposed. The controller is based on the indirect vector control. The fuzzy logic speed controller is employed as an outer loop. The results of applying the developed fuzzy logic controllers are compared to those obtained by the application of a conventional PI controller. The results indicate superior performance and robustness of fuzzy logic controllers over the PI controller at any working conditions. (orig.)

  2. Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.

  3. Fuzzy controller based on chaos optimal design and its application

    Institute of Scientific and Technical Information of China (English)

    邹恩; 李祥飞; 张泰山

    2004-01-01

    In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.

  4. A reinforcement learning-based architecture for fuzzy logic control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  5. Switch Reluctance Motor Control Based on Fuzzy Logic System

    Directory of Open Access Journals (Sweden)

    S. Aleksandrovsky

    2012-01-01

    Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.

  6. Temporal Difference based Tuning of Fuzzy Logic Controller through Reinforcement Learning to Control an Inverted Pendulum

    Directory of Open Access Journals (Sweden)

    Raj kumar

    2012-08-01

    Full Text Available This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.

  7. Robust observer-based adaptive fuzzy sliding mode controller

    Science.gov (United States)

    Oveisi, Atta; Nestorović, Tamara

    2016-08-01

    In this paper, a new observer-based adaptive fuzzy integral sliding mode controller is proposed based on the Lyapunov stability theorem. The plant is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. Based on the classical sliding mode controller, the equivalent control effort is obtained to satisfy the sufficient requirement of sliding mode controller and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. In order to relax the norm-bounded constrains on the control law and solve the chattering problem of sliding mode controller, a fuzzy logic inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, for evaluating the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.

  8. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  9. Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

    Institute of Scientific and Technical Information of China (English)

    Hongli L(U); Lei JIA; Shulan KONG; Zhaosheng ZHANG

    2007-01-01

    In heating,ventilating and air-conditioning(HVAC)systems,there exist severe nonlinearity,time-varying nature,disturbances and uncertainties.A new predictive functional control based on Takagi-Sugeno(T-S)fuzzy model was proposed to control HVAC systems.The T-S fuzzy model of stabilized controlled process was obtained using the least squares method,then on the basis of global linear predictive model from T-S fuzzy model,the process was controlled by the predictive functional controller.Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model.Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness.Compared with the conventional PID controller,this control strategy has the advantages of less overshoot and shorter setting time,etc.

  10. Urban Intersection Traffic Signal Control Based on Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    魏武; 张毅; 张佐; 宋靖雁

    2002-01-01

    This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The "urgency degree" term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.

  11. Motion Control of the Soccer Robot Based on Fuzzy Logic

    Science.gov (United States)

    Coman, Daniela; Ionescu, Adela

    2009-08-01

    Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.

  12. Fuzzy Controllers Based Multipath Routing Algorithm in MANET

    Science.gov (United States)

    Pi, Shangchao; Sun, Baolin

    Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station or a wired backbone network. Due to the limited transmission range of wireless network nodes, multiple hops are usually needed for a node to exchange information with any other node in the network. Multipath routing allows the establishment of multiple paths between a single source and single destination node. The multipath routing in mobile ad hoc networks is difficult because the network topology may change constantly, and the available alternative path is inherently unreliable. This paper introduces a fuzzy controllers based multipath routing algorithm in MANET (FMRM). The key idea of FMRM algorithm is to construct the fuzzy controllers with the help to reduce reconstructions in the ad hoc network. The simulation results show that the proposed approach is effective and efficient in applications to the MANETs. It is an available approach to multipath routing decision.

  13. Development of single-chip fuzzy controller based on FFSI in binary

    Institute of Scientific and Technical Information of China (English)

    张吉礼; 欧进萍; 孙德兴

    2003-01-01

    Length and concise structure of fuzzy logic reasoning program and its real-time reasoning characteris-tic have their effect on the performance of a digital single-chip fuzzy controller. The control effect of a digitalfuzzy controller based on looking up fuzzy control responding table is only relative to the table and not relative tothe fuzzy control rules in the practical control process. Aiming at above problem and having combined fuzzy log-ic reasoning with digital operational characteristics of a single-chip microcomputer, functioning-fuzzy-subset in-ference (FFSI) in binary, in which triangle membership functions of error and error-in-change are all represen-ted in binary and singleton membership functions of control variable is binary too, has been introduced. The cir-cuit principle plans of a single-chip fuzzy controller have been introduced for development of its hardware, andthe primary program structure, fuzzy logic reasoning subroutine, serial communication subroutine with PC andreliability design of the fuzzy controller are all discussed in detail. The control of indoor temperature by a fuzzycontroller has been conducted using a testing-room thermodynamic system. Research results show that the FFSIin binary can exercise a concise fuzzy control in a single-chip fuzzy controller, and the fuzzy controller is there-fore reliable and possesses a high performance-price ratio.

  14. Neutral network and fuzzy logic based grate control; Roststyrning med neutrala naetverk och fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)

    2002-04-01

    Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between

  15. On PID Controller Design Using Knowledge Based Fuzzy System

    Directory of Open Access Journals (Sweden)

    Jana Nowakova

    2012-01-01

    Full Text Available The designing of PID controllers is a frequently discussed problem. Many of design methods have been developed, classic (analytical tuning methods, optimization methods etc. or not so common fuzzy knowledge based methods which are designed to achieve good setpoint following, corresponding time response etc. In this case, the new way of designing PID controller parameters is created where the above mentioned knowledge system based on relations of Ziegler-Nichols design methods is used, more precisely the combination of the both Ziegler-Nichols methods. The proof of efficiency of a proposed method and a numerical experiment is presented.

  16. Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

    Institute of Scientific and Technical Information of China (English)

    Qi Zhidong; Zhu Xinjian; Cao Guangyi

    2006-01-01

    Aiming at on-line controlling of Direct Methanol Fuel Cell (DMFC) stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative (PID) and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

  17. Fuzzy logic controllers: A knowledge-based system perspective

    Science.gov (United States)

    Bonissone, Piero P.

    1993-01-01

    Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

  18. Fuzzy Sets-based Control Rules for Terminating Algorithms

    Directory of Open Access Journals (Sweden)

    Jose L. VERDEGAY

    2002-01-01

    Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.

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

    Directory of Open Access Journals (Sweden)

    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.

  20. LMI-based output feedback fuzzy control of chaotic system with uncertainties

    Institute of Scientific and Technical Information of China (English)

    Tan Wen; Wang Yao-Nan; Duan Feng; Li Xiao-Hui

    2006-01-01

    This paper studies the robust fuzzy control for nonlinear chaotic system in the presence of parametric uncertainties. An uncertain Takagi-Sugeno (T-S) fuzzy model is employed for fuzzy modelling of an unknown chaotic system. A sufficient condition formulated in terms of linear matrix inequality (LMI) for the existence of fuzzy controller is obtained. Then the output feedback fuzzy-model-based regulator derived from the LMI solutions can guarantee the stability of the closed-loop overall fuzzy system. The T-S fuzzy model of the chaotic Chen system is developed as an example for illustration. The effectiveness of the proposed controller design methodology is finally demonstrated through computer simulations on the uncertain Chen chaotic system.

  1. Turbine speed control system based on a fuzzy-PID

    Institute of Scientific and Technical Information of China (English)

    SUN Jian-hua; WANG Wei; YU Hai-yan

    2008-01-01

    The flexibility demand of marine nuclear power plant is very high,the multiple parameters of the marine nuclear power plant with the once-through steam generator are strongly coupled,and the normal PID control of the turbine speed can't meet the control demand. This paper introduces a turbine speed Fuzzy-PID controller to coordinately control the steam pressure and thus realize the demand for quick tracking and steady state control over the turbine speed by using the Fuzzy control's quick dynamic response and PID control's steady state performance. The simulation shows the improvement of the response time and steady state performance of the control system.

  2. Intelligent control for nonlinear inverted pendulum based on interval type-2 fuzzy PD controller

    Directory of Open Access Journals (Sweden)

    Ahmad M. El-Nagar

    2014-03-01

    Full Text Available The interval type-2 fuzzy logic controller (IT2-FLC is able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of a fuzzy logic system (FLS. This paper proposes an interval type-2 fuzzy PD (IT2F-PD controller for nonlinear inverted pendulum. The proposed controller uses the Mamdani interval type-2 fuzzy rule based, interval type-2 fuzzy sets (IT2-FSs with triangular membership function, and the Wu–Mendel uncertainty bound method to approximate the type-reduced set. The proposed controller is able to minimize the effect of the structure uncertainties and the external disturbances for the inverted pendulum. The results of the proposed controller are compared with the type-1 fuzzy PD (T1F-PD controller in order to investigate the effectiveness and the robustness of the proposed controller. The simulation results show that the performance of the proposed controller is significantly improved compared with the T1F-PD controller. Also, the results show good performance over a wide range of the structure uncertainties and the effect of the external disturbances.

  3. Thickness and Shape Synthetical Adjustment for DC Mill Based on Dynamic Nerve-Fuzzy Control

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; WANG Ying-rui; ZHOU Hui-feng

    2004-01-01

    Due to the complexity of thickness and shape synthetical adjustment system and the difficulties to build a mathematical model, a thickness and shape synthetical adjustment scheme on DC mill based on dynamic nerve-fuzzy control was put forward, and a self-organizing fuzzy control model was established. The structure of the network can be optimized dynamically. In the course of studying, the network can automatically adjust its structure based on the specific questions and make its structure the optimal. The input and output of the network are fuzzy sets, and the trained network can complete the composite relation, the fuzzy inference. For decreasing the off-line training time of BP network, the fuzzy sets are encoded. The simulation results indicate that the self-organizing fuzzy control based on dynamic neural network is better than traditional decoupling PID control.

  4. Fuzzy Based Auto-coagulation Control Through Photometric Dispersion Analyzer

    Institute of Scientific and Technical Information of China (English)

    白桦; 李圭白

    2004-01-01

    The main role of water treatment plants is to supply high-quality safe drinking water. Coagulation is one of the most important stages of surface water treatment. The photometric dispersion analyzer(PDA) is a new optical method for flocculation monitoring, and is feasible to realize coagulation feedback control. The on line modification of the coagulation control system' s set point( or optimum dosing coagulant) has influenced the application of this technology in water treatment plant for a long time. A fuzzy control system incorporating the photometric dispersion analyzer was utilized in this coagulation control system. Proposed is a fuzzy logic inference control system by using Takagi and Sugeno' s fuzzy if-then rule for the self-correction of set point on line. Programmed is the dosing rate fuzzy control system in SIEMENS small-scale programmable logic controller. A 400 L/min middle-scale water treatment plant was utilized to simulate the reaction. With the changes of raw water quality, the set point was modified correctly in time, as well as coagulant dosing rate, and residual turbility before filtration was eligible and stable. Results show that this fuzzy inference and control system performs well on the coagulation control system through PDA.

  5. The foundations of fuzzy control

    CERN Document Server

    Lewis, Harold W

    1997-01-01

    Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.

  6. Gain Scheduling of PID Controller Based on Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Singh Sandeep

    2016-01-01

    Full Text Available This paper aims to utilize fuzzy rules and reasoning to determine the controller parameters and the PID controller generates the control signal. The objective of this study is to simulate the proposed scheme on various processes and arrive at results providing better response of the system when compared with best industrial auto-tuning technique: Ziegler-Nichols. The proposed scheme is based upon the Ultimate Gain (Ku and the Period (Tu of the system. The error and rate of change in error gains are tuned manually to get the desired response using LabVIEW. This can also be done with various optimization techniques. A thumb rule for choosing the ranges for Kc, Kd and Ki has been obtained experimentally.

  7. Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

    Science.gov (United States)

    Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

    2016-01-01

    In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

  8. Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

    Science.gov (United States)

    Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

    2016-01-01

    In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

  9. 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...

  10. A Substractive Clustering Based Fuzzy Hybrid Reference Control Design for Transient Response Improvement of PID Controller

    Directory of Open Access Journals (Sweden)

    Endra Joelianto

    2009-11-01

    Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.

  11. Backstepping design of missile guidance and control based on adaptive fuzzy sliding mode control

    Institute of Scientific and Technical Information of China (English)

    Ran Maopeng; Wang Qing; Hou Delong; Dong Chaoyang

    2014-01-01

    This paper presents an integrated missile guidance and control law based on adaptive fuzzy sliding mode control. The integrated model is formulated as a block-strict-feedback nonlinear system, in which modeling errors, unmodeled nonlinearities, target maneuvers, etc. are viewed as unknown uncertainties. The adaptive nonlinear control law is designed based on backstepping and sliding mode control techniques. An adaptive fuzzy system is adopted to approximate the coupling nonlinear functions of the system, and for the uncertainties, we utilize an online-adaptive control law to estimate the unknown parameters. The stability analysis of the closed-loop system is also conducted. Simulation results show that, with the application of the adaptive fuzzy sliding mode control, small miss distances and smooth missile trajectories are achieved, and the system is robust against system uncertainties and external disturbances.

  12. Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

    Directory of Open Access Journals (Sweden)

    Ashraf Ahmed Fahmy

    2014-03-01

    Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation.  Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.

  13. Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

    Institute of Scientific and Technical Information of China (English)

    谭冠政; 肖宏峰; 王越超

    2002-01-01

    A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on-line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors xp, xi, and xd are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

  14. Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

  15. A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    OpenAIRE

    Abdul Kareem; Mohammad Fazle Azeem

    2012-01-01

    This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...

  16. Fuzzy PID control algorithm based on PSO and application in BLDC motor

    Science.gov (United States)

    Lin, Sen; Wang, Guanglong

    2017-06-01

    A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

  17. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  18. A Fuzzy Logic Based Supervisory Hierarchical Control Scheme for Real Time Pressure Control

    Institute of Scientific and Technical Information of China (English)

    N.Kanagaraj; P.Sivashanmugam; S.Paramasivam

    2009-01-01

    This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system.The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances.This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range.The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller.The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time.To demonstrate the effectiveness,the results of the proposed hierarchical controller,fuzzy controller and conventional proportional-integral (PI) controller are analyzed.The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.

  19. fuzzy control technique fuzzy control technique applied to modified ...

    African Journals Online (AJOL)

    eobe

    ABSTRACT. In this paper, fuzzy control technique is applied to the modified mathematical model for malaria control presented ... be devised for rule-based systems that deals with continuous ... necessary to use fuzzy logic as it is not easy to follow a particular .... point movement and control is realized and designed. (e.g. α1 ...

  20. WALL-FOLLOWING BEHAVIOR-BASED MOBILE ROBOT USING PARTICLE SWARM FUZZY CONTROLLER

    Directory of Open Access Journals (Sweden)

    Andi Adriansyah

    2016-02-01

    Full Text Available Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO. A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.

  1. Supervisory Control of Fuzzy Discrete Event Systems Based on Agent

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    FDES (fuzzy discrete event systems) can effectively represent a kind of complicated systems involving deterministic uncertainties and vagueness as well as human subjective observation and judgement from the view of discrete events, here the information system is divided into some independent intelligent entitative Agents. The concept of information processing state based on Agents was proposed. The processing state of Agent can be judged by some assistant observation parameters about the Agent and its environment around, and the transition among these states can be represented by FDES based on rules. In order to ensure the harmony of the Agents for information processing, its upstream and downstream buffers are considered in the modeling of the Agent system,and the supervisory controller based on FDES is constructed. The processing state of Agent can be adjusted by the supervisory controller to improve the stability of the system and the efficiency of resource utilization during the process according to the control policies. The result of its application was provided to illustrate the validity of the supervisory adjustment.

  2. Active Queue Management in TCP Networks Based on Fuzzy-Pid Controller

    Directory of Open Access Journals (Sweden)

    Hossein ASHTIANI

    2012-01-01

    Full Text Available We introduce a novel and robust active queue management (AQM scheme based on a fuzzy controller, called hybrid fuzzy-PID controller. In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In this paper a novel Fuzzy-based proportional-integral derivative (PID controller, which acts as an active queue manager (AQM for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TCP/IP networks. A new hybrid controller is proposed and compared with traditional RED based controller. Simulations are carried out to demonstrate the effectiveness of the proposed method and show that, the new hybrid fuzzy PID controller provides better performance than random early detection (RED and PID controllers

  3. Investigation into Model-Based Fuzzy Logic Control

    Science.gov (United States)

    1993-12-01

    Logic, in this context, will be used to bridge the gap between linear systems theory and nonlinear control application. Said another way, the language of...to demonstrate the value of applying both Fuzzy Set theory and linear systems theory to the control of nonlinear plants. It is conjectured that the... linear systems theory , to the extent possible. * The plant should be as simple as possible to dearly demonstrate the the developed controller. The

  4. Neuro-fuzzy based Controller for Solving Active Power Filter

    Directory of Open Access Journals (Sweden)

    Homayoun Ebrahimian

    2016-03-01

    Full Text Available In this paper, two soft computing techniques by fuzzy logic, neural network are used to design alternative control schemes for switching the APF active power filter (APF. The control of a shunt active power filter designed for harmonic and reactive current mitigation. Application of the mentioned model has been combined by an intelligent algorithm for improving the efficiency of proposed controller. Effectiveness of the proposed method has been applied over test case and shows the validity of proposed model.

  5. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    OpenAIRE

    Boumediene ALLAOUA; Laoufi, Abdellah; Brahim GASBAOUI; Nasri, Abdelfatah; Abdessalam ABDERRAHMANI

    2008-01-01

    In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...

  6. Application of Fuzzy Control with Market-Based Control Strategy to Structures

    Directory of Open Access Journals (Sweden)

    Gang Li

    2013-01-01

    Full Text Available The market-based control as a control strategy is developed based on an analogy between the control force-energy source in the structural area and the supply demand in the free market. The optimal problem of control force from an actuator is transformed to that of the allocation resource in the market. Since the supply-demand relation model and iteration procedure for the optimal price solution are necessary and relatively hard to understand and perform for civil engineers, the fuzzy logical method is proposed in the framework of the market-based control to acquire an equivalent system corresponding to the market-based control method. An equivalent fuzzy logical rule is established through analyzing a single-degree-of-freedom system with the controller using the MBC strategy under earthquakes. The results show that the fuzzy logical method is able to also reduce the displacement and acceleration responses effectively similar to the MBC method, and the consumed computational time for the fuzzy logical method is obviously saved.

  7. 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.

  8. MOEA-Based Fuzzy Control for Seismically Excited Structures

    Science.gov (United States)

    Ning, Xiang-Liang; Tan, Ping; Zhou, Fu-Lin

    To guarantee the safety and functionality of structures simultaneously at different levels of seismic loadings, this paper proposes a multi-objective switching fuzzy control (MOSFC) strategy. MOSFC functions as a trigger with two control states considered. When the structure is at the state of linear, the main objection of control is the peak acceleration. On the other hand, once the nonlinear appears, the control of peak inter-storey drift is the main objection. Multi-objective genetic algorithm, NSGA-II, is employed for optimizing the fuzzy control rules. A scaled model of a six-storey building with two MR dampers installed at the two bottom floors is simulated here. Linear and Nonlinear numerical simulations demonstrate the effectiveness and robustness.

  9. Adaptive Control of MEMS Gyroscope Based on T-S Fuzzy Model

    Directory of Open Access Journals (Sweden)

    Yunmei Fang

    2015-01-01

    Full Text Available A multi-input multioutput (MIMO Takagi-Sugeno (T-S fuzzy model is built on the basis of a nonlinear model of MEMS gyroscope. A reference model is adjusted so that a local linear state feedback controller could be designed for each T-S fuzzy submodel based on a parallel distributed compensation (PDC method. A parameter estimation scheme for updating the parameters of the T-S fuzzy models is designed and analyzed based on the Lyapunov theory. A new adaptive law can be selected to be the former adaptive law plus a nonnegative in variable to guarantee that the derivative of the Lyapunov function is smaller than zero. The controller output is implemented on the nonlinear model and T-S fuzzy model, respectively, for the purpose of comparison. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme and the correctness of the T-S fuzzy model.

  10. Control of Yaw Disturbance Using Fuzzy Logic Based Yaw Stability Controller

    Directory of Open Access Journals (Sweden)

    S. Krishna

    2014-01-01

    Full Text Available Yaw stability is an important consideration for the vehicle directional stability and handling behavior during emergency maneuvers. In order to maintain the desired path of the vehicle, in presence of disturbances due to cross wind, different road conditions, and tire deflections, a fuzzy logic based yaw stability controller is proposed in this paper. Proposed control system receives yaw rate error, steering angle given by the driver, and side slip angle as inputs, for calculating the additional steering angle as output, for maintaining the yaw stability of the vehicle. As the side slip angle cannot be measured directly in a vehicle, it was estimated using a model based Kalman observer. A two-degrees-of-freedom vehicle model is considered in the present work. The effect of disturbance on yaw rate and yaw rate error of the vehicle is simulated for sinusoidal, step maneuver and compared with the existing fuzzy control system which uses two inputs such as steering angle and yaw rate. The simulation results show better performance of the proposed fuzzy based yaw controller as compared with existing control system. Proposed fuzzy based yaw stability controller can be implemented in steer-by-wire system for an active front steering of a road vehicle.

  11. Signal frequency based self-tuning fuzzy controller for semi-active suspension system

    Institute of Scientific and Technical Information of China (English)

    孙涛; 黄震宇; 陈大跃; 汤磊

    2003-01-01

    A new kind of fuzzy control scheme, based on the identification of the signal's main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method adjusts the fuzzy controller to achieve the best isolation effect by analyzing the main frequency's characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the optimal damping ratio is the output. Simulation results indicated that the proposed control method is very effective in isolating the vibration.

  12. Signal frequency based self-tuning fuzzy controller for semi-active suspension system

    Institute of Scientific and Technical Information of China (English)

    孙涛; 黄震宇; 陈大跃; 汤磊

    2003-01-01

    A new kind of fuzzy control scheme, based on the identification of the signal' s main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method ad-justs the fuzzy controller to achieve the best isolation effect by analyzing the main frequency' s characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the op-timal damping ratio is the output. Simulation results indicated that the proposed control method is very effec-tive in isolating the vibration.

  13. Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Jian-an; MIAO Qing-ying; GUO Zhao-xia; SHAO Shi-huang

    2002-01-01

    This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

  14. Design High Efficiency-Minimum Rule Base PID Like Fuzzy Computed Torque Controller

    Directory of Open Access Journals (Sweden)

    Alireza Khalilian

    2014-06-01

    Full Text Available The minimum rule base Proportional Integral Derivative (PID Fuzzy Computed Torque Controller is presented in this research. The popularity of PID Fuzzy Computed Torque Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy Computed Torque Controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI controller to have the minimum rule base. However computed torque controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator’s dynamic equation. This research is used to reduce or eliminate the computed torque controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

  15. The Redundant Arm Self-motion Control Based on Self-tuning Fuzzy PID Controller

    Institute of Scientific and Technical Information of China (English)

    Liu Yu(刘宇); Sun Lining; Du Zhijiang

    2004-01-01

    A fuzzy control algorithm based on self-tuning PID proportional factor is presented. To a certain degree, it overcomes robot motion control's nonlinearity and uncertainty caused by joints coupled and friction, and decreases overshoot of end manipulator's tracking desired curves. The controller's structure is very simple but effective. With this control method, a 7-DOF redundant arm's self-motion developed by the authors is investigated. Research results show that the said controller restrains track overshoot and possesses preferable merits.

  16. Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

    Directory of Open Access Journals (Sweden)

    Hajer Omrane

    2016-01-01

    Full Text Available This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

  17. Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

    Science.gov (United States)

    Masmoudi, Mohamed Slim; Masmoudi, Mohamed

    2016-01-01

    This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path. PMID:27688748

  18. Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation.

    Science.gov (United States)

    Omrane, Hajer; Masmoudi, Mohamed Slim; Masmoudi, Mohamed

    This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

  19. Indirect Vector Control of an Induction Motor with Fuzzy-Logic based Speed Controller

    Directory of Open Access Journals (Sweden)

    BIROU, I.

    2010-02-01

    Full Text Available The aim of this paper is to present a new speed control structure for induction motors (IM by using fuzzy-logic based speed controllers. A fuzzy controller is designed to achieve fast dynamic response and robustness for low and high speeds. Different types of membership functions of the linguistic variables and output/input characteristics are analyzed. A simple but robust structure enables a wide range speed control of the driving system. The rotor flux field oriented control (FOC is realized by using a flux observer based on the IM model with nonlinear parameters. The control is extended to operate also in the field weakening region with an optimal rotor flux regulation. The control structure was implemented on a computer system, based on a fixed point digital signal processor (DSP. To verify the performances of the proposed driving system, simulated and experimental results are presented.

  20. AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

    Institute of Scientific and Technical Information of China (English)

    JIA Li; YU Jinshou

    2005-01-01

    In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.

  1. Modeling and Stability Analysis for Non-linear Network Control System Based on T-S Fuzzy Model

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hong; FANG Huajing

    2007-01-01

    Based on the T-S fuzzy model, this paper presents a new model of non-linear network control system with stochastic transfer delay. Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model. Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model. All these results present a new approach for networked control system analysis and design.

  2. Research on Coordinated Robotic Motion Control Based on Fuzzy Decoupling Method in Fluidic Environments

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-01-01

    Full Text Available The underwater recovery of autonomous underwater vehicles (AUV is a process of 6-DOF motion control, which is related to characteristics with strong nonlinearity and coupling. In the recovery mission, the vehicle requires high level control accuracy. Considering an AUV called BSAV, this paper established a kinetic model to describe the motion of AUV in the horizontal plane, which consisted of nonlinear equations. On the basis of this model, the main coupling variables were analyzed during recovery. Aiming at the strong coupling problem between the heading control and sway motion, we designed a decoupling compensator based on the fuzzy theory and the decoupling theory. We analyzed to the rules of fuzzy compensation, the input and output membership functions of fuzzy compensator, through compose operation and clear operation of fuzzy reasoning, and obtained decoupling compensation quantity. Simulation results show that the fuzzy decoupling controller effectively reduces the overshoot of the system, and improves the control precision. Through the water tank experiments and analysis of experimental data, the effectiveness and feasibility of AUV recovery movement coordinated control based on fuzzy decoupling method are validated successful, and show that the fuzzy decoupling control method has a high practical value in the recovery mission.

  3. Fault tolerant control based on interval type-2 fuzzy sliding mode controller for coaxial trirotor aircraft.

    Science.gov (United States)

    Zeghlache, Samir; Kara, Kamel; Saigaa, Djamel

    2015-11-01

    In this paper, a robust controller for a Six Degrees of Freedom (6 DOF) coaxial trirotor helicopter control is proposed in presence of defects in the system. A control strategy based on the coupling of the interval type-2 fuzzy logic control and sliding mode control technique are used to design a controller. The main purpose of this work is to eliminate the chattering phenomenon and guaranteeing the stability and the robustness of the system. In order to achieve this goal, interval type-2 fuzzy logic control has been used to generate the discontinuous control signal. The simulation results have shown that the proposed control strategy can greatly alleviate the chattering effect, and perform good reference tracking in presence of defects in the system.

  4. Speed Control System on Marine Diesel Engine Based on a Self-Tuning Fuzzy PID Controller

    Directory of Open Access Journals (Sweden)

    Naeim Farouk

    2012-03-01

    Full Text Available The degree of speed control of ship machinery effects on the economics and optimization of the machinery configuration and operation. All marine vessel ranging need some sort of speed control system to control and govern the speed of the marine diesel engines. This study presents a self-tuning fuzzy PID control system for speed control system of marine diesel engine. The system under consideration is a fourth-order plant with highly dynamic and uncertain environments. The current speed controllers for marine/traction diesel engines based on PID Controller cannot fully handle the uncertainties associated with such dynamic environments. A fuzzy logic control algorithm is used to estimate the PID coefficients in order to handle such uncertainties to produce a better control performance. Simulation tests were established using Simulink of MATLAB. The obtained results have demonstrated the feasibility and effectiveness of the proposed approach. Simulation results are represented in this study.

  5. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...

  6. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well......The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...

  7. 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.

  8. A minimum-time based fuzzy logic dynamic braking resistor control for sub-synchronous resonance

    Energy Technology Data Exchange (ETDEWEB)

    Rahim, A.H.M.A. [University of Petroleum and Minerals, Dhahran (Saudi Arabia). Dept. of Electrical Engineering

    2004-03-01

    Dynamically switched resistor banks connected to the generator transformer bus are known to improve transient stability of the power system. In this article, a braking resistor control strategy designed through fuzzy logic control theory has been proposed to damp the slowly growing sub-synchronous resonant (SSR) frequency oscillations of a power system. The proposed control has been tested on the IEEE second benchmark model for SSR studies. A fuzzy logic controller designed through a classical minimum-time strategy was compared with a general fuzzy strategy employing generator speed variation and acceleration as input to the controller. It was observed that the proposed minimum-time based fuzzy controller provides better damping control; and it is computationally very efficient. (author)

  9. Fuzzy Controller based Neutral Current Harmonic Suppression in Distribution System

    Directory of Open Access Journals (Sweden)

    T.Guna Sekar

    2013-10-01

    Full Text Available Recent surveys of three-phase four-wire electric systems, buildings and industrial plants with computers and non-linear loads shows the excessive currents in the neutral conductor. This is mainly due to unbalancing system and non-linear loads. Third order harmonics are much dominant in the neutral conductor due to the presence of zero sequence components. In response to this concern, this paper presents a concept of series active filter scheme to suppress the neutral current harmonics to reduce the burden of the secondary of the distribution transformer. In this scheme, the series active filteris connected in series with the neutral conductor to eliminate the zero sequence components in the neutral conductor. In this paper, Fuzzy based controller is used to extract the harmonic component in the neutral conductor. The proposed method improves the overall performance of the system and eliminates the burden of the neutral conductor. To validate the proposed simulation results, a scale-down prototype experimental model is developed.

  10. 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.

  11. Design and implementation of the tree-based fuzzy logic controller.

    Science.gov (United States)

    Liu, B D; Huang, C Y

    1997-01-01

    In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.

  12. Fuzzy variable impedance control based on stiffness identification for human-robot cooperation

    Science.gov (United States)

    Mao, Dachao; Yang, Wenlong; Du, Zhijiang

    2017-06-01

    This paper presents a dynamic fuzzy variable impedance control algorithm for human-robot cooperation. In order to estimate the intention of human for co-manipulation, a fuzzy inference system is set up to adjust the impedance parameter. Aiming at regulating the output fuzzy universe based on the human arm’s stiffness, an online stiffness identification method is developed. A drag interaction task is conducted on a 5-DOF robot with variable impedance control. Experimental results demonstrate that the proposed algorithm is superior.

  13. Design of Takagi-Sugeno fuzzy model based nonlinear sliding model controller

    Institute of Scientific and Technical Information of China (English)

    Xu Yong; Chen Zengqiang; Yuan Zhuzhi

    2005-01-01

    A design method is presented for Takagi-Sugeno (T-S) fuzzy model based nonlinear sliding model controller. First, the closed-loop fuzzy system is divided into a set of dominant local linear systems according to operating sub-regions. In each sub-region the fuzzy system consists of nominal linear system and a group of interacting systems. Then the controller composed two parts is designed. One part is designed to control the nominal system, the other is designed to control the interacting systems with sliding mode theory. The proposed controller can improve the robustness and guarantee tracking performance of the fuzzy system. Stability is guaranteed without finding a common positive definite matrix.

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

    Science.gov (United States)

    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.

  15. The Motion Path Study of Measuring Robot Based on Variable Universe Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Ma Guoqing

    2017-01-01

    Full Text Available For the problem of measuring robot requires a higher positioning, firstly learning about the error overview of the system, analysised the influence of attitude, speed and other factors on systematic errors. Then collected and analyzed the systematic error curve in the track to complete the planning process. The last adding fuzzy control in both cases, by comparing with the original system, can found that the method based on fuzzy control system can significantly reduce the error during the motion.

  16. Maximum Energy Extraction Control for Wind Power Generation Systems Based on the Fuzzy Controller

    Science.gov (United States)

    Kamal, Elkhatib; Aitouche, Abdel; Mohammed, Walaa; Sobaih, Abdel Azim

    2016-10-01

    This paper presents a robust controller for a variable speed wind turbine with a squirrel cage induction generator (SCIG). For variable speed wind energy conversion system, the maximum power point tracking (MPPT) is a very important requirement in order to maximize the efficiency. The system is nonlinear with parametric uncertainty and subject to large disturbances. A Takagi-Sugeno (TS) fuzzy logic is used to model the system dynamics. Based on the TS fuzzy model, a controller is developed for MPPT in the presence of disturbances and parametric uncertainties. The proposed technique ensures that the maximum power point (MPP) is determined, the generator speed is controlled and the closed loop system is stable. Robustness of the controller is tested via the variation of model's parameters. Simulation studies clearly indicate the robustness and efficiency of the proposed control scheme compared to other techniques.

  17. Fuzzy-based Navigation and Control of a Non-Holonomic Mobile Robot

    CERN Document Server

    Rashid, Razif; Begam, Mumtaj; Arrofiq, M

    2010-01-01

    In recent years, the use of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, a theoretical model of a fuzzy based controller for an autonomous mobile robot is developed. The paper begins with the mathematical model of the robot that involves the kinematic model. Then, the fuzzy logic controller is developed and discussed in detail. The proposed method is successfully tested in simulations, and it compares the effectiveness of three different set of membership of functions. It is shown that fuzzy logic controller with input membership of three provides better performance compared with five and seven membership functions.

  18. Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; LIU Hong-min; ZHOU Hui-feng

    2005-01-01

    For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully,and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy.

  19. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2008-12-01

    Full Text Available In this paper, an intelligent controller of the DC (Direct current Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became very strong, gives a very good results and possesses good robustness.

  20. Fuzzy Logic Controller based on geothermal recirculating aquaculture system

    Directory of Open Access Journals (Sweden)

    Hanaa M. Farghally

    2014-01-01

    Full Text Available One of the most common uses of geothermal heat is in recirculation aquaculture systems (RAS where the water temperature is accurately controlled for optimum growing conditions for sustainable and intensive rearing of marine and freshwater fish. This paper presents a design for RAS rearing tank and brazed heat exchanger to be used with geothermal energy as a source of heating water. The heat losses from the RAS tank are calculated using Geo Heat Center Software. Then a plate type heat exchanger is designed using the epsilon – NTU analysis method. For optimal growth and abundance of production, a Fuzzy Logic control (FLC system is applied to control the water temperature (29 °C. A FLC system has several advantages over conventional techniques; relatively simple, fast, adaptive, and its response is better and faster at all atmospheric conditions. Finally, the total system is built in MATLAB/SIMULINK to study the overall performance of control unit.

  1. Constrained predictive control based on T-S fuzzy model for nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Su Baili; Chen Zengqiang; Yuan Zhuzhi

    2007-01-01

    A constrained generalized predictive control (GPC) algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno (T-S) fuzzy model based on the fuzzy cluster algorithm and the orthogonal least square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented.This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

  2. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique.

    Science.gov (United States)

    Kumarasabapathy, N; Manoharan, P S

    2015-01-01

    This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.

  3. Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator

    Directory of Open Access Journals (Sweden)

    Srinivasan Alavandar

    2008-01-01

    Full Text Available The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy. This paper presents the control of six degrees of freedom robot arm (PUMA Robot using Adaptive Neuro Fuzzy Inference System (ANFIS based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

  4. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

    Science.gov (United States)

    Kumarasabapathy, N.; Manoharan, P. S.

    2015-01-01

    This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895

  5. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

    Directory of Open Access Journals (Sweden)

    N. Kumarasabapathy

    2015-01-01

    Full Text Available This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs. The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.

  6. Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method

    Directory of Open Access Journals (Sweden)

    Yeh-Hsiang Ho

    2013-07-01

    Full Text Available Lithium ion (Li-ion batteries have been widely used in various kinds of applications, including consumer electronics, green energy systems and electrical vehicles. Since the charging method has a significant influence on the performance and lifetime of Li-ion batteries, an intelligent charging algorithm which can properly determine the charging current is essential. In this study, a fuzzy-logic-control-based (FLC-based five-stage Li-ion battery charger is proposed. The proposed charger takes the temperature rise and the gradient of temperature rise of battery into account, and adjusts the charging current accordingly. To further improve the performance of the proposed FLC, the fuzzy-based Taguchi method is utilized to determine the optimal output membership functions (MFs. Comparing with the conventional constant current-constant voltage (CC-CV method, the charging time, charging efficiency, average temperature rise and the obtained cycle life of the Li-ion battery are improved by about 58.3%, 1.65%, 26.7% and 59.3%, respectively.

  7. Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator

    OpenAIRE

    Du, Zhijiang; Wang, Wei; Yan, Zhiyuan; Dong, Wei; Wang, Weidong

    2017-01-01

    In order to get natural and intuitive physical interaction in the pose adjustment of the minimally invasive surgery manipulator, a hybrid variable admittance model based on Fuzzy Sarsa(?)-learning is proposed in this paper. The proposed model provides continuous variable virtual damping to the admittance controller to respond to human intentions, and it effectively enhances the comfort level during the task execution by modifying the generated virtual damping dynamically. A fuzzy partition de...

  8. Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots

    Directory of Open Access Journals (Sweden)

    Gyula Mester

    2014-07-01

    Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.

  9. Performance evaluation of fuzzy and BPN based congestion controller in WSN

    Directory of Open Access Journals (Sweden)

    Rekha Chakravarthi

    2015-04-01

    Full Text Available In a Wireless Sensor Network when an event is detected, the network traffic increases. It in turn increases the flow of data packets and congestion. Congestion in Wireless Sensor Network plays a vital role in degrading the performance of the network. Hence it necessitates, developing a novel technique to control congestion. In this paper, soft computing based congestion control technique is proposed. Fuzzy logic and neural network are the soft computing tools used for estimating the packet drop. The performance of the proposed technique is evaluated using Accuracy. From the results, it is proved that neural network based congestion control technique provides better results than fuzzy based congestion control technique.

  10. Fuzzy Control Based on Neural Networks for Armored Vehicle Electric Drive System

    Institute of Scientific and Technical Information of China (English)

    MA Xiao-jun; LI Hua; ZHANG Jian; ZHANG Yu-nan

    2006-01-01

    In order to meet rigorous demands of control of electric motors in armored vehicle electric drive system and make the system of strong robustness and antijamming capability, a fuzzy control method based on neural networks is put forward. The simulation model of the armored vehicle electric drive system is built up to test the validity of the control. Simulation experiments show that when load is increased or decreased suddenly, the system adopting fuzzy control based on neural networks is insensitive to parameter change and has little overshooting and oscillation compared with PID control.

  11. DSP Based Vector Control of Five-Phase Induction Using Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    Zakaria Mohamed Salem

    2012-03-01

    Full Text Available Abstract - This paper proposes an indirect field oriented controller for five-phase induction motor drives. The controller is based on fuzzy logic control technique. Simulation is carried out by using the Matlab/Simulink package. A complete control system experimentally implemented using digital signal processing (DSP board. The performance of the proposed system is investigated at different operating conditions. The proposed controller is robust and suitable to high performance five-phase induction motor drives. Simulation and experimental results validate the proposed approaches.

  12. Fuzzy PID Control Method for Internet-based Tele-operation Manipulators System

    Directory of Open Access Journals (Sweden)

    Wei Gao

    2013-11-01

    Full Text Available Trajectory tracking control problem for internet-based tele-operation system is researched in this paper. The control structure of master and slave tele-operation manipulators adapts bilateral servo control architecture with force deviation feedback. The simulation model of three degrees of freedom (3-DOF manipulator is presented. In order to ensure the synchronization of positions of the master and slave manipulators, a fuzzy PID control method is proposed. This control algorithm is to adjust the three parameters of PID controller online by fuzzy control method. The contrast simulation experiments of PID and fuzzy PID control methods show that the proposed control method can effectively improve the force and position tracking performance and reduce time delay.

  13. Modelling on fuzzy control systems

    Institute of Scientific and Technical Information of China (English)

    LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)

    2002-01-01

    A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.

  14. Fuzzy Logic Controller Based Distributed Generation Integration Strategy for Stochastic Performance Improvement

    Directory of Open Access Journals (Sweden)

    Jagdish Prasad Sharma

    2016-01-01

    Full Text Available In the restructured environment, distributed generation (DG is considered as a very promising option due to a high initial capital cost of conventional plants, environmental concerns, and power shortage. Apart from the above, distributed generation (DG has also abilities to improve performance of feeder. Most of the distribution feeders have radial structure, which compel to observe the impact of distributed generations on feeder performance, having different characteristics and composition of time varying static ZIP load models. Two fuzzy-based expert system is proposed for selecting and ranking the most appropriated periods to an integration of distributed generations with a feeder. Madami type fuzzy logic controller was developed for sizing of distributed generation, whereas Sugeno type fuzzy logic controller was developed for the DG location. Input parameters for Madami fuzzy logic controller are substation reserve capacity, feeder power loss to load ratio, voltage unbalance, and apparent power imbalances. DG output, survivability index, and node distance from substation are chosen as input to Sugeno type fuzzy logic controller. The stochastic performance of proposed fuzzy expert systems was evaluated on a modified IEEE 37 node test feeder with 15 minutes characteristics time interval varying static ZIP load models.

  15. Modelling and Control of the Qball X4 Quadrotor System based on Pid and Fuzzy Logic Structure

    Science.gov (United States)

    Bodrumlu, Tolga; Turan Soylemez, Mehmet; Mutlu, Ilhan

    2017-01-01

    This work focuses on a quadrocopter model, which was developed by QuanserTM and named as Qball X4. First, mathematical model of the Qball X4 is obtained. Then, a conventional PID control technique is presented. This PID control parameters come from Qball user manual. After the presentation of conventional PID control, as an extension of the conventional PID control theory, a different fuzzy controller structure is given. The proposed fuzzy controller structure is based on fuzzy logic and its name is PID type fuzzy controller. All of the simulations are done in MATLABTM environment.

  16. A complex control system based on the fuzzy PID control and state predictor feedback control

    Institute of Scientific and Technical Information of China (English)

    Zhengxi Li; Jie Liu; Dehui Sun; Rentao Zhao

    2004-01-01

    A multi-mode adaptive controller was proposed. The controller features in the combination of Bang-bang and Fuzzy PID controls with state predictor. When large error exists, the controller operates in Bang-bang mode, otherwise it works as a fuzzy PID controller. For only few parameters to be adjusted, the real time controlled system achieveed good stability and fast response. Furthermore, the introduction of state observer was also discussed to extend the capability of the proposed controller to the plant with time-delay factors. The classical PID controller and the multi-mode controller were applied to the same second-order system successively. By comparison of the simulation results, the effectiveness of the controller were shown. At last, on electric-wire production line, this approach was practiced to control electric-wire diameter with an additive random disturbance signal. The test result further proved the effectiveness of the multi-mode controller.

  17. A Fuzzy PI Speed Controller based on Feedback Compensation Strategy for PMSM

    Directory of Open Access Journals (Sweden)

    Ou Sheng

    2015-05-01

    Full Text Available in order to solve the problem of robustness or anti-disturbance of the traditional PI speed controller in the permanent magnet synchronous motor. A fuzzy PI speed controller based on load torque feedback compensation is proposed for the permanent magnet synchronous motor. The combination of fuzzy PI control strategy and load feedback compensation method can enhance the robustness and disturbance rejection of the speed loop. According to the validated results of simulation and experiments, by using this PMSM speed controller, the robustness of the system speed control was enhanced markedly, and the capacity of anti-disturbance was also improved significantly.

  18. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

    Directory of Open Access Journals (Sweden)

    Zain Anwar Ali

    2016-05-01

    Full Text Available In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV. The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST controller with model reference adaptive control (MRAC, in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

  19. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

    Science.gov (United States)

    Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

    2016-05-09

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

  20. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

    Science.gov (United States)

    Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

    2016-01-01

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

  1. Controller design of uncertain nonlinear systems based on T-S fuzzy model

    Institute of Scientific and Technical Information of China (English)

    Songtao ZHANG; Shizhen BAI

    2009-01-01

    A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group (EMORG) is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.

  2. Polynomial fuzzy model-based control systems stability analysis and control synthesis using membership function dependent techniques

    CERN Document Server

    Lam, Hak-Keung

    2016-01-01

    This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function dependent analysis are considered. The membership-function-dependent analysis offers a new research direction for fuzzy-model-based control systems by taking into account the characteristic and information of the membership functions in the stability analysis. The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for read...

  3. Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

    Directory of Open Access Journals (Sweden)

    B.S.K.K Ibrahim

    2011-12-01

    Full Text Available The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES. FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI. A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment.

  4. H∞ tracking control of coupled spatiotemporal chaos based on fuzzy models

    Institute of Scientific and Technical Information of China (English)

    Dou Chun-Xia

    2005-01-01

    Due to the interactions among coupled spatiotemporal subsystems, it is difficult to achieve the tracking control of the coupled spatiotemporal chaos. However, every subsystem of the coupled spatiotemporal chaos can be approximated by a set of fuzzy models, of which each represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Based on these fuzzy models, an H∞ fuzzy tracking control scheme is suggested,where a linear matrix inequalities (LMI) is employed to represent the feedback controller. The parameters of controller are obtained by using convex optimization techniques of LMI. The model reference tracking control of the coupled spatiotemporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performance is tested by simulation as an example.

  5. Controlling Torque Distribution for Parallel Hybrid Vehicle Based on Hierarchical Structure Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    HuangMiao-hua; JinGuo-dong

    2003-01-01

    The Hierarchical Structure Fuzzy Logic Control(HSFLC) strategies of torque distribute for Parallel Hybrid Electric Vehicle (PHEV) in the mocle of operation of the vehicle i. e. , acceleration, cruise, deceleration etc. have been studied. Using secondly developed the hybrid vehicle simulation tool ADVISOR, the dynamic model of PHEV has been set up by MATLAB/SIMULINK. The engine, motor as well as the battery characteristics have been studied. Simulation results show that the proposed hierarchical structured fuzzy logic control strategy is effective over the entire operating range of the vehicle in terms of fuel economy. Based on the analyses of the simulation results and driver's experiences, a fuzzy controller is designed and developed to control the torque distribution. The controller is evaluated via hardware-in-the-loop simulator (HILS). The results show that controller verify its value.

  6. 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)

  7. Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control.

    Science.gov (United States)

    Mahfouf, M; Abbod, M F; Linkens, D A

    2003-01-01

    Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

  8. Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system

    Directory of Open Access Journals (Sweden)

    C. Boldisor

    2009-12-01

    Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.

  9. Fuzzy Inference Based Obstacle Avoidance Control of Electric Powered Wheelchair Considering Driving Risk

    Science.gov (United States)

    Kiso, Atsushi; Murakami, Hiroki; Seki, Hirokazu

    This paper describes a novel obstacle avoidance control scheme of electric powered wheelchairs for realizing the safe driving in various environments. The “electric powered wheelchair” which generates the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people; however, the driving performance must be further improved because the number of driving accidents caused by elderly operator's narrow sight and joystick operation errors is increasing. This paper proposes a novel obstacle avoidance control scheme based on fuzzy algorithm to prevent driving accidents. The proposed control system determines the driving direction by fuzzy algorithm based on the information of the joystick operation and distance to obstacles measured by ultrasonic sensors. Fuzzy rules to determine the driving direction are designed surely to avoid passers-by and walls considering the human's intent and driving environments. Some driving experiments on the practical situations show the effectiveness of the proposed control system.

  10. Adaptive-Fuzzy Controller Based Shunt Active Filter for Power Line Conditioners

    Directory of Open Access Journals (Sweden)

    KamalaKanta Mahapatra

    2011-08-01

    Full Text Available This paper presents a novel Fuzzy Logic Controller (FLC in conjunction with Phase Locked Loop (PLL based shunt active filter for Power Line Conditioners (PLCs to improve the power quality in the distribution system. The active filter is implemented with current controlled Voltage Source Inverter (VSI for compensating current harmonics and reactive power at the point of common coupling. The VSI gate control switching pulses are derived from proposed Adaptive-Fuzzy-Hysteresis Current Controller (HCC and this method calculates the hysteresis bandwidth effectively using fuzzy logic. The bandwidth can be adjusted based on compensation current variation, which is used to optimize the required switching frequency and improves active filter substantially. These shunt active power filter system is investigated and verified under steady and transient-state with non-linear load conditions. This shunt active filter is in compliance with IEEE 519 and IEC 61000-3 recommended harmonic standards.

  11. Direct Torque Control of IPMSM to Improve Torque ripple and Efficiency based on Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    B. Mirzaeian Dehkordi

    2012-09-01

    Full Text Available In this paper, a stator-flux-reference frame control method is proposed in order to control the speed and torque of an Interior Permanent Magnet Synchronous Machine (IPMSM in different loads condition. Direct Torque Control method (DTC based on Space Vector Modulation (SVM is used for control of IPMSM. In the proposed control method, conventional PI controller is used for controlling the stator flux, and torque of the motor. Also, a fuzzy controller is considered to improve the dynamic performance of DTC technique for speed control. In comparison to the conventional reference flux controller methods, this method, in addition, improves the torque profile of the motor drive. Moreover, it reduces copper losses. Simulation results for a 240V, 120A, 2500rpm, IPMSM confirm the appropriate performances of the method.

  12. Fuzzy Control Strategies in Human Operator and Sport Modeling

    CERN Document Server

    Ivancevic, Tijana T; Markovic, Sasa

    2009-01-01

    The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.

  13. Observer-Based Robust Control of Uncertain Switched Fuzzy Systems with Combined Switching Controller

    Directory of Open Access Journals (Sweden)

    Hong Yang

    2013-01-01

    Full Text Available The observer-based robust control for a class of switched fuzzy (SF time-delay systems involving uncertainties and external disturbances is investigated in this paper. A switched fuzzy system, which differs from existing ones, is firstly employed to describe a nonlinear system. Next, a combined switching controller is proposed. The designed controller based on the observer instead of the state information integrates the advantages of both the switching controllers and the supplementary controllers but eliminates their disadvantages. The proposed controller provides good performance during the transient period, and the chattering effect is removed when the system state approaches the origin. Sufficient condition for the solvability of the robust control problem is given for the case that the state of system is not available. Since convex combination techniques are used to derive the delay-independent criteria, some subsystems are allowed to be unstable. Finally, various comparisons of the elaborated examples are conducted to demonstrate the effectiveness of the proposed control design approach.

  14. Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.

    Science.gov (United States)

    Liu, B D; Chen, C Y; Tsao, J Y

    2001-01-01

    In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.

  15. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    Science.gov (United States)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  16. Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.

    Science.gov (United States)

    Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

    2013-10-01

    In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not

  17. Research of robust adaptive trajectory linearization control based on T-S fuzzy system

    Institute of Scientific and Technical Information of China (English)

    Jiang Changsheng; Zhang Chunyu; Zhu Liang

    2008-01-01

    A robust adaptive trajectory linearization control (RATLC) algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.

  18. Study and simulation of a MPPT controller based on fuzzy logic controller for photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Belaidi, R.; Chikouche, A.; Fathi, M.; Mohand Kaci, G.; Smara, Z. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr

    2011-07-01

    With the depletion of fossil fuels and the increasing concerns about the environment, renewable energies have become more and more attractive. Photovoltaic systems convert solar energy into electric energy through the use of photovoltaic cells. The aim of this paper is to compare the capacity of fuzzy logic and perturb and observe controllers in optimizing the control performance of photovoltaic systems. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of both controllers and compare them. Results showed that the fuzzy controller has a better dynamic performance than the perturb and observe controller in terms of response time and damping characteristics. In addition, the fuzzy controller was found to better follow the maximum power point and to provide faster convergence and lower statistical error. This study demonstrated that the fuzzy controller gives a better performance than traditional controllers in optimizing the performance of photovoltaic systems.

  19. Robust State Feedback H∞Control for Dynamic Biped Robot Based on T-S Fuzzy Model

    Institute of Scientific and Technical Information of China (English)

    HUAI Chuangfeng; FANG Yuefa

    2006-01-01

    T-S fuzzy model was applied to describe nonlinear system and global fuzzy model was expressed by the form of uncertain system. Based on robust state feedback H∞control strategy, designed a global asymptotic steady fuzzy model. This control system can use the experimental input-output data pairs for the biped robot learning and walking with dynamic balance. It is proved by simulation result that robust state feedback H∞ control method based on T-S fuzzy model can effectively restrain the effect of model uncertainties and external disturbance acting on biped robot. From these works, we showed the satisfactory performance of joint tracking without any chattering.

  20. Fuzzy neural network control of underwater vehicles based on desired state programming

    Institute of Scientific and Technical Information of China (English)

    LIANG Xiao; LI Ye; XU Yu-ru; WAN Lei; QIN Zai-bai

    2006-01-01

    Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn't been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle.The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model's uncertainty and external disturbance, which has theoretical and practical value.

  1. Fuzzy Supervisory Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. For high level control and supervi......Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. For high level control...

  2. Pid Plus Fuzzy Logic Controller Based Electronic Load Controller For Self Exited Induction Generator.

    Directory of Open Access Journals (Sweden)

    S.Swathi,

    2014-01-01

    Full Text Available This paper deals with the electronic load controller for self exited induction generator using PID plus fuzzy logic controller. The self-excited induction generators (SEIGs are considered to be well suited for generating electricity by means of conventional energy sources and for supplying electrical energy in remote and rural areas. Induction generators have many advantages such as cost, reduced maintenance, rugged, and simple construction, brushless rotor (squirrel cage. A three phase induction generator can be operated on a delta connection for supplying single phase loads. The main disadvantage of SEIG has is that it poor voltage regulation, and its value depends on the prime mover speed, capacitance, load current and power factor of the load. The electronic load controller (ELC can be used for maintaining constant voltage and frequency of SEIG with variable consumer load driven by constant prime mover. This paper presents the simulation design and implementation of ELC using fuzzy logic method for an SEIG feeding single-phase load. The ELC consist of a rectifier, IGBT as a chopper switch, PI controller, voltage sensor, and resistive dump load in which power consumption was varied through the duty cycle of the chopper. However an ELC consist of electronics system, in general, has complex nonlinear model with parameter variation problem, and the control need to be very fast. The fuzzy logic based controller gives nonlinear control with fast response and virtually no overshoot. The simulation of ELC for self exited induction generator is carried out on MATLAB/SIMULINK. By this proposed ELC using FLC for SEIG we can maintain the constant voltage and frequency of SEIG with variable consumer load.

  3. Static Synchronous Series Compensator Controller based on Fuzzy Logic Control for Power System Stabilization

    Directory of Open Access Journals (Sweden)

    Prechanon Kumkratug

    2011-01-01

    Full Text Available Problem statement: Modern power system consists of the complicated network of transmission lines and carries heavy demand. Thus they cause in the stability problem. Approach: Static Synchronous Series Compensator (SSSC is a power electronic based device that has the capability of controlling the power flow through a line. The series voltage injection model of SSSC is modeled into power flow equation and thus it is used to determine its control strategy. This study applies the fuzzy logic applies the SSSC to improve stability of power system. The mathematical model and control strategy of a SSSC are presented. The SSSC is represented by variable voltage injection with associate transformer leakage control to derive control strategy of SSSC. The swing curves of the three phase faulted power system without and with a SSSC is tested and compared in various cases. Results: The swing curve of the system with SSSC based fuzzy logic control has the less amplitude during the dynamic period. Conclusion: It was found from simulation results that SSSC can improve the power system oscillation after disturbance.

  4. Fuzzy Logic based Coordinated Control of Battery Energy Storage System and Dispatchable Distributed Generation for Microgrid

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Chengshan;

    2015-01-01

    Microgrid is an efficient solution to integraterenewable energy sources (RES) into power systems. Inorder to deal with the intermittent characteristics of therenewable energy based distributed generation (DG) units,a fuzzy-logic based coordinated control strategy of thebattery energy storage system...

  5. Expert system training and control based on the fuzzy relation matrix

    Science.gov (United States)

    Ren, Jie; Sheridan, T. B.

    1991-01-01

    Fuzzy knowledge, that for which the terms of reference are not crisp but overlapped, seems to characterize human expertise. This can be shown from the fact that an experienced human operator can control some complex plants better than a computer can. Proposed here is fuzzy theory to build a fuzzy expert relation matrix (FERM) from given rules or/and examples, either in linguistic terms or in numerical values to mimic human processes of perception and decision making. The knowledge base is codified in terms of many implicit fuzzy rules. Fuzzy knowledge thus codified may also be compared with explicit rules specified by a human expert. It can also provide a basis for modeling the human operator and allow comparison of what a human operator says to what he does in practice. Two experiments were performed. In the first, control of liquid in a tank, demonstrates how the FERM knowledge base is elicited and trained. The other shows how to use a FERM, build up from linguistic rules, and to control an inverted pendulum without a dynamic model.

  6. Heading Control for a Robotic Dolphin Based on a Self-Tuning Fuzzy Strategy

    Directory of Open Access Journals (Sweden)

    Zhiqiang Cao

    2016-02-01

    Full Text Available In this paper, a heading controller based on a self-tuning fuzzy strategy for a robotic dolphin is proposed to improve control accuracy and stability. The structure of the robotic dolphin is introduced and the turning motion is analysed. The analytic model indicates that the turning joint angle can be employed for the heading control. This non-linear model prevents the successful application of traditional model-based controllers. A fuzzy controller is proposed to realize the heading control in our work. It should be mentioned that the traditional fuzzy controller suffers from a distinguished steady-state error, due to the fact that the heading range is relatively large and the fuzzy controller's universe of discourse is fixed. To resolve this problem, a self-tuning mechanism is employed to adjust the input and output scaling factors according to the active working region in pursuit of favourable performance. Experimental results demonstrate the performance of the proposed controller in terms of steady-state error and robustness to interferences.

  7. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment.

    Science.gov (United States)

    Chen, W C; Chang, Ni-Bin; Chen, Jeng-Chung

    2003-01-01

    Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.

  8. Artificial Error Tuning Based on Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

    Directory of Open Access Journals (Sweden)

    Samaneh Zahmatkesh

    2013-10-01

    Full Text Available This paper examines single input single output (SISO chattering free variable structure control (VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load.

  9. Command Filtering-Based Fuzzy Control for Nonlinear Systems With Saturation Input.

    Science.gov (United States)

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Lin, Chong

    2016-12-13

    In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.

  10. Study of Multimedia Streams Dynamic Rate Control Based on Fuzzy Adaptive PID

    Institute of Scientific and Technical Information of China (English)

    SUN Yan-fei; ZHANG Shun-yi; SHI Jin; WANG Jiang-tao

    2005-01-01

    A Multimedia streams dynamic rate control algorithm based on Fuzzy adaptive PID (MFPID) has been proposed to implement multimedia streams' end sending rate on-line self-regulating and smoothing, and to track system resources in time, so that it can avoid system's regulating oscillation and guarantee system's stability. And, some work has been done to analyze adaptive session model of multimedia streams, to implement future available bandwidth estimation of IP network, to achieve PID parameters' on-line self-tuning by fuzzy controlling. Simulation validated the theoretical results of MFPID.

  11. Adaptive fuzzy PID temperature control system based on single-chip computer for the autoclave

    Science.gov (United States)

    Zhang, F.; Wang, J.; Fu, S. L.; He, Z. T.; Li, X. P.

    2008-12-01

    The autoclave is one of main preparation equipments of crystal preparation by hydrothermal method. The preparation temperature will seriously influence crystals quality and crystals size at high temperature, how to measure and control precisely the autoclave temperature can be of real significance. The characteristic of hysteresis, nonlinearity and difficulty to acquire the precise mathematical model existing in the temperature control of the autoclave was researched. The general PID controller adopted usually in the autoclave temperature control system is hard to improve temperature control performance. Based on the advantages of fuzzy controller that does not depend on the precise mathematical model and the stabilization of PID controller, single-chip computer integrated fuzzy PID control algorithm is adopted, and the temperature system is designed, the foundational working principle was discussed. The control system includes SCM (AT89C52), temperature sensor, A/D converter circuit and corresponding circuit and interface, can make the autoclave temperature measure and control accurately. The system hardware includes main circuit, thyristor drive circuit, audible and visual alarm circuit, watchdog circuit, clock circuit, keyboard and display circuit so on, which can achieve gathering, analyzing, comparing and controlling the autoclave temperature parameter. The program of control system includes the treatment and collection of temperature data, the dynamic display program, the fuzzy PID control system, the audible and visual alarm program, et al, and the system's main software, which includes initialization, key-press processing, input processing, display, and the fuzzy PID control program was analyzed. The results showed that the fuzzy PID control system makes the adjustment time of temperature decreased and the precision of temperature control improved, the quality and the crystals size of the preparation crystals can achieve the expect experiment results.

  12. Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks

    Institute of Scientific and Technical Information of China (English)

    费翔; 何小燕; 罗军舟; 吴介一; 顾冠群

    2000-01-01

    Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.

  13. PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

    Institute of Scientific and Technical Information of China (English)

    Yong YANG; An LUO; Karl-Erik RYDBERG

    2006-01-01

    A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system (HCBS). Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator(LSI), are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

  14. Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    Saifullah Khalid

    2016-09-01

    Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.

  15. Control Design Of Robot Manipulator Position Based On Pd-Fuzzy Mamdani Controlled With Computed Torque Control (Pd-Fuzzy-Ctc

    Directory of Open Access Journals (Sweden)

    Duli Ridlo Istriantono

    2015-03-01

    Full Text Available Robotics science has evolved significantly, driven by rapid advances in computer and sensor technology; and theoretical advances in control and computer vision. These development make widespread use of robot manipulators in industrial environments. Major problem in controlling a robot manipulator is to control the robot in order to achieve the desired position. Therefore the design issue of the robot control is to choose the right type controller. Computed Torque Controller (CTC is a powerful nonlinear controllers are widely used in the control of robot manipulators. CTC controller is designed based on feedback linearization and the required torque of the robot arm by using a nonlinear feedback control law. Simulation is done by providing joint trajectory from point to point. The simulation results show that the PD-Fuzzy-CTC controller is able to follow the joint trajectory with The RMSE value of the joint angle position of PD-Fuzzy-CTC controller is 10 times smaller than that of the PD-CTC controller with the end-effector position accuracy is 0.1 mm.  

  16. A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter

    Science.gov (United States)

    Krasowski, M. J.; Dickens, D. E.

    1992-01-01

    A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.

  17. Fuzzy Logic Based Speed Control System for ThreePhase Induction Motor

    Directory of Open Access Journals (Sweden)

    Marwan A. Badran

    2013-05-01

    Full Text Available Three-phase induction motors have been used in a wide range of industry applications. Using modern technology, the speed of induction motor can be easily controlled by variable frequency drives (VFDs. These drives use high speed power transistors with various switching techniques, mainly PWM schemes. For several decades, conventional control systems were applied to electric drives to control the speed of induction motor. Although conventional controllers showed good results, but they still need tuning to obtain optimum results. The recent proposed control systems use fuzzy logic controller (FLC to enhance the performance of induction motor drives. In this paper, a fuzzy logic based speed control system is presented. The proposed controller has been designed with MATLAB/SIMULINK software, and it was tested for various operating conditions including load disturbance and sudden change of reference speed. The results showed better performance of the proposed controller compared with the conventional PI controller.

  18. Stabilizing Planar Inverted Pendulum System Based on Fuzzy Nine-point Controller

    Directory of Open Access Journals (Sweden)

    Qi Qian

    2013-07-01

    Full Text Available In order to stabilize planar inverted pendulum, after analyzing the physical characteristics of the planar inverted pendulum system, a pendulum nine-point controller and a car nine-point controller for X-axis and Y-axis were designed respectively. Then a fuzzy coordinator was designed using the fuzzy control theory based on the priority of those two controllers, and the priority level of the pendulum is higher than the car. Thus, the control tasks of each controller in each axis were harmonized successfully. The designed control strategy did not depend on mathematical model of the system, it depended on the control experience of people or the control experts. The compared experiments showed that the control strategy was easy and effective, what was’s more, it had a very good robust feature.  

  19. Design and Application of Variable Universe Fuzzy Controller Based on Cat Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Haipeng Pan

    2016-01-01

    Full Text Available A novel variable universe fuzzy controller based on cat swarm optimization (CSO-VUFC is proposed to regulate the temperature of the reactor system, which is characterized by nonlinearity, large time delay, and uncertainty. In CSO-VUFC, firstly, corresponding contraction-expansion factors with the function form were, respectively, introduced for the input and output fuzzy universes of the controller. Then, cat swarm optimization was used to optimize the relevant parameter values in the contraction-expansion factor function to achieve the intelligence optimization of the contraction-expansion factors, based on the system performance test function as an evaluation index; the contradiction between the universe adjustment and control accuracy of the fuzzy controller will be effectively solved to achieve the online self-adjustment of the universe. The simulation results indicate that the variable universe adaptive fuzzy control method based on the cat swarm optimization has the features of high precision adjustment, short transient time, and hard real-time.

  20. A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments

    Directory of Open Access Journals (Sweden)

    Olusegun Folorunso

    2015-07-01

    Full Text Available Crowdsourcing has been widely accepted across a broad range of application areas. In crowdsourcing environments, the possibility of performing human computation is characterized with risks due to the openness of their web-based platforms where each crowd worker joins and participates in the process at any time, causing serious effect on the quality of its computation. In this paper, a combination of Trust-Based Access Control (TBAC strategy and fuzzy-expert systems was used to enhance the quality of human computation in crowdsourcing environment. A TBAC-fuzzy algorithm was developed and implemented using MATLAB 7.6.0 to compute trust value (Tvalue, priority value as evaluated by fuzzy inference system (FIS and finally generate access decision to each crowd-worker. In conclusion, the use of TBAC is feasible in improving quality of human computation in crowdsourcing environments.

  1. The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation.

    Science.gov (United States)

    Guler, Hasan; Ata, Fikret

    2014-09-01

    The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat's status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student's t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat's lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully.

  2. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    OpenAIRE

    Abdul Kareem; Mohammad Fazle Azeem

    2012-01-01

    This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...

  3. Fuzzy Shape Control Based on Elman Dynamic Recursion Network Prediction Model

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; LIU Hong-min

    2006-01-01

    In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self-adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

  4. SLIP RATE ESTIMATION FOR VEHICLE STABILITY ENHANCEMENT USING FUZZY BASED ELECTRONIC DIFFERENTIAL CONTROLLER

    Directory of Open Access Journals (Sweden)

    A. RAVI

    2015-11-01

    Full Text Available Fuzzy Logic based Electronic Differential Controller (FLEDC for sensorless drive based electric vehicle is presented. The proposed system consists of two Brushless DC motors (BLDC that ensure the drive of the two back driving wheels of an electric vehicle. Electronic Differential Controller (EDC can control both the driving wheel independently to turn at different speeds in any curve according to the steering angle. The sensorless control strategies include back EMF zero crossing detection and third harmonic voltage integration are used to analyse the proposed system. Fuzzy logic based EDC is used on these sensorless control strategies which optimizes the slip rate within the specified limit. To enhances the vehicle stability, the performances in terms of optimum value of slip rate and also current, torque, back EMF are obtained by the proposed method. By this investigation, a suitable control strategy has been identified and also experimentally validated.

  5. An Intelligent Irrigation System Based on Wireless Sensor Network and Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Liai Gao

    2013-05-01

    Full Text Available In order to resolve the problems which include loss of soil fertility and waste of water resource in agriculture production, we design an intelligent irrigation system based on wireless sensor networks and fuzzy control. The system mainly consists of wireless sensor networks and the monitoring center. All of the nodes in Monitoring area use solar power, collect the information of soil moisture, together with the growth information of different crops in different periods. Soil moisture content deviation and the rate of change of deviation are taken as input variables of fuzzy controller, and the fuzzy control regular database is established for the fuzzy irrigation control system. The monitoring center receives the data transmission from wireless sensor network node, and output information of irrigation water demands to the relay via a wireless sensor network to control opening and closing time of the valve in crop areas. The experimental results show that the system has a stable and reliable data transmission, which achieve real-time monitoring of soil on crop growth, give a right amount of irrigation based on crops growth information, which has broad application prospects.

  6. Induction machine Direct Torque Control system based on fuzzy adaptive control

    Science.gov (United States)

    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.

  7. PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization

    Energy Technology Data Exchange (ETDEWEB)

    Priyambodo, Tri Kuntoro, E-mail: mastri@ugm.ac.id; Putra, Agfianto Eko [Aerospace and Aeronautics Electronics Research Group, Universitas Gadjah Mada, Yogyakarta (Indonesia); Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta (Indonesia); Dharmawan, Andi, E-mail: andi-dharmawan@ugm.ac.id [Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta (Indonesia)

    2016-02-01

    Quadrotor as one type of UAV have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various methods. The simplest method is tuning manually. This method has several weaknesses. For example if the three constants are not exact, the resulting response will deviate from the desired result. By combining the methods of PID with fuzzy logic systems where human expertise is implemented into the machine language is expected to further optimize the control system.

  8. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Science.gov (United States)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2011-01-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  9. Analysis and Synthesis of Memory-Based Fuzzy Sliding Mode Controllers.

    Science.gov (United States)

    Zhang, Jinhui; Lin, Yujuan; Feng, Gang

    2015-12-01

    This paper addresses the sliding mode control problem for a class of Takagi-Sugeno fuzzy systems with matched uncertainties. Different from the conventional memoryless sliding surface, a memory-based sliding surface is proposed which consists of not only the current state but also the delayed state. Both robust and adaptive fuzzy sliding mode controllers are designed based on the proposed memory-based sliding surface. It is shown that the sliding surface can be reached and the closed-loop control system is asymptotically stable. Furthermore, to reduce the chattering, some continuous sliding mode controllers are also presented. Finally, the ball and beam system is used to illustrate the advantages and effectiveness of the proposed approaches. It can be seen that, with the proposed control approaches, not only can the stability be guaranteed, but also its transient performance can be improved significantly.

  10. Dynamic response improvement of doubly fed induction generator-based wind farm using fuzzy logic controller

    Science.gov (United States)

    Hasanien, Hany M.; Al-Ammar, Essam A.

    2012-11-01

    Doubly fed induction generator (DFIG) based wind farm is today the most widely used concept. This paper presents dynamic response enhancement of DFIG based wind farm under remote fault conditions using the fuzzy logic controller. The goal of the work is to improve the dynamic response of DFIG based wind farm during and after the clearance of fault using the proposed controller. The stability of wind farm during and after the clearance of fault is investigated. The effectiveness of the fuzzy logic controller is then compared with that of a PI controller. The validity of the controllers in restoring the wind farms normal operation after the clearance of fault is illustrated by the simulation results which are carried out using MATLAB/SIMULINK. Simulation results are analyzed under different fault conditions.

  11. Terminal sliding mode fuzzy control based on multiple sliding surfaces for nonlinear ship autopilot systems

    Science.gov (United States)

    Yuan, Lei; Wu, Han-Song

    2010-12-01

    A terminal sliding mode fuzzy control based on multiple sliding surfaces was proposed for ship course tracking steering, which takes account of rudder characteristics and parameter uncertainty. In order to solve the problem, the controller was designed by employing the universal approximation property of fuzzy logic system, the advantage of Nussbaum function, and using multiple sliding mode control algorithm based on the recursive technique. In the last step of designing, a nonsingular terminal sliding mode was utilized to drive the last state of the system to converge in a finite period of time, and high-order sliding mode control law was designed to eliminate the chattering and make the system robust. The simulation results showed that the controller designed here could track a desired course fast and accurately. It also exhibited strong robustness peculiarly to system, and had better adaptive ability than traditional PID control algorithms.

  12. An experiment-based comparative study of fuzzy logic control

    Science.gov (United States)

    Berenji, Hamid R.; Chen, Yung-Yaw; Lee, Chuen-Chein; Murugesan, S.; Jang, Jyh-Shing

    1989-01-01

    An approach is presented to the control of a dynamic physical system through the use of approximate reasoning. The approach has been implemented in a program named POLE, and the authors have successfully built a prototype hardware system to solve the cartpole balancing problem in real-time. The approach provides a complementary alternative to the conventional analytical control methodology and is of substantial use when a precise mathematical model of the process being controlled is not available. A set of criteria for comparing controllers based on approximate reasoning and those based on conventional control schemes is furnished.

  13. 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.

  14. Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

    Institute of Scientific and Technical Information of China (English)

    LIU Hongyi; WANG Lei; WANG Fei

    2007-01-01

    To precisely implement the force control of robot manipulators in an unknown environment,a control strategy based on fuzzy prediction of the reference trajectory in the impedance model is developed.The force tracking experiments are executed in an open-architecture control system with different tracking velocities,different desired forces,different contact stiffnesses and different surface figurations.The corresponding force control results are compared and analyzed.The influences of unknown parameters of the environment on the contact force are analyzed based on experimental data,and the tunings of predictive scale factors are illustrated.The experimental results show that the desired trajectory in the impedance model is predicted exactly and rapidly in the cases that the contact surface is unknown,the contact stiffness changes,and the fuzzy force control algorithm has high adaptability to the unknown environment.

  15. Designing Flexible Neuro-Fuzzy System Based on Sliding Mode Controller for Magnetic Levitation Systems

    Directory of Open Access Journals (Sweden)

    Zahra Mohammadi

    2011-07-01

    Full Text Available This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.

  16. Tuning of Fuzzy PID Controllers

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains compared to proportional-integral-derivative (PID) controllers. This research paper proposes a design procedure and a tuning procedure that carries tuning rules from the PID domain over to fuzzy single......-loop controllers. The idea is to start with a tuned, conventional PID controller, replace it with an equivalent linear fuzzy controller, make the fuzzy controller nonlinear, and eventually fine-tune the nonlinear fuzzy controller. This is relevant whenever a PID controller is possible or already implemented....

  17. Correction factor based double model fuzzy logic control strategy of arc voltage in pulsed MIG welding

    Institute of Scientific and Technical Information of China (English)

    Wu Kaiyuan; Huang Shisheng; Meng Yongmin

    2005-01-01

    According to the feature of arc voltage control in welding steel using pulsed MIG welding, a correction factor based double model fuzzy logic controller (FLC) was developed to realize the arc voltage control by means of arc voltage feedback.When the error of peak arc voltage was great, a coarse adjusting fuzzy logic control rules with correction factor was designed,in the controller, the peak arc voltage was controlled by the wire feeding speed by means of arc voltage feedback. When the error of peak arc voltage was small, a fine adjusting fuzzy logic control rules with correction factor was designed, in this controller, the peak arc voltage was controlled by the background time by means of arc voltage feedback. The FLC was realized in a Look-Up Table ( LUT) method. Experiments had been carried out aiming at implementing the control strategy to control the arc length change in welding process. Experimental results show that the controller proposed enables the consistency of arc length and the stabolity of arc voltage and welding process to be achieved in pulsed MIG welding process.

  18. Implementation of fuzzy-sliding mode based control of a grid connected photovoltaic system.

    Science.gov (United States)

    Menadi, Abdelkrim; Abdeddaim, Sabrina; Ghamri, Ahmed; Betka, Achour

    2015-09-01

    The present work describes an optimal operation of a small scale photovoltaic system connected to a micro-grid, based on both sliding mode and fuzzy logic control. Real time implementation is done through a dSPACE 1104 single board, controlling a boost chopper on the PV array side and a voltage source inverter (VSI) on the grid side. The sliding mode controller tracks permanently the maximum power of the PV array regardless of atmospheric condition variations, while The fuzzy logic controller (FLC) regulates the DC-link voltage, and ensures via current control of the VSI a quasi-total transit of the extracted PV power to the grid under a unity power factor operation. Simulation results, carried out via Matlab-Simulink package were approved through experiment, showing the effectiveness of the proposed control techniques.

  19. Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

    Science.gov (United States)

    Huang, Shiuh-Jer; Chen, Hung-Yi

    In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

  20. A NEW FUZZY LOGIC BASED SPACE VECTOR MODULATION APPROACH ON DIRECT TORQUE CONTROLLED INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    Fatih Korkmaz

    2013-11-01

    Full Text Available The induction motors are indispensable motor types for industrial applications due to its wellknown advantages. Therefore, many kind of control scheme are proposed for induction motors over the past years and direct torque control has gained great importance inside of them due to fast dynamic torque response behavior and simple control structure. This paper suggests a new approach on the direct torque controlled induction motors, Fuzzy logic based space vector modulation, to overcome disadvantages of conventional direct torque control like high torque ripple. In the proposed approach, optimum switching states are calculated by fuzzy logic controller and applied by space vector pulse width modulator to voltage source inverter. In order to test and compare the proposed DTC scheme with conventional DTC scheme simulations, in Matlab/Simulink, have been carried out in different speed and load conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional DTC scheme.

  1. Temperature control system of resistance-heated furnace based on variable fuzzy-PI control

    Science.gov (United States)

    Shi, Dequan; Gao, Guili; Gao, Zhiwei; Xiao, Peng

    2013-03-01

    In order to solve the problem that resistance-heated furnace has the disadvantage of non-linearity, slow time-variant and large delay and so on, a temperature control system of the resistance-heated furnace has been designed according to the fuzzy-PI algorithm. Because this method has the merits of both PI and fuzzy control, the temperature control effect is improved to large extent. When the temperature error between given value and measured value is too large, the fuzzy control is adopted. When the error is too small, the PI control is used. The simulation test is performed by MATLAB, and the results indicate that this system has the advantages of small overshoot, short adjusting time and good robustness.

  2. Reduced rule base self-tuning fuzzy PI controller for TCSC

    Energy Technology Data Exchange (ETDEWEB)

    Hameed, Salman; Das, Biswarup; Pant, Vinay [Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee - 247 667, Uttarakhand (India)

    2010-11-15

    In this paper, a reduced rule base self-tuning fuzzy PI controller (STFPIC) for thyristor controlled series capacitor (TCSC) is proposed. Essentially, a STFPIC consists of two fuzzy logic controllers (FLC). In this work, for each FLC, 49 rules have been used and as a result, the overall complexity of the STFPIC increases substantially. To reduce this complexity, application of singular value decomposition (SVD) based rule reduction technique is also proposed in this paper. By applying this methodology, the number of rules in each FLC has been reduced from 49 to 9. Therefore, the proposed rule base reduction technique reduces the total number of rules in the STFPIC by almost 80% (from 49 x 2 = 98 to 9 x 2 = 18), thereby reducing the complexity of the STFPIC significantly. The feasibility of the proposed algorithm has been tested on 2-area 4-machine power system and 10-machine 39-bus system through detailed digital simulation using MATLAB/SIMULINK. (author)

  3. Fuzzy Integral-Based Gaze Control of a Robotic Head for Human Robot Interaction.

    Science.gov (United States)

    Yoo, Bum-Soo; Kim, Jong-Hwan

    2015-09-01

    During the last few decades, as a part of effort to enhance natural human robot interaction (HRI), considerable research has been carried out to develop human-like gaze control. However, most studies did not consider hardware implementation, real-time processing, and the real environment, factors that should be taken into account to achieve natural HRI. This paper proposes a fuzzy integral-based gaze control algorithm, operating in real-time and the real environment, for a robotic head. We formulate the gaze control as a multicriteria decision making problem and devise seven human gaze-inspired criteria. Partial evaluations of all candidate gaze directions are carried out with respect to the seven criteria defined from perceived visual, auditory, and internal inputs, and fuzzy measures are assigned to a power set of the criteria to reflect the user defined preference. A fuzzy integral of the partial evaluations with respect to the fuzzy measures is employed to make global evaluations of all candidate gaze directions. The global evaluation values are adjusted by applying inhibition of return and are compared with the global evaluation values of the previous gaze directions to decide the final gaze direction. The effectiveness of the proposed algorithm is demonstrated with a robotic head, developed in the Robot Intelligence Technology Laboratory at Korea Advanced Institute of Science and Technology, through three interaction scenarios and three comparison scenarios with another algorithm.

  4. An adaptive fuzzy-sliding lateral control strategy of automated vehicles based on vision navigation

    Science.gov (United States)

    Guo, Jinghua; Li, Linhui; Li, Keqiang; Wang, Rongben

    2013-10-01

    Lateral control is considered to be one of the toughest challenges in the development of automated vehicles due to their features of nonlinearities, parametric uncertainties and external disturbances. In order to overcome these difficulties, an adaptive fuzzy-sliding mode control strategy used for lateral control of vision-based automated vehicles is proposed in this paper. First, a vision algorithm is designed to provide accurate location information of vehicle relative to reference path. Then, an adaptive fuzzy-sliding mode lateral controller is proposed to counteract parametric uncertainties and strong nonlinearities, and the asymptotic stability of the closed-loop lateral control system is proven by the Lyapunov theory. Finally, experimental results indicate that the proposed algorithm can achieve favourable tracking performance, and it has strong robustness.

  5. A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

    DEFF Research Database (Denmark)

    Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist

    Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation...... in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete...

  6. Power System Stabilizer Design Based on Model Reference Robust Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Yazdchi

    2012-04-01

    Full Text Available Power System Stabilizers (PSS are used to generate supplementary damping control signals for the excitation system in order to damp the Low Frequency Oscillations (LFO of the electric power system. The PSS is usually designed based on classical control approaches but this Conventional PSS (CPSS has some problems in power system control and stability enhancement. To overcome the drawbacks of CPSS, numerous techniques have been proposed in literatures. In this study a new method based on Model Reference Robust Fuzzy Control (MRRFC is considered to design PSS. In this new approach, in first an optimal PSS is designed in the nominal operating condition and then power system identification is used to obtain model reference of power system including optimal PSS. With changing system operating condition from the nominal condition, the error between obtained model reference and power system response in sent to a fuzzy controller and this fuzzy controller provides the stabilizing signal for damping power system oscillations just like PSS. In order to model reference identification a PID type PSS (PID-PSS is considered for damping electric power system oscillations. The parameters of this PID-PSS are tuned based on hybrid Genetic Algorithms (GA optimization method. The proposed MRRFC is evaluated against the CPSS at a single machine infinite bus power system considering system parametric uncertainties. The simulation results clearly indicate the effectiveness and validity of the proposed method.

  7. Visual-based quadrotor control by means of fuzzy cognitive maps.

    Science.gov (United States)

    Amirkhani, Abdollah; Shirzadeh, Masoud; Papageorgiou, Elpiniki I; Mosavi, Mohammad R

    2016-01-01

    By applying an image-based visual servoing (IBVS) method, the intelligent image-based controlling of a quadrotor type unmanned aerial vehicle (UAV) tracking a moving target is studied in this paper. A fuzzy cognitive map (FCM) is a soft computing method which is classified as a fuzzy neural system and exploits the main aspects of fuzzy logic and neural network systems; so it seems to be a suitable choice for implementing a vision-based intelligent technique. An FCM has been employed in implementing an IBVS scheme on a quadrotor UAV, so that the UAV can track a moving target on the ground. For this purpose, by properly combining the perspective image moments, some features with the desired characteristics for controlling the translational and yaw motions of a UAV have been presented. In designing a vision-based control method for a UAV quadrotor, there are some challenges, including the target mobility and not knowing the height of UAV above the target. Also, no sensor has been installed on the moving object and the changes of its yaw angle are not available. Despite all the stated challenges, the proposed method, which uses an FCM in controlling the translational motion and the yaw rotation of a UAV, adequately enables the quadrotor to follow the moving target. The simulation results for different paths show the satisfactory performance of the designed controller. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Embedded system based on a real time fuzzy motor speed controller

    Directory of Open Access Journals (Sweden)

    Ebrahim Abd El-Hamid Mohamed Ramadan

    2014-06-01

    Full Text Available This paper describes an implementation of a fuzzy logic control (FLC system and a/the conventional proportional-integral (PI controller for speed control of DC motor, based on field programmable gate array (FPGA circuit. The proposed scheme is aimed to improve the tracking performance and to eliminate the load disturbance in the speed control of DC motors. The proposed fuzzy system has been applied to a permanent magnet DC motor, via a configuration of H-bridge. The fuzzy control algorithm is designed and verified with a nonlinear model, using the MATLAB® tools. Both FLC and conventional PI controller hardware are synthesized, functionally verified and implemented using Xilinx Integrated Software Environment (ISE Version 11.1i. The real time implementation of these controllers is made on Spartan-3E FPGA starter kit (XC3S500E. The practical results showed that the proposed FLC scheme has better tracking performance than the conventional PI controller for the speed control of DC motors.

  9. Learning fuzzy logic control system

    Science.gov (United States)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  10. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  11. Neuro-Fuzzy based Controller for a Three- Phase Four-Wire Shunt Active Power Filter

    Directory of Open Access Journals (Sweden)

    Mridul Jha

    2011-10-01

    Full Text Available This paper describes the application of a novel neuro-fuzzy based control strategy which is used in order to improve the Active Power Filter (APF dynamics to minimize the harmonics for wide range of variations of load current under various conditions. To improve dynamic behavior of a three phase four-wire shunt active power filter and its robustness under range of load variations, adaptive hysteresis band with instantaneous p-q theory is used with the inclusion of neural network filter for reference current generation and fuzzy logic controller for DC voltage control. The proposed control scheme for “split-capacitor” converter topology is simple and also capable of maintaining the compensated line currents balanced, irrespective of unbalancing in the source voltages & deviation in the capacitor voltages. The results presented in MATLAB-SIMULINK software in this paper clearly reflect the effectiveness of the proposed APF to meet the IEEE-519 standard recommendations on harmonic levels.

  12. HYDRAULIC ACTIVE GUIDE ROLLER SYSTEM FOR HIGH-SPEED ELEVATOR BASED ON FUZZY CONTROLLER

    Institute of Scientific and Technical Information of China (English)

    FENG Yonghui; ZHANG Jianwu

    2007-01-01

    Increase of elevator speed brings about amplified vibrations of high-speed elevator. In order to reduce the horizontal vibrations of high-speed elevator, a new type of hydraulic active guide roller system based on fuzzy logic controller is developed. First the working principle of the hydraulic guide system is introduced, then the dynamic model of the horizontal vibrations for elevator cage with active guide roller system and the mathematical model of the hydraulic system are given. A fuzzy logic controller for the hydraulic system is designed to control the hydraulic actuator. To improve the control performance, preview compensation for the controller is provided. Finally, simulation and experiments are executed to verify the hydraulic active guide roller system and the control strategy. Both the simulation and experimental results indicate that the hydraulic active guide roller system can reduce the horizontal vibrations of the elevator effectively and has better effects than the passive one, and the fuzzy logic controller with preview compensation can give superior control performance.

  13. Comparative Study of Fuzzy Logic Based Speed Control of Multilevel Inverter fed Brushless DC Motor Drive

    Directory of Open Access Journals (Sweden)

    Pritha Agrawal

    2014-02-01

    Full Text Available This paper presents a comparative analysis of speed control of brushless DC motor (BLDC drive fed with conventional two-level, three and five level diode clamped multilevel inverter (DC-MLI. The performance of the drive system is successfully evaluated using Fuzzy Logic (FL based speed controller. The control structure of the proposed drive system is described. The speed and torque characteristic of conventional two-level inverter is compared with the three and five-level multilevel inverter (MLI for various operating conditions. The three and five level diode clamped multilevel inverters are simulated using IGBT’s and the mathematical model of BLDC motor has been developed in MATLAB/SIMULINK environment. The simulation results show that the Fuzzy based speed controller eliminate torque ripples and provides fast speed response. The developed Fuzzy Logic model has the ability to learn instantaneously and adapt its own controller parameters based on disturbances with minimum steady state error, overshoot and rise time of the output voltage.

  14. 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.

  15. SERVICE-AWARE BASED FUZZY ADMISSION CONTROL SCHEME IN MULTI-SERVICE NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Qiu Gongan; Zhang Shunyi; Liu Shidong

    2007-01-01

    Multi-service aggregated transmission is the direction of IP network. Providing different Quality of Service (QoS) assurance for different services has become a crucial problem in future network.Admission control is a vital function for multi-service IP network. This paper proposes a novel fuzzy admission control scheme based on coarse granularity service-aware technique. Different service has discriminative sensitivity to the same QoS characteristic parameter in general. The traffic class can be perceived by the service request parameter and the proposed QoS function. And requirements of different applications can be met by maintaining the life parameter. From simulation results, the proposed scheme shows a better QoS provisioning than those traditional fuzzy logic based methods under the same admission probability.

  16. Robust chaos synchronization based on adaptive fuzzy delayed feedback $\\mathcal{H}_{∞}$ control

    Indian Academy of Sciences (India)

    Choon Ki Ahn

    2012-03-01

    In this paper, we propose a new adaptive $\\mathcal_{∞}$ synchronization strategy, called an adaptive fuzzy delayed feedback $\\mathcal_{∞}$ synchronization (AFDFHS) strategy, for chaotic systems with uncertain parameters and external disturbances. Based on Lyapunov–Krasovskii theory, Takagi–Sugeno (T–S) fuzzy model and adaptive delayed feedback $\\mathcal_{∞}$ control scheme, the AFDFHS controller is presented such that the synchronization error system is asymptotically stable with a guaranteed $\\mathcal_{∞}$ performance. It is shown that the design of the AFDFHS controller with adaptive law can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed AFDFHS approach.

  17. Decentralized model reference adaptive sliding mode control based on fuzzy model

    Institute of Scientific and Technical Information of China (English)

    Gu Haijun; Zhang Tianping; Shen Qikun

    2006-01-01

    A new design scheme of decentralized model reference adaptive sliding mode controller for a class of MIMO nonlinear systems with the high-order interconnections is proposed. The design is based on the universal approximation capability of the Takagi - Seguno (T-S) fuzzy systems. Motivated by the principle of certainty equivalentcontrol, a decentralized adaptive controller is designed to achieve the tracking objective without computation of the T-S fuzz ymodel. The approach does not require the upper bound of the uncertainty term to be known through some adaptive estimation. By theoretical analysis, the closed-loop fuzzy control system is proven to be globally stable in the sense that all signalsinvolved are bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.

  18. Electric Drive Control with Rotor Resistance and Rotor Speed Observers Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    C. Ben Regaya

    2014-01-01

    Full Text Available Many scientific researchers have proposed the control of the induction motor without speed sensor. These methods have the disadvantage that the variation of the rotor resistance causes an error of estimating the motor speed. Thus, simultaneous estimation of the rotor resistance and the motor speed is required. In this paper, a scheme for estimating simultaneously the rotor resistance and the rotor speed of an induction motor using fuzzy logic has been developed. We present a method which is based on two adaptive observers using fuzzy logic without affecting each other and a simple algorithm in order to facilitate the determination of the optimal values of the controller gains. The control algorithm is proved by the simulation tests. The results analysis shows the characteristic robustness of the two observers of the proposed method even in the case of variation of the rotor resistance.

  19. Control of Hydrogen Generation from Water Molecules Dissociated by Activated Aluminum Particles Based on Fuzzy Logic

    Science.gov (United States)

    Maekawa, Koji; Takahara, Kenji; Kajiwara, Toshinori; Watanabe, Masao

    This paper proposes a control system to keep hydrogen generation by a reaction between water and activated aluminum particles at desired level. Because the activated aluminum particles are produced shredded aluminum sawdust, the characteristics of hydrogen generation vary depending on its samples. Therefore, the fuzzy control system to determine the quantum of the activated aluminum particles is designed based on the measured characteristics of hydrogen generation. Error form a desired value, error rate and dead time of the reaction are chosen as the labels of the proposed fuzzy membership functions. The reactor vessel that the activated aluminum particles are put into is developed to generate hydrogen continuously. Three types of aluminum particles of the characteristic are used for the experiments. The proposed system is confirmed to be useful for the control of hydrogen generation, coping with the effect of reacting characteristic changes according to the activated aluminum samples.

  20. Metal-Organic Framework Synthesis System Based on Fuzzy Predictive Control via Network Transmission

    Directory of Open Access Journals (Sweden)

    Jui-Ho Chen

    2014-01-01

    Full Text Available The purpose of this study is to construct metal-organic framework (MOF synthesis heating systems based on fuzzy method for monitoring and automatic control. In this study, the temperature sensing module for measurements sensed values that it through a wireless ZigBee chips and wired DAQ device for real-time data transmission. Because MOF synthesis, often due to different modes of heating or heating instability caused by its nucleation and crystal growth rate, is an important influence, leading to different crystallinity, the use of fuzzy theory to predict the temperature parameter and instant heating MOF synthesis parameters can be adjusted to improve the accuracy of the system. The research system to RS-232 interface module for infrared emission control packets issued and automated control of the furnace through the infrared receiver module. This study is based on a terminal interface window of Visual Basic programming and LabView graphical diagram for control system design. Finally, this research, through a number of experiments to validate the use of fuzzy system development methods and networks, can improve the accuracy of the reaction efficiency MOF sensing and control the heating system.

  1. Research on the Stabilization Control for Inverted Pendulum using New Nonlinear Method based on Fuzzy%Research on the Stabilization Control for Inverted Pendulum using New Nonlinear Method based on Fuzzy

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    In this paper mathematical model of single inverted pendulum established based on Lagrange method. Stability of the inverted pendulum system is analyzed. Single inverted pendulum can be controlled by modem control theory, pole assignment method and Linear-quadratic regulator theory, effectually but only in small angle range. In order to obtain the larger controllable angle, the fuzzy method has been accepted to treat with this system. The idea behind of fuzzy control method in this paper is to divide the operating region of nonlinear system into small area, and treated as a collection of local linear systems which can be controlled. Global bounded property of the fuzzy method has been proven success, and according the simulation results of fuzzy servo system controllable angle is extended.

  2. An Intelligent Irrigation System Based on Wireless Sensor Network and Fuzzy Control

    OpenAIRE

    Liai Gao; Meng Zhang; Geng Chen

    2013-01-01

    In order to resolve the problems which include loss of soil fertility and waste of water resource in agriculture production, we design an intelligent irrigation system based on wireless sensor networks and fuzzy control. The system mainly consists of wireless sensor networks and the monitoring center. All of the nodes in Monitoring area use solar power, collect the information of soil moisture, together with the growth information of different crops in different periods. Soil moisture content...

  3. Direct Vector Control of Induction Motor Based on Sinusoidal PWM Inverter with Fuzzy Logic Controller

    Directory of Open Access Journals (Sweden)

    Nirban Chakraborty

    2014-04-01

    Full Text Available This paper presents the speed control scheme of direct vector control of Induction Motor drive (IM drive. The Fuzzy logic controller is (FLC used as the controller part here for the direct vector control of Induction Motor using Sinusoidal PWM Inverter (SPWM. Fuzzy logic controller has become a very popular controlling scheme in the field of Industrial application. The entire module of this IM is divided into several parts such as IM body module, Inverter module, coordinate transformation module and Sinusoidal pulse width modulation (SPWM production module and so on. With the help of this module we can analyze a variety of different simulation waveforms, which provide an effective means for the analysis and design of the IM control system using FLC technique.

  4. A computationally efficient fuzzy control s

    Directory of Open Access Journals (Sweden)

    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.

  5. Guaranteed Cost Control for Uncertain Nonlinear Time-Delay Neutral Systems Based on T-S Fuzzy Model

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The problem of guaranteed cost fuzzy controller is studied for a class of nonlinear time-delay neutral systems with norm-bounded uncertainty based on T-S model. The sufficient conditions are first derived for the existence of guaranteed cost fuzzy controllers. These sufficient conditions are equivalent to a kind of linear matrix inequalities. Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controller.

  6. ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-Ⅰ intelligent leg

    Institute of Scientific and Technical Information of China (English)

    TAN Guan-zheng; DOU Hong-quan

    2007-01-01

    Based on the ant colony system (ACS) algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

  7. Recurrent RBFN-based fuzzy neural network control for X-Y-theta motion control stage using linear ultrasonic motors.

    Science.gov (United States)

    Lin, Faa-Jeng; Shieh, Po-Huang

    2006-12-01

    A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) control system is proposed to control the position of an X-Y-theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.

  8. control of a dc motor using fuzzy logic control algorithm

    African Journals Online (AJOL)

    user

    conditions such as changes in motor load demand, non- linearity ... Figure 1: Structure of a fuzzy logic controller (Source. [6]). A typical fuzzy logic ... mathematical modeling based on first principles; and via ..... applied. On the premise of these findings, it would be tactful in ... and Sugeno Type Fuzzy Inference Systems for Air.

  9. Practical Velocity Tracking Control of a Parallel Robot Based on Fuzzy Adaptive Algorithm

    Directory of Open Access Journals (Sweden)

    Zude Zhou

    2013-01-01

    Full Text Available Due to the advantages of its compact structure and high operation accuracy, the six degrees of freedom (6-DOF parallel platform has been widely used as a carrier of medical rehabilitation devices. Fuzzy adaptive algorithm does not depend on the mathematical model of controlled object, which possesses good nonlinear characteristics. Those entire features make it an effective method to control such complex and coupling platforms. To facilitate the application of robotics in lower limb rehabilitation fields, a robotic system in practical environment was established based on kinematics modeling of the 6-DOF Stewart-based platform. In order to improve the velocity tracking accuracy, this paper proposed a closed-loop control strategy based on fuzzy adaptive algorithm. The velocity feedback information was utilized to modify the PID parameters adaptively in realtime through fuzzy inference units. Several experiments in practical environment were conducted, and the results demonstrated that the proposed algorithm could effectively reduce the speed jitter, enhance the position and velocity tracking precision of the robot, and the reliability and robustness of the system could also be ensured.

  10. A Practical Fast Acting Control Scheme For Fuzzy Logic-Based Voltage Stabilization Control

    Directory of Open Access Journals (Sweden)

    E. E. EL-Kholy

    2005-03-01

    Full Text Available This paper presents a simplified control model for stabilizing a load voltage using a switched reactor in parallel with a fixed capacitor of static VAR compensator. Two IGBT’s are used to control the reactance of the switched reactor. A uniform pulse width modulation is used for controlling the two switches. The compensator has a simple control circuit and structure. A complete modeling and numerical simulation for the proposed systems is presented. A high speed Digital Signal Processor is used for implementing proportional-integral (PI and fuzzy load voltage controllers. Experimental results indicate the superiority of fuzzy logic control over the conventional proportional-integral control method. Simulation results are reported and proved to be in good agreement with the relevant experimental results.

  11. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Abdul Kareem

    2012-07-01

    Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.

  12. Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control.

    Science.gov (United States)

    Farivar, Faezeh; Shoorehdeli, Mahdi Aliyari

    2012-01-01

    In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization.

  13. A New Control Strategy Based Multi Converter UPQC Using Fuzzy Logic Controller to Improve the Power Quality Issues

    Directory of Open Access Journals (Sweden)

    Chandra Babu Paduchuri

    2014-01-01

    Full Text Available A design of multiconverter unified power quality conditioner to improve the power quality issues is presents in this paper. Modified SRF theory and fuzzy logic controller technique are incorporated in this modelling. This newly designed controller is connected to a source in order to compensate voltage and current in the two feeders. The expanded concept of UPQC is multi converter-UPQC; this system has two series voltage source converter (VSC and one shunt VSC connected back to back. In the proposed system, the power can be conveyed from one feeder to another in order to mitigate the voltage sag, swell, interruption and transient response of the system. The control strategies of multi converter-UPQC are designed based on the modified synchronous reference frame theory with fuzzy logic controller. The transient response of the fuzzy logic controller in dc-link voltage controller will be very fast. The relevant simulation and compensation performance analysis of multi converter-UPQC with fuzzy logic controller is performed using MATLAB/Simulink software.

  14. LMI-based stability analysis of fuzzy-model-based control systems using approximated polynomial membership functions.

    Science.gov (United States)

    Narimani, Mohammand; Lam, H K; Dilmaghani, R; Wolfe, Charles

    2011-06-01

    Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.

  15. Refining fuzzy logic controllers with machine learning

    Science.gov (United States)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  16. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    Energy Technology Data Exchange (ETDEWEB)

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  17. Fuzzeval: A Fuzzy Controller-Based Approach in Adaptive Learning for Backgammon Game

    DEFF Research Database (Denmark)

    Heinze, Mikael; Ortiz-Arroyo, Daniel; Larsen, Henrik Legind

    2005-01-01

    In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it re-...

  18. Real Time Fuzzy Based Speed and Direction Angle Control of an Automated Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Abdullah Başçı

    2015-04-01

    Full Text Available In this paper a fuzzy controller is applied to velocity and direction angle control of a certain type of wheeled mobile robots called Automated Guided Vehicles (AGVs. The velocity and direction angle of the AGV are controlled to keep the vehicle on desired path. A PI controller is also applied to AGV in order to show the robustness of the fuzzy controller. Experimental results prove that the fuzzy controller shows better tracking performance than the PI controller in terms of robustness, smoothness and fast dynamics. Results are also given for sudden disturbance and extra load conditions and satisfied results are obtained.

  19. SVR learning-based spatiotemporal fuzzy logic controller for nonlinear spatially distributed dynamic systems.

    Science.gov (United States)

    Zhang, Xian-Xia; Jiang, Ye; Li, Han-Xiong; Li, Shao-Yuan

    2013-10-01

    A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.

  20. A Position Controller Model on Color-Based Object Tracking using Fuzzy Logic

    Science.gov (United States)

    Cahyo Wibowo, Budi; Much Ibnu Subroto, Imam; Arifin, Bustanul

    2017-04-01

    Robotics vision is applying technology on the camera to view the environmental conditions as well as the function of the human eye. Colour object tracking system is one application of robotics vision technology with the ability to follow the object being detected. Several methods have been used to generate a good response position control, but most are still using conventional control approach. Fuzzy logic which includes several step of which is to determine the value of crisp input must be fuzzification. The output of fuzzification is forwarded to the process of inference in which there are some fuzzy logic rules. The inference output forwarded to the process of defuzzification to be transformed into outputs (crisp output) to drive the servo motors on the X-axis and Y-axis. Fuzzy logic control is applied to the color-based object tracking system, the system is successful to follow a moving object with average speed of 7.35 cm/s in environments with 117 lux light intensity.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. Network Based Building Lighting Design and Fuzzy Logic via Remote Control

    Directory of Open Access Journals (Sweden)

    Cemal YILMAZ

    2009-02-01

    Full Text Available In this paper, a network based building lighting system is implemented. Profibus-DP network structure is used in the design and Fuzzy Logic Controller (FLC is used on control of the building lighting. Informations received from sensors which measures level of the building illumination is used on FLC and they are transferred to the system by Profibus-DP network. Control of lighting luminaries are made via Profibus-DP network. The illuminance inside the bulding is fitted required level. Energy saving and healthy lighting facilities have been obtained by the design.

  3. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Abdul Kareem

    2012-08-01

    Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for thecontrol of dynamic uncertain systems. The proposed controller combines the advantages of Second orderSliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability androbustness of the system with the proposed controller are guaranteed. In addition, the proposed controlleris well suited for simple design and implementation. The effectiveness of the proposed controller over thefirst order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on aDC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desiredtransient response without causing chattering and error under steady-state conditions. The proposedcontroller is able to give robust performance in terms of rejection to input voltage variations and loadvariations

  4. Fuzzy logic based DSP controlled servo position control for ultrasonic motor

    Energy Technology Data Exchange (ETDEWEB)

    Bal, G.; Demirbas, S.; Colak, I. [Gazi University, Ankara (Turkey). Electrical Dept.; Bekiroglu, E. [Abant Izzet Baysal University, Bolu (Turkey). Dept. of Electrical and Electronics Engineering

    2004-12-01

    In this paper, position control of an ultrasonic motor was implemented on the basis of fuzzy reasoning. A digitally controllable two phase serial resonant inverter was developed to drive the ultrasonic motor by using a TMS320F243 digital signal processor. The driving frequency was used as a control input in the position control loop. The position characteristics obtained from the proposed drive and control system were demonstrated and evaluated by experiments. The experimental results verify that the developed position control scheme is highly effective, reliable and applicable for the ultrasonic motor. (author)

  5. Relationship between fuzzy controllers and PID controllers

    Institute of Scientific and Technical Information of China (English)

    李洪兴

    1999-01-01

    The internal relations between fuzzy controllers and PID controllers are revealed. First, it is pointed out that a fuzzy controller with one input and one output is just a piecewise P controller. Then it is proved that a fuzzy controller with two inputs and one output is just a piecewise PD (or I) controller with interaction between P and D (or PI). At last, the conclusion that a fuzzy controller with three inputs and one output is just a piecewise PID controller with interaction among P, I and D is given. Moreover, a kind of difference scheme of fuzzy controllers is designed.

  6. Fuzzy Logic Based Control of Power of PEM Fuel Cell System for Residential Application

    Directory of Open Access Journals (Sweden)

    Khaled MAMMAR

    2009-07-01

    Full Text Available This paper presents a dynamic model of Fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC controller is used to control active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation.

  7. Fuzzy Logic Based Control of Power of PEM Fuel Cell System for Residential Application

    OpenAIRE

    Khaled MAMMAR; CHAKER, Abdelkader

    2009-01-01

    This paper presents a dynamic model of Fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC) controller is used to control active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation.

  8. Research on Sensorless control strategies for Vehicle stability using Fuzzy based EDC

    Directory of Open Access Journals (Sweden)

    A.Ravi

    2014-05-01

    Full Text Available This paper presents an efficient and robust control scheme of Fuzzy Logic Electronic Differential Controller (FLEDC for sensorless drive based electric vehicle. The proposed system consists of two Brushless DC motors (BLDC that ensure the drive of the two back driving wheels of an electric vehicle. Electronic differential controller (EDC ensures the maximum torque and it can control both the driving wheel independently to turn at different speeds in any curve according to the steering angle. The sensorless control strategies include back EMF zero crossing detection and third harmonic voltage integration. Fuzzy logic based EDC is used on these sensorless control strategies which optimizes the slip rate within the specified limit and thus enhances the vehicle stability The performances in terms of slip rate, current, Torque, back EMF are obtained by the proposed method and are compared with those obtained using conventional control method. By this comparative investigation, a suitable control strategy has been identified. The effectiveness and substantiation of these analysis are ascertained in the MATLAB/Simulink environment and also experimentally validated.

  9. A Fuzzy-Logic-Based Controller for Three-Phase PWM Rectifier With Unity Power Factor Operation

    Directory of Open Access Journals (Sweden)

    A. Bouafia

    2008-03-01

    Full Text Available In this paper, direct power control (DPC of three-phase PWM rectifiers based on fuzzy logic controller is presented, without line voltage sensors. The control technique is built upon the ideas of the well known direct torque control (DTC for induction motors. The instantaneous active and reactive powers, directly controlled by selecting the optimum state of the converter, are used as the PWM control variables instead of the phase line currents being used. The proposed fuzzy logic controller presents the advantage to be based on linguistic description and does not require a mathematical model of the system. The controller ensures a good regulation of the output voltage, and guarantees the power factor close to one. The simulation results show that the designed fuzzy controller has a good dynamic behavior, a good rejection of impact load disturbance, and is very robust.

  10. Research on Fuzzy Immune Self-Adaptive PID Algorithm Based on New Smith Predictor for Networked Control System

    Directory of Open Access Journals (Sweden)

    Haitao Zhang

    2015-01-01

    Full Text Available We first analyze the effect of network-induced delay on the stability of networked control systems (NCSs. Then, aiming at stochastic characteristics of the time delay, we introduce a new Smith predictor to remove the exponential function with the time delay in the closed-loop characteristic equation of the NCS. Furthermore, we combine the fuzzy PID algorithm with the fuzzy immune control algorithm and present a fuzzy immune self-adaptive PID algorithm to compensate the influence of the model deviation of the controlled object. At last, a kind of fuzzy immune self-adaptive PID algorithm based on new Smith predictor is presented to apply to the NCS. The simulation research on a DC motor is given to show the effectiveness of the proposed algorithm.

  11. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re......-adjustment of the neural network during the recall phase. In order to illustrate the effectiveness of the proposed technique, the controller is tested on a cart-pole balancing problem. Results of extensive simulation studies show a very good performance in comparison with other intelligent control methods....

  12. A SELF-ORGANISING FUZZY LOGIC CONTROLLER

    African Journals Online (AJOL)

    ES Obe

    One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a ... an algorithm that allows a designer to initially specify a possibly inaccurate rule-base, which ... an adaptive FLC strategy based on these ideas.

  13. Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant.

    Science.gov (United States)

    Bae, H; Seo, H Y; Kim, S; Kim, Y

    2006-01-01

    Because biological wastewater treatment plants (WWTPs) involve a long time-delay and various disturbances, in general, skilled operators manually control the plant based on empirical knowledge. And operators usually diagnose the plant using similar cases experienced in the past. For the effective management of the plant, system automation has to be accomplished based upon operating recipes. This paper introduces automatic control and diagnosis based upon the operator's knowledge. Fuzzy logic was employed to design this knowledge-based controller because fuzzy logic can convert the linguistic information to rules. The controller can manage the influent and external carbon in considering the loading rate. The input of the controller is not the loading rate but the dissolved oxygen (DO) lag-time, which has a strong relation to the loading rate. This approach can replace an expensive sensor, which measures the loading rate and ammonia concentration in the reactor, with a cheaper DO sensor. The proposed controller can assure optimal operation and prevent the over-feeding problem. Case-based diagnosis was achieved by the analysis of profile patterns collected from the past. A new test profile was diagnosed by comparing it with template patterns containing normal and abnormal cases. The proposed control and diagnostic system will guarantee the effective and stable operation of WWTPs.

  14. Fuzzy Logic Based MPPT Controller for a Small Wind Turbine System

    DEFF Research Database (Denmark)

    Petrila, Diana; Blaabjerg, Frede; Muntean, Nicolae

    2012-01-01

    This paper describes the design of a maximum power point tracking (MPPT) strategy for a variable speed, small scale, wind turbine systems based on a fuzzy logic controller (FLC). The FLC has as input variables the change in mechanical power (ΔPm), the change in rotor speed (Δω), and the sign of ΔPm...... operation. Therefore, the mechanical power (Pm) is composed of the generator mechanical (input) power (Pg) plus the dynamic power, resulting in the dynamic power versus rotating speed curve. The controller is able to track the maximum power point for changing wind conditions, and is robust with respect...

  15. Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller

    Science.gov (United States)

    Saini, Sanju; Saini, J. S.

    2012-11-01

    This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.

  16. Research on Acceleration Compensation Strategy of Electric Vehicle Based on Fuzzy Control Theory

    Science.gov (United States)

    Zhu, Tianjun; Li, Bin; Zong, Changfu; Wei, Zhicheng

    2017-09-01

    Nowadays, the driving technology of electric vehicle is developing rapidly. There are many kinds of methods in driving performance control technology. The paper studies the acceleration performance of electric vehicle. Under the premise of energy management, an acceleration power compensation method by fuzzy control theory based on driver intention recognition is proposed, which can meet the driver’s subjective feelings better. It avoids the problem that the pedal opening and power output are single correspondence when the traditional vehicle accelerates. Through the simulation test, this method can significantly improve the performance of acceleration and output torque smoothly in non-emergency acceleration to ensure vehicle comfortable and stable.

  17. Tank Fire Control Systems Accuracy Assignment Based on Fuzzy Comprehensive Judgment Method

    Institute of Scientific and Technical Information of China (English)

    刘文果; 陈杰; 窦丽华

    2003-01-01

    A method of accuracy assignment based on fuzzy comprehensive judgment method (FCJM) in tank fire control system is proposed. From the flowing route of the error sources and their respective correlative signals, the transfer functions of several sources are analysed by means of mathematic simulation, and FCJM is applied to obtain the cost comprehensive factor for each part of system, combining its error sensitivity factor the mathematical model is built to solve the accuracy assignment problem. Simulation result shows the proposed method can help designer of tank fire control system work out an optimal system more efficiently and more economically.

  18. STATCOM Stabilizer based on Fuzzy Logic Control for Damping Power Oscillation

    Directory of Open Access Journals (Sweden)

    Prechanon Kumkratug

    2011-01-01

    Full Text Available Problem statement: In power systems, there exists a continuous challenge to improve dynamic performance of power system. Approach: Static Synchronous Compensator (STATCOM is a power electronic based device that has the capability of controlling the power flow through a line. This study applies the Static Synchronous Compensator (STATCOM to control the power flow during dynamic period. To verify the effect of the STATCOM on dynamic performance, the mathematical model and control strategy of a STATCOM was needed to be presented. The converters of STATCOM were represented by variable voltage source with associate transformer leakage reactance and the voltage source and the reactance were transformed into current injection. The current injection model of STATCOM was modeled into power flow equation and thus it was used to determine control strategy. This study applies the fuzzy logic control to determine the control strategy of STATCOM. The swing curves of the three phase faulted power system without and with a STATCOM was tested and compared in various cases. Results: The swing curve of the system with STATCOM based fuzzy logic control had the less amplitude during the dynamic period. Conclusion: STATCOM can improve the dynamic performance of the system after disturbance.

  19. Improvement on fuzzy controller design techniques

    Science.gov (United States)

    Wang, Paul P.

    1993-01-01

    This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.

  20. Intelligent Flow Control Technique of ABR Service in ATM Networks Based on Fuzzy Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZhangLiangjie; LiYanda; 等

    1997-01-01

    The ATM Forum voted to implement the rate-based flow control(RBFC)scheme to manage traffic in asynchronous transfer mode(ATM)networks.RBFC will be used specifically to manage available bit rate(ABR)service.Through the study of the transmission rate adjusting of the ABR traffic source,we propose and enhanced bit rate feedback(EBRF)scheme,which is the dynamic bit rate adjusting scheme based on fuzzy neural network(FNN).Simulation results show that it can enhance the switch buffer utilization on the premise of a full link utilization.

  1. Temperature Control System Using Fuzzy Logic Technique

    Directory of Open Access Journals (Sweden)

    Isizoh A N

    2012-06-01

    Full Text Available Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based-temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.

  2. Hardware simulation of automatic braking system based on fuzzy logic control

    Directory of Open Access Journals (Sweden)

    Noor Cholis Basjaruddin

    2016-07-01

    Full Text Available In certain situations, a moving or stationary object can be a barrier for a vehicle. People and vehicles crossing could potentially get hit by a vehicle. Objects around roads as sidewalks, road separator, power poles, and railroad gates are also a potential source of danger when the driver is inattentive in driving the vehicle. A device that can help the driver to brake automatically is known as Automatic Braking System (ABS. ABS is a part of the Advanced Driver Assistance Systems (ADAS, which is a device designed to assist the driver in driving the process. This device was developed to reduce human error that is a major cause of traffic accidents. This paper presents the design of ABS based on fuzzy logic which is simulated in hardware by using a remote control car. The inputs of fuzzy logic are the speed and distance of the object in front of the vehicle, while the output of fuzzy logic is the intensity of braking. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the design of ABS can work according to design.

  3. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Directory of Open Access Journals (Sweden)

    Y.-M. Chiang

    2010-09-01

    Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagatiom fuzzy neural network (CFNN for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  4. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Directory of Open Access Journals (Sweden)

    Y.-M. Chiang

    2011-01-01

    Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  5. LMI-Based Fuzzy Optimal Variance Control of Airfoil Model Subject to Input Constraints

    Science.gov (United States)

    Swei, Sean S.M.; Ayoubi, Mohammad A.

    2017-01-01

    This paper presents a study of fuzzy optimal variance control problem for dynamical systems subject to actuator amplitude and rate constraints. Using Takagi-Sugeno fuzzy modeling and dynamic Parallel Distributed Compensation technique, the stability and the constraints can be cast as a multi-objective optimization problem in the form of Linear Matrix Inequalities. By utilizing the formulations and solutions for the input and output variance constraint problems, we develop a fuzzy full-state feedback controller. The stability and performance of the proposed controller is demonstrated through its application to the airfoil flutter suppression.

  6. Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

    Science.gov (United States)

    Peng, Chen; Ma, Shaodong; Xie, Xiangpeng

    2017-02-07

    This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

  7. Longitudinal Motion Control of AUV Based on Fuzzy Sliding Mode Method

    Directory of Open Access Journals (Sweden)

    Duo Qi

    2016-01-01

    Full Text Available According to the characteristics of AUV movement, a fuzzy sliding mode controller was designed, in which fuzzy rules were adopted to estimate the switching gain to eliminate disturbance terms and reduce chattering. The six-degree-of-freedom model of AUV was simplified and longitudinal motion equations were established on the basis of previous research. The influences of first-order wave force and torque were taken into consideration. The REMUS was selected to simulate the control effects of conventional sliding mode controller and fuzzy sliding mode controller. Simulation results show that the fuzzy sliding mode controller can meet the requirements and has higher precision and stronger antijamming performances compared with conventional sliding mode controller.

  8. Fuzzy Logic Control ASIC Chip

    Institute of Scientific and Technical Information of China (English)

    沈理

    1997-01-01

    A fuzzy logic control VLSI chip,F100,for industry process real-time control has been designed and fabricated with 0.8μm CMOS technology.The chip has the features of simplicity,felexibility and generality.This paper presents the Fuzzy control inrerence method of the chip,its VLSI implementation,and testing esign consideration.

  9. A fuzzy-logic based diagnosis and control of a reactor performing complete autotrophic nitrogen removal

    DEFF Research Database (Denmark)

    Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist

    2013-01-01

    This contribution explores the use of diagnosis and control modules based on fuzzy set theory and logic for bioreactor monitoring and control. With this aim, two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information...... to control the reactor. The separation in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control...... system was designed for a complete autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation. Additionally, the diagnosis tool was demonstrated by analysis 100 days of experimental data....

  10. Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block.

    Science.gov (United States)

    Shieh, J S; Fan, S Z; Chang, L W; Liu, C C

    2000-01-01

    The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation. The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. The Student's test was used to compare the variance between the groups. p control of muscle relaxation with a mean T1% error of -0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21-0.49 mg x kg(-1) x h(-1). When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p controller activity of these two drugs showed no significant difference (p > 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76) %) indicated a good controller activity. The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control

  11. Design and simulation of an image-based fuzzy tracking controller for a wheeled mobile robot

    Science.gov (United States)

    Shiao, Ying Shing; Wu, Ching Wei

    2011-12-01

    Image processing algorithms and fuzzy logic method are used to design a visual tracking controller for mobile robot navigation. In this paper, a wheeled mobile robot is equipped with a camera for detecting its task space. The grabbed environmental images are treated using image recognition processing to obtain target's size and position. The images are treated using input membership functions as the fuzzy logic controller input. The recognized target's size and position are input into a fuzzy logic controller in which fuzzy rules are used for inference. The inference results are output to the defuzzifier to obtain a physical control signal to control the mobile robot's movement. The velocity and direction of the mobile robot are the output of fuzzy logic controller. The differences in velocities for two wheels are used to control the robot's movement directions. The fuzzy logic controller outputs the control commands to drive the mobile robot to reach a position 50cm front of the target location. The simulation results verify that the proposed FLC is effective in navigating the mobile robot to track a moving target.

  12. Fuzzy control in environmental engineering

    CERN Document Server

    Chmielowski, Wojciech Z

    2016-01-01

    This book is intended for engineers, technicians and people who plan to use fuzzy control in more or less developed and advanced control systems for manufacturing processes, or directly for executive equipment. Assuming that the reader possesses elementary knowledge regarding fuzzy sets and fuzzy control, by way of a reminder, the first parts of the book contain a reminder of the theoretical foundations as well as a description of the tools to be found in the Matlab/Simulink environment in the form of a toolbox. The major part of the book presents applications for fuzzy controllers in control systems for various manufacturing and engineering processes. It presents seven processes and problems which have been programmed using fuzzy controllers. The issues discussed concern the field of Environmental Engineering. Examples are the control of a flood wave passing through a hypothetical, and then the real Dobczyce reservoir in the Raba River, which is located in the upper Vistula River basin in Southern Poland, th...

  13. Fuzzy logic based feedback control system for laser beam pointing stabilization.

    Science.gov (United States)

    Singh, Ranjeet; Patel, Kiran; Govindarajan, J; Kumar, Ajai

    2010-09-20

    This paper reports a fuzzy logic based feedback control system for beam pointing stabilization of a high-power nanosecond Nd:YAG laser operating at 30 Hz. This is achieved by generating the correcting signal for each consequent pulse from the error in the pointing position of the previous laser pulse. We have successfully achieved a reduction of beam position fluctuation from ±60 to ±5.0 μrad without the focusing optics and ±0.9 μrad with focusing optics.

  14. Fuzzy logic controller based three-phase shunt active power filter under unbalanced network

    Energy Technology Data Exchange (ETDEWEB)

    Belaidi, R.; Chikouche, A.; Fathi, M. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A.; Guendouz, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr

    2011-07-01

    In recent years, public awareness of power quality issues in distribution systems has arisen. The photovoltaic interactive shunt active power filter is a system which provides harmonic current damping and reactive power compensation and a fuzzy logic controller was created to adjust the energy storage of the DC voltage; the aim of this paper is to study the performance of the fuzzy logic controller. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of the system; the instantaneous reactive power theory was utilized. Results showed that the use of the fuzzy logic controller achieves a reduction of the total harmonic distortion of the current from 26.54% to 2.27%. This study demonstrated that the fuzzy logic controller combined with the photovoltaic interactive shunt active power filter helps improve power quality by filtering harmonic currents and compensating reactive power generated by non-linear loads.

  15. A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors.

    Science.gov (United States)

    Robles, A; Latrille, E; Ruano, M V; Steyer, J P

    2017-01-01

    The main objective of this work was to develop a controller for biogas production in continuous anaerobic fixed-bed reactors, which used effluent total volatile fatty acids (VFA) concentration as control input in order to prevent process acidification at closed loop. To this aim, a fuzzy-logic-based control system was developed, tuned and validated in an anaerobic fixed-bed reactor at pilot scale that treated industrial winery wastewater. The proposed controller varied the flow rate of wastewater entering the system as a function of the gaseous outflow rate of methane and VFA concentration. Simulation results show that the proposed controller is capable to achieve great process stability even when operating at high VFA concentrations. Pilot results showed the potential of this control approach to maintain the process working properly under similar conditions to the ones expected at full-scale plants.

  16. 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

  17. Design of a fuzzy logic based controller for neutron power regulation; Diseno de un controlador basado en logica difusa para la regulacion de flujo neutronico

    Energy Technology Data Exchange (ETDEWEB)

    Velez D, D

    2000-07-01

    This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)

  18. HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS

    Directory of Open Access Journals (Sweden)

    M.K. Tan

    2011-07-01

    Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.

  19. Design issues for a reinforcement-based self-learning fuzzy controller

    Science.gov (United States)

    Yen, John; Wang, Haojin; Dauherity, Walter

    1993-01-01

    Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

  20. Efficient adaptive fuzzy control scheme

    NARCIS (Netherlands)

    Papp, Z.; Driessen, B.J.F.

    1995-01-01

    The paper presents an adaptive nonlinear (state-) feedback control structure, where the nonlinearities are implemented as smooth fuzzy mappings defined as rule sets. The fine tuning and adaption of the controller is realized by an indirect adaptive scheme, which modifies the parameters of the fuzzy

  1. Research on Fuzzy Control for Automatic Transmission of Tracked Vehicles

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A principle of fuzzy control for tracked vehicles is proposed to make its automatic transmission system be able to adapt complex running conditions, and a model of its power train is established to be used in simulation. Based on the fuzzy control method, a fuzzy shift control system composed of a basic shift strategy and a fuzzy modification module is developed to improve the dynamic characteristics and cross-country maneuverability. Simulation results show that the fuzzy shift strategy can improve the shift quality under manifold driving conditions and avoid cycled shift effectively. Therefore,the proposed fuzzy shift strategies are proved to be feasible and practicable.

  2. Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.

    Science.gov (United States)

    Tong, Shaocheng; Sui, Shuai; Li, Yongming

    2015-12-01

    In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

  3. Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

    Science.gov (United States)

    Coelho, Antonio Augusto Rodrigues

    2016-01-01

    This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

  4. Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation

    Directory of Open Access Journals (Sweden)

    Dongjie Li

    2012-01-01

    Full Text Available The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE, and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments.

  5. GA and Lyapunov theory-based hybrid adaptive fuzzy controller for non-linear systems

    Science.gov (United States)

    Roy, Ananya; Das Sharma, Kaushik

    2015-02-01

    In this present article, a new hybrid methodology for designing stable adaptive fuzzy logic controllers (AFLCs) for a class of non-linear system is proposed. The proposed design strategy exploits the features of genetic algorithm (GA)-based stochastic evolutionary global search technique and Lyapunov theory-based local adaptation scheme. The objective is to develop a methodology for designing AFLCs with optimised free parameters and guaranteed closed-loop stability. Simultaneously, the proposed method introduces automation in the design process. The stand-alone Lyapunov theory-based design, GA-based design and proposed hybrid GA-Lyapunov design methodologies are implemented for two benchmark non-linear plants in simulation case studies with different reference signals and one experimental case study. The results demonstrate that the hybrid design methodology outperforms the other control strategies on the whole.

  6. The Optimized Disign of the Fuzzy Controller(Ⅰ)--The predigested disquisition of rules of fuzzy control

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.

  7. ESO-Based Fuzzy Sliding-Mode Control for a 3-DOF Serial-Parallel Hybrid Humanoid Arm

    Directory of Open Access Journals (Sweden)

    Yueling Wang

    2014-01-01

    Full Text Available This paper presents a unique ESO-based fuzzy sliding-mode controller (FSMC-ESO for a 3-DOF serial-parallel hybrid humanoid arm (HHA for the trajectory tracking control problem. The dynamic model of the HHA is obtained by Lagrange method and is nonlinear in dynamics with inertia uncertainty and external disturbance. The FSMC-ESO is based on the combination of the sliding-mode control (SMC, extended state observer (ESO theory, and fuzzy control (FC. The SMC is insensitive to both internal parameter uncertainties and external disturbances. The motivation for using ESO is to estimate the disturbance in real-time. The fuzzy parameter self-tuning strategy is proposed to adjust the switching gain on line according to the running state of the system. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. The effectiveness and robustness of the designed FSMC-ESO are illustrated by simulations.

  8. Fuzzy Sliding Mode Control of Plate Vibrations

    Directory of Open Access Journals (Sweden)

    Manu Sharma

    2010-01-01

    Full Text Available In this paper, fuzzy logic is meshed with sliding mode control, in order to control vibrations of a cantilevered plate. Test plate is instrumented with a piezoelectric sensor patch and a piezoelectric actuator patch. Finite element method is used to obtain mathematical model of the test plate. A design approach of a sliding mode controller for linear systems with mismatched time-varying uncertainties is used in this paper. It is found that chattering around the sliding surface in the sliding mode control can be checked by the proposed fuzzy sliding mode control approach. With presented fuzzy sliding mode approach the actuator voltage time response has a smooth decay. This is important because an abrupt decay can excite higher modes in the structure. Fuzzy rule base consisting of nine rules, is generated from the sliding mode inequality. Experimental implementation of the control approach verify the theoretical findings. For experimental implementation, size of the problem is reduced using modal truncation technique. Modal displacements as well as velocities of first two modes are observed using real-time kalman observer. Real time implementation of fuzzy logic based control has always been a challenge because a given set of rules has to be executed in every sampling interval. Results in this paper establish feasibility of experimental implementation of presented fuzzy logic based controller for active vibration control.

  9. Disturbance Observer-Based Fuzzy Control of Uncertain MIMO Mechanical Systems With Input Nonlinearities and its Application to Robotic Exoskeleton.

    Science.gov (United States)

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2017-04-01

    We develop a novel disturbance observer-based adaptive fuzzy control approach in this paper for a class of uncertain multi-input-multi-output mechanical systems possessing unknown input nonlinearities, i.e., deadzone and saturation and time-varying external disturbance. It is shown that the input nonlinearities can be represented by a nominal part and a nonlinear disturbance term. High-dimensional integral-type Lyapunov function is used to construct the controller. Fuzzy logic system is employed to cancel model uncertainties, and disturbance observer is also integrated into control design to compensate the fuzzy approximation error, external disturbance, and nonlinear disturbance caused by the unknown input nonlinearities. Semiglobally uniformly ultimately boundness of the closed-loop control system is guaranteed with tracking errors keeping bounded. Experimental studies on a robotic exoskeleton using the proposed control demonstrate the effectiveness of the approach.

  10. Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.

  11. Performance-Based Adaptive Fuzzy Tracking Control for Networked Industrial Processes.

    Science.gov (United States)

    Wang, Tong; Qiu, Jianbin; Yin, Shen; Gao, Huijun; Fan, Jialu; Chai, Tianyou

    2016-08-01

    In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

  12. SPOT WELDING QUALITY FUZZY CONTROL SYSTEM BASED ON MULTISENSOR INFORMATION FUSION

    Institute of Scientific and Technical Information of China (English)

    CHANG Yunlong; SU Hang; LIN Bin; YANG Xu

    2007-01-01

    The multisensor information fusion technology is adopted for real time measuring the four Parameters which are connected closely with the weld nugget size(welding current, electrode displacement, dynamic resistance, welding time), thus much more original information is obtained. In this way, the difficulty caused by measuring indirectly weld nugget size can be decreased in spot welding quality control, and the stability of spot welding quality can be improved. According to this method, two-dimensional fuzzy Controllers are designed with the information fusion result as input and the thyristor control signal as output. The spot welding experimental results indicate that the spot welding quality intelligent control method based on multisensor information fusion technology can compensate the influence caused by variable factors in welding process and ensure the stability of welding quality.

  13. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor

    Directory of Open Access Journals (Sweden)

    K. Premkumar

    2016-06-01

    Full Text Available In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation.

  14. Perancangan Autonomous Landing pada Quadcopter Menggunakan Behavior-Based Intelligent Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Chalidia Nurin Hamdani

    2013-09-01

    Full Text Available Quadcopter adalah salah satu platform unmanned aerial vehicle (UAV yang saat ini banyak diriset karena kemampuannya melakukan take-off dan landing secara vertikal. Karena menggunakan 4 motor brushless sebagai penggerak utama, quadcopter memiliki kompleksitas yang cukup tinggi baik dalam pemodelan maupun pengendalian. Landing merupakan salah satu mekanisme pada quadcopter yang membutuhkan kecepatan yang akurat dan aman dengan tetap mempertahankan keseimbangan. Pada penelitian ini, penulis menggunakan Behavior-Based Intelligent Fuzzy Control (BBIFC sebagai dasar kontrol untuk penerapan autonomous landing pada quadcopter. BBIFC adalah salah satu skema high-level control di mana desain kontrol terdiri dari beberapa layer. Ada 2 layer yang digunakan pada penelitian ini yaitu layer untuk pengendalian sudut pitch, roll, yaw dan layer untuk pengendalian ketinggian. Setiap layer memiliki mekanisme kontrol yang berbeda yang didesain menggunakan Intelligent Fuzzy Controller dan kontroler PID. Dengan metode ini dihasilkan algoritma untuk mekanisme safe autonomous landing dengan mengikuti sinyal eksponensial di mana quadcopter mencapai titik 0 (nol meter dalam waktu 15 detik dan Kontroler PID dapat mengendalikan keseimbangan quadcopter dalam waktu 7.97 detik untuk roll dan pitch serta 1.25 detik untuk yaw sejak gangguan sudut diberikan.

  15. Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

    CERN Document Server

    Xia, Feng; Sun, Youxian; Tian, Yu-Chu

    2008-01-01

    Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantag...

  16. A Fuzzy Logic Based Power Control for Wideband Code Division Multiple Access Wireless Networks

    Directory of Open Access Journals (Sweden)

    T. Ravichandran

    2012-01-01

    Full Text Available Problem statement: Resource management is one of the most important engineering issues in 3G systems where multiple traffic classes are supported each being characterized by its required Quality of Service (QoS parameters. Call Admission Control (CAC is one of the resource management functions, which regulates network access to ensure QoS provisioning. Efficient CAC is necessary for the QoS provisioning in WCDMA environment. The effective functioning of WCDMA systems is influenced by the power control utility. Approach: In this study, we propose to design a fuzzy logic based power control for Wideband Code Division Multiple Access Wireless Networks. This proposed technique is aimed at multiple services like voice, video and data for multiclass users. The fuzzy logic technique is used to estimate the optimal admissible users group inclusive of optimum transmitting power level. This technique reduces the interference level and call rejection rate. Results: By simulation results, we demonstrate that the proposed technique achieve reduced energy consumption for a cell with increased throughput. Conclusion: The proposed technique minimizes the power consumption and call rejection rate.

  17. FPGA based Fuzzy Logic Controller for plasma position control in ADITYA Tokamak

    Energy Technology Data Exchange (ETDEWEB)

    Suratia, Pooja, E-mail: poojasuratia@yahoo.com [Electrical Engineering Department, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara 390001, Gujarat (India); Patel, Jigneshkumar, E-mail: jjp@ipr.res.in [Institute for Plasma Research, Bhat, Gandhinagar 382428, Gujarat (India); Rajpal, Rachana, E-mail: rachana@ipr.res.in [Institute for Plasma Research, Bhat, Gandhinagar 382428, Gujarat (India); Kotia, Sorum, E-mail: smkotia-eed@msubaroda.ac.in [Electrical Engineering Department, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara 390001, Gujarat (India); Govindarajan, J., E-mail: govindarajan@ipr.res.in [Institute for Plasma Research, Bhat, Gandhinagar 382428, Gujarat (India)

    2012-11-15

    Highlights: Black-Right-Pointing-Pointer Evaluation and comparison of the working performance of FLC is done with that of PID Controller. Black-Right-Pointing-Pointer FLC is designed using MATLAB Fuzzy Logic Toolbox, and validated on ADITYA RZIP model. Black-Right-Pointing-Pointer FLC was implemented on a FPGA. The close-loop testing is done by interfacing FPGA to MATLAB/Simulink. Black-Right-Pointing-Pointer Developed FLC controller is able to maintain the plasma column within required range of {+-}0.05 m and was found to give robust control against various disturbances and faster and smoother response compared to PID Controller. - Abstract: Tokamaks are the most promising devices for obtaining nuclear fusion energy from high-temperature, ionized gas termed as Plasma. The successful operation of tokamak depends on its ability to confine plasma at the geometric center of vacuum vessel with sufficient stability. The quality of plasma discharge in ADITYA Tokamak is strongly related to the radial position of the plasma column in the vacuum vessel. If the plasma column approaches too near to the wall of vacuum vessel, it leads to minor or complete disruption of plasma. Hence the control of plasma position throughout the entire plasma discharge duration is a fundamental requirement. This paper describes Fuzzy Logic Controller (FLC) which is designed for radial plasma position control. This controller is tested and evaluated on the ADITYA RZIP control model. The performance of this FLC was compared with that of Proportional-Integral-Derivative (PID) Controller and the response was found to be faster and smoother. FLC was implemented on a Field Programmable Gate Array (FPGA) chip with the use of a Very High-Speed Integrated-Circuits Hardware Description-Language (VHDL).

  18. WIDE-AREA BASED ON COORDINATED TUNING OF FUZZY PSS AND FACTS CONTROLLER IN MULTI-MACHINE ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Homayoun Ebrahimian

    2016-03-01

    Full Text Available In this paper coordination of fuzzy power system stabilizer (FPSS and flexible ac transmission systems (FACTS have been considered in a multi-machine power system. The proposed model, has been applied for a wide-area power system. The proposed FPSS presented with local, nonlinear feedbacks, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs. For this model, in fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Also, the parameters of FACTS controller have been evaluated by improved honey bee mating optimization (IHBMO. The effectiveness of the proposed method has been applied over two case studies of single-machine infinite-bus (SMIB and two areas four machine (TAFM Kundur’s power system. The obtained results demonstrate the superiority of proposed strategy.

  19. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    OpenAIRE

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y. -F.; Chang, F.-J.

    2011-01-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy infere...

  20. Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID

    Science.gov (United States)

    Chen, Zhigang; Qu, Jiangang

    2017-09-01

    In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.

  1. Simulation and analysis of a Truck Model's ride comfort based on fuzzy adaptive control theory

    Institute of Scientific and Technical Information of China (English)

    JIANG Li-biao; WANG Deng-feng; NI Qiang; TAN Wei-ming

    2007-01-01

    This paper tried to analyse and verify the fuzzy adaptive control strategy of electronic control air suspension system for heavy truck. Created the seven-freedoms vehicle suspension model, and the road input model; with Matlab/Simulink toolboxes and modules, built dynamical system simulation model for heavy truck with air suspension, fuzzy adaptive control model, height control model for air spring, and intelligent control and analyse on root mean square value of acceleration of gravity center of the vehicle under excitation of road. Results show that the fuzzy control had less help to the body vibration on the better pavement, but had the better benefit on the bad road, and the vehicle's root mean square value of acceleration of gravity center is less than passive suspension's obviously.

  2. MPPT Based on Fuzzy Logic Controller (FLC for Photovoltaic (PV System in Solar Car

    Directory of Open Access Journals (Sweden)

    Seno Aji

    2013-12-01

    Full Text Available This paper presents a control called Maximum Power Point Tracking (MPPT for photovoltaic (PV system in a solar car. The main purpose of this system is to extracts PV power maximally while keeping small losses using a simple design of converter. Working principle of MPPT based fuzzy logic controller (MPPT-FLC is to get desirable values of reference current and voltage. MPPT-FLC compares them with the values of the PV's actual current and voltage to control duty cycle value. Then the duty cycle value is used to adjust the angle of ignition switch (MOSFET gate on the Boost converter. The proposed method was shown through simulation performed using PSIM and MATLAB software. Simulation results show that the system is able to improve the PV power extraction efficiency significantly by approximately 98% of PV’s power.

  3. Greenhouse irrigation control system design based on ZigBee and fuzzy PID technology

    Science.gov (United States)

    Zhou, Bing; Yang, Qiliang; Liu, Kenan; Li, Peiqing; Zhang, Jing; Wang, Qijian

    In order to achieve the water demand information accurately detect of the greenhouse crop and its precision irrigation automatic control, this article has designed a set of the irrigated control system based on ZigBee and fuzzy PID technology, which composed by the soil water potential sensor, CC2530F256 wireless microprocessor, IAR Embedded Workbench software development platform. And the time of Irrigation as the output .while the amount of soil water potential and crop growth cycle as the input. The article depended on Greenhouse-grown Jatropha to verify the object, the results show that the system can irrigate timely and appropriately according to the soil water potential and water demend of the different stages of Jatropha growth , which basically meet the design requirements. Therefore, the system has broad application prospects in the amount of greenhouse crop of fine control irrigation.

  4. Shunt hybrid active power filter under nonideal voltage based on fuzzy logic controller

    Science.gov (United States)

    Dey, Papan; Mekhilef, Saad

    2016-09-01

    In this paper, a synchronous reference frame (SRF) method based on a modified phase lock loop (PLL) circuit is developed for a three-phase four-wire shunt hybrid active power filter (APF). Its performance is analysed under unbalanced grid conditions. The dominant lower order harmonics as well as reactive power can be compensated by the passive elements, whereas the active part mitigates the remaining distortions and improves the power quality. As different control methods show contradictory performance, fuzzy logic controller is considered here for DC-link voltage regulation of the inverter. Extensive simulations of the proposed technique are carried out in a MATLAB-SIMULINK environment. A laboratory prototype has been built on dSPACE1104 platform to verify the feasibility of the suggested SHAPF controller. The simulation and experimental results validate the effectiveness of the proposed technique.

  5. Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC Valves Used in Diesel Engines

    Directory of Open Access Journals (Sweden)

    Umut Tugsal

    2014-05-01

    Full Text Available In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves.

  6. A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

    Science.gov (United States)

    van der Lee, J H; Svrcek, W Y; Young, B R

    2008-01-01

    Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.

  7. Robust Optimal Output Tracking Control of A Midwater Trawl System Based on T-S Fuzzy Nonlinear Model

    Institute of Scientific and Technical Information of China (English)

    ZHOU Hua; CHEN Ying-long; YANG Hua-yong

    2013-01-01

    A robust optimal output tracking control method for a midwater trawl system is investigated based on T-S fuzzy nonlinear model.A simplified nonlinear mathematical model is first employed to represent a midwater trawl system,and then a T-S fuzzy model is adopted to approximate the nonlinear system.Since the strong nonlinearities and the external disturbance of the trawling system,a mixed H2/H∞ fuzzy output tracking control strategy via T-S fuzzy system is proposed to regulate the trawl depth to follow a desired trajectory.The trawl depth can be regulated by adjusting the winch velocity automatically and the tracking error can be minimized according to the robust optimal criterion.In order to validate the proposed control method,a computer simulation is conducted.The simulation results indicate that the proposed fuzzy robust optimal controller make the trawl net rapidly follow the desired trajectory under the model uncertainties and the external disturbance caused by wave and current.

  8. Performance analysis of CDMA power control system based on fuzzy prediction

    Institute of Scientific and Technical Information of China (English)

    杨涛; 谢剑英

    2004-01-01

    Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results,we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.

  9. A Research about Greenhouse Robot’s Motion Control Based on Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    Wang Sheng; Wang Shuai; Sun Yanhong

    2013-01-01

    This article, with greenhouse mobile robot as the investigated subject, planted fuzzy control into greenhouse robot’s route control system in order to develop a route control method that fits in the complicated environment in greenhouses. Such method aims at ensuring the safety of greenhouse mobile robot by actualizing the function of tracking routes and avoiding obstacles.

  10. Variable Admittance Control Based on Fuzzy Reinforcement Learning for Minimally Invasive Surgery Manipulator.

    Science.gov (United States)

    Du, Zhijiang; Wang, Wei; Yan, Zhiyuan; Dong, Wei; Wang, Weidong

    2017-04-12

    In order to get natural and intuitive physical interaction in the pose adjustment of the minimally invasive surgery manipulator, a hybrid variable admittance model based on Fuzzy Sarsa(λ)-learning is proposed in this paper. The proposed model provides continuous variable virtual damping to the admittance controller to respond to human intentions, and it effectively enhances the comfort level during the task execution by modifying the generated virtual damping dynamically. A fuzzy partition defined over the state space is used to capture the characteristics of the operator in physical human-robot interaction. For the purpose of maximizing the performance index in the long run, according to the identification of the current state input, the virtual damping compensations are determined by a trained strategy which can be learned through the experience generated from interaction with humans, and the influence caused by humans and the changing dynamics in the robot are also considered in the learning process. To evaluate the performance of the proposed model, some comparative experiments in joint space are conducted on our experimental minimally invasive surgical manipulator.

  11. Fuzzy Control of Semi-Active Suspension Based on 7-DOF Tracked Vehicle

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ming-ze; ZHANG Yong-fa

    2008-01-01

    On the basis of analyzing the system constitution of tracked vehicle semi-active suspension, which consists of electrorheological (ER) damper and shape memory alloy (SMA) spring, a seven degree of freedom (7-DOF) dynamic model is established. The change of the elasticity of SMA spring and the characteristics of the ER shock absorber are studied. In addition, the study about fuzzy control logic is also carried out. Simulation of a C grade road under random excitations and definite disturbances is performed. Simulation result shows the fuzzy control strategy, which is used in the control of semi-active suspension system with ER damper and SMA springs, is effective, stable and reliable.

  12. Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators

    Directory of Open Access Journals (Sweden)

    Sahraoui Mustapha

    2014-10-01

    Full Text Available The present paper is dedicated to the presentation and implementation of an optimized technique allowing an on-line estimation of a robot manipulator parameters to use them in a computed torque control. Indeed the proposed control law needs the exact robot model to give good performances. The complexity of the robot manipulator and its strong non-linearity makes it hard to know its parameters. Therefore, we propose in this paper to use neuro-fuzzy networks Sequential Adaptive Fuzzy Inference System (SAFIS to estimate the parameters of the controlled robot manipulator.

  13. Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.

    Science.gov (United States)

    Zheng, Ying; Fang, Huajing; Wang, Hua O

    2006-08-01

    A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches.

  14. Fuzzy Sliding Mode Lateral Control of Intelligent Vehicle Based on Vision

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2013-01-01

    Full Text Available The lateral control of intelligent vehicle is studied in this paper, with the intelligent vehicle DLUIV-1 based on visual navigation as the object of research. Firstly, the lateral control model based on visual preview is established. The kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, according to the characteristics of lateral control, an efficient strategy of intelligent vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. The sliding mode switching function is regarded as the control objective, to ensure the stability of the steering wheel rotation. Simulation results show that the lateral controller can guarantee high path-tracking accuracy and strong robustness for the change of model parameters.

  15. The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters

    Directory of Open Access Journals (Sweden)

    Yancai Xiao

    2016-05-01

    Full Text Available In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD wind turbines, this paper proposes a fuzzy proportional integral (PI controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

  16. Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers

    Directory of Open Access Journals (Sweden)

    YUCEL, M.

    2017-02-01

    Full Text Available Erbium-doped fiber amplifiers (EDFA must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM and dense WDM (DWDM applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC is designed based on the adaptive neuro-fuzzy inference system (ANFIS for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.

  17. Application of a PID controller based on fuzzy logic to reduce variations in the control parameters in PWR reactors

    Energy Technology Data Exchange (ETDEWEB)

    Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira; Brito, Thiago Souza Pereira de; Afonso, Antonio Claudio Marques, E-mail: wagner@unicap.br, E-mail: cabol@ufpe.br, E-mail: afonsofisica@gmail.com, E-mail: thiago.brito86@yahoo.com.br [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Centro de Tecnologia e Geociencias. Departamento de Energia Nuclear; Cruz Filho, Antonio Jose da; Marques, Jose Antonio, E-mail: antonio.jscf@gmail.com, E-mail: jamarkss@uol.com.br [Universidade Catolica de Pernambuco (CCT/PUC-PE), Recife, PE (Brazil). Centro de Ciencias e Tecnologia; Teixeira, Marcello Goulart, E-mail: marcellogt@dcc.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil). Instituto de Matematica. Dept. de Matematica

    2013-07-01

    Nuclear reactors are in nature nonlinear systems and their parameters vary with time as a function of power level. These characteristics must be considered if large power variations occur in power plant operational regimes, such as in load-following conditions. A PWR reactor has a component called pressurizer, whose function is to supply the necessary high pressure for its operation and to contain pressure variations in the primary cooling system. The use of control systems capable of reducing fast variations of the operation variables and to maintain the stability of this system is of fundamental importance. The best-known controllers used in industrial control processes are proportional-integral-derivative (PID) controllers due to their simple structure and robust performance in a wide range of operating conditions. However, designing a fuzzy controller is seen to be a much less difficult task. Once a Fuzzy Logic controller is designed for a particular set of parameters of the nonlinear element, it yields satisfactory performance for a range of these parameters. The objective of this work is to develop fuzzy proportional-integral-derivative (fuzzy-PID) control strategies to control the level of water in the reactor. In the study of the pressurizer, several computer codes are used to simulate its dynamic behavior. At the fuzzy-PID control strategy, the fuzzy logic controller is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region. Thus the fuzzy logic controller tunes the gain of PID controller to adapt the model with changes in the water level of reactor. The simulation results showed a favorable performance with the use to fuzzy-PID controllers. (author)

  18. Some Improvements on Active Queue Management Mechanism Based on Adaptive Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Nguyen Kim Quoc

    2015-11-01

    Full Text Available Active queue management operates at network nodes to control the number of packets in the queue of nodes, by actively receiving packets when the queue is not full, removing packets when the queue is full or notifying bottlenecks even in the embryonic period of the bottlenecks due to to-be-full queue. In recent years, scientists have used fuzzy logic to improve queue management mechanisms. Overall, these improvements have used Mamdani fuzzy system with a fixed structure with triangular functions for input and output variables, so they do not adapt to the changing state of the network. We propose a adaptive fuzzy control (AFC model to improve the effectiveness of active queue management mechanisms.

  19. Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

    Science.gov (United States)

    Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed

    2012-12-01

    In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

  20. Control of PET Based On Fuzzy Logic for Power Quality Improvement

    Directory of Open Access Journals (Sweden)

    P Sravanthi

    2016-06-01

    Full Text Available During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial process, which do not lend of quantities data regarding the input-output relations. This paper presents a power electronic transformer with fuzzy controller. In the design process converters and high frequency transformers have been used. One matrix converter operates as AC/AC converter in power electronic transformer. The proposed AC/AC converter can generate desired output voltage from square input voltage. The main point of proposed PET is reduction of the stage and components of the three-part PETs. The reliability and power quality of distribution system can be significantly improved by using proposed PET. To verify the performance of the proposed PET, computer-aided simulations are carried out using MATLAB/SIMULINK

  1. Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems.

    Science.gov (United States)

    Harinath, Eranda; Mann, George K I

    2008-06-01

    This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.

  2. Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

    Directory of Open Access Journals (Sweden)

    Ahmed M. Othman

    2012-12-01

    Full Text Available In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV systems. Maximum power point tracking (MPPT plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O algorithm and is compared to a designed fuzzy logic controller (FLC. The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

  3. Controlling fractional order chaotic systems based on Takagi-Sugeno fuzzy model and adaptive adjustment mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Zheng Yongai, E-mail: zhengyongai@163.co [Department of Computer, Yangzhou University, Yangzhou, 225009 (China); Nian Yibei [School of Energy and Power Engineering, Yangzhou University, Yangzhou, 225009 (China); Wang Dejin [Department of Computer, Yangzhou University, Yangzhou, 225009 (China)

    2010-12-01

    In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Roessler system and fractional order Lorenz system.

  4. Study on fuzzy control of dynamic equalization based on state prediction

    Institute of Scientific and Technical Information of China (English)

    Cheng Shukang; Tian Likun; Kong Zhiguo

    2008-01-01

    A new method of using dynamic equalization technology to realize the maximum energy storage utilization was presented to overcome the influence of the disaccord among units of series super capacitor (SC) bank and ensure that the units could work safely. By considering in combination with the high specific power, low working voltage, wide voltage working range and nonlinear external characteristics, we present constant duty ratio pulse frequency modulation mode and fuzzy control method based on state prediction in the active equalization circuit and accomplish the software and hardware design for the equalization system. The simulation analysis and experiment results of constant current multi-cycle and variable current multi-cycle charge-discharge process verify the validity of the design.

  5. Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System

    Institute of Scientific and Technical Information of China (English)

    吴怀宇; 周兆英; 熊沈蜀

    2001-01-01

    A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``

  6. Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems

    Science.gov (United States)

    Lin, Tsung-Chih; Roopaei, Mehdi

    2010-12-01

    In this article, based on the adaptive interval type-2 fuzzy logic, by adjusting weights, centers and widths of proposed fuzzy neural network (FNN), the modeling errors can be eliminated for a class of SISO time-delay nonlinear systems. The proposed scheme has the advantage that can guarantee the H∞ tracking performance to attenuate the lumped uncertainties caused by the unmodelled dynamics, the approximation error and the external disturbances. Moreover, the stability analysis of the proposed control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded and arbitrary small attenuation level. The simulation results are demonstrated to show the effectiveness of the advocated design methodology.

  7. How to combine probabilistic and fuzzy uncertainties in fuzzy control

    Science.gov (United States)

    Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert

    1991-01-01

    Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.

  8. FPGA-based adaptive backstepping fuzzy control for a micro-positioning Scott-Russell mechanism

    Science.gov (United States)

    Fung, Rong-Fong; Weng, Ming-Hong; Kung, Ying-Shieh

    2009-11-01

    This paper utilizes the field programmable gate array (FPGA) and Nios II embedded processor technologies to design a controller IC for a micro-positioning Scott-Russell (SR) mechanism, which is driven by a piezoelectric actuator (PA) and its hysteresis phenomenon is described by Bouc-Wen hysteresis model. For the controller design, the adaptive backstepping fuzzy control (ABFC) method is developed to compensate the PA's hysteresis and achieve the motion tracking control. The fuzzy logic method (FLM) is utilized to find the best adaptation gain of the adaptation law and control gain of the stabilization controls. This ABFC controller method can improve the transient and asymptotic tracking performances, and make the SR mechanism keep good working performance when external disturbances is added in the control system. Finally, we successfully apply the system-on-a-programmable-chip (SoPC) technologies to develop the motion controller IC, and achieve the advantages of reduced space, high performance and low cost.

  9. Decentralized fuzzy control of multiple nonholonomic vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Driessen, B.J.; Feddema, J.T.; Kwok, K.S.

    1997-09-01

    This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Control of Flexible Joint Manipulator via Variable Structure Rule-Based Fuzzy Control and Chaos Anti-Control with Experimental Validation

    Directory of Open Access Journals (Sweden)

    Mojtaba Rostami Kandroodi

    2014-03-01

    Full Text Available This paper presents a variable structure rule-based fuzzy control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic anti-control. Based on Lyapunov stability theory for variable structure control and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. The fuzzy rules are directly constructed subject to a Lyapunov function obtained from variable structure surfaces such that the error dynamics of control problem satisfy stability in the Lyapunov sense. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

  12. 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.

  13. A Fuzzy Control Irrigation System For Cottonfield

    Science.gov (United States)

    Zhang, Jun; Zhao, Yandong; Wang, Yiming; Li, Jinping

    A fuzzy control irrigation system for cotton field is presented in this paper. The system is composed of host computer, slave computer controller, communication module, soil water sensors, valve controllers, and system software. A fuzzy control model is constructed to control the irrigation time and irrigation quantity for cotton filed. According to the water-required rules of different cotton growing periods, different irrigation strategies can be carried out automatically. This system had been used for precision irrigation of the cotton field in Langfang experimental farm of Soil and Fertilizer Institute, Chinese Academy of Agricultural Sciences in 2006. The results show that the fuzzy control irrigation system can improve cotton yield and save much water quantity than the irrigation system based on simple on-off control algorithm.

  14. Linear Design Approach to a Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1999-01-01

    A ball-balancer, basically an inverted pendulum problem, is stabilised by a linear controller. With certain design choices, a fuzzy controller is equivalent to a summation; thus it can replace the linear controller. It can be claimed, that the fuzzy controller performs at least as well...... as the linear controller, since the linear controller is contained in the fuzzy controller. The approach makes it somewhat easier to design a fuzzy controller....

  15. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    Science.gov (United States)

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  16. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    Science.gov (United States)

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  17. Path Following of an Underactuated AUV Based on Fuzzy Backstepping Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    Xiao Liang

    2016-06-01

    Full Text Available This paper addresses the path following problem of an underactuated autonomous underwater vehicle (AUV with the aim of dealing with parameter uncertainties and current disturbances. An adaptive robust control system was proposed by employing fuzzy logic, backstepping and sliding mode control theory. Fuzzy logic theory is adopted to approximate unknown system function, and the controller was designed by combining sliding mode control with backstepping thought. Firstly, the longitudinal speed was controlled, then the yaw angle was made as input of path following error to design the calm function and the change rate of path parameters. The controller stability was proved by Lyapunov stable theory. Simulation and outfield tests were conducted and the results showed that the controller is of excellent adaptability and robustness in the presence of parameter uncertainties and external disturbances. It is also shown to be able to avoid the chattering of AUV actuators.

  18. Path following of an Underactuated AUV Based on Fuzzy Backstepping Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    Xiao Liang

    2016-06-01

    Full Text Available This paper addresses the path following problem of an underactuated autonomous underwater vehicle (AUV with the aim of dealing with parameter uncertainties and current disturbances. An adaptive robust control system was proposed by employing fuzzy logic, backstepping and sliding mode control theory. Fuzzy logic theory is adopted to approximate unknown system function, and the controller was designed by combining sliding mode control with backstepping thought. Firstly, the longitudinal speed was controlled, then the yaw angle was made as input of path following error to design the calm function and the change rate of path parameters. The controller stability was proved by Lyapunov stable theory. Simulation and outfield tests were conducted and the results showed that the controller is of excellent adaptability and robustness in the presence of parameter uncertainties and external disturbances. It is also shown to be able to avoid the chattering of AUV actuators.

  19. Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

    Science.gov (United States)

    Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

    2017-09-14

    In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Robust Fuzzy Control for Doubly Fed Wind Power Systems with Variable Speed Based on Variable Structure Control Technique

    Directory of Open Access Journals (Sweden)

    Xizheng Zhang

    2014-01-01

    Full Text Available The design of a variable structure sliding-mode controller (SMC for a variable speed wind turbine with double-fed induction-generator, based on the fuzzy logic, is described in this paper. The purpose of this controller is to maximize the energy capture by operating the turbine at the optimal rotational speed as well as fast and stable generator response. The dynamics of both the turbine and the generator are modeled to exhibit their mechanical/electrical characteristics. Two global sliding-mode controllers, which eliminate the reaching phase of SMC and the sliding-mode motion starts from the beginning, are designed to guarantee the robust tracking of both the optimal blade-rotor speed and the reference generator torque/flux in the whole process, despite the parametric uncertainty and external disturbance. To reduce the adverse chattering effect of the conventional SMC, the adaptive fuzzy inference strategy is adopted to deduce the adjustable switch gain, instead of the fixed gains. Simulation results show that the proposed controller achieves global asymptotic tracking, satisfied torque/flux responses, and has better performance and higher utilization ratio of wind energy than the conventional feedback-linearization method.

  1. Research on Airborne Electro-Optical Tracking and Sighting System Based on Fuzzy PID and H∞ Control

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Airborne electro-optical tracking and sighting system is a three-degree-of-freedom angular position servo system which is influenced by multi-disturbance, and its control system consists of stabilizing and tracking components. Stabilizing control is applied to track angular velocity order and control multi-disturbance under airborne condition, and its robustness should be very good; tracking control is applied to compensate tracking error of angular position. A mathematical model is established by taking the control of yaw loop as example. H∞ stabilizing controller is designed by taking the advantage of H∞ control robustness and combining with Kalman filter. A fuzzy control is introduced in general PID control to design a decoupled fuzzy Smith estimating PID controller for tracking control. Simulation research shows that the control effect of airborne electro-optical tracking and sighting system based on fuzzy PID and H∞ control is good, especially when the model parameters change and the multi-disturbance exists, the system capability has little fall, but this system still can effectively track a target.

  2. Automobile active suspension system with fuzzy control

    Institute of Scientific and Technical Information of China (English)

    刘少军; 黄中华; 陈毅章

    2004-01-01

    A quarter-automobile active suspension model was proposed. High speed on/off solenoid valves were used as control valves and fuzzy control was chosen as control method . Based on force analyses of system parts, a mathematical model of the active suspension system was established and simplified by linearization method. Simulation study was conducted with Matlab and three scale coefficients of fuzzy controller (ke, kec, ku) were acquired. And an experimental device was designed and produced. The results indicate that the active suspension system can achieve better vibration isolation performance than passive suspension system, the displacement amplitude of automobile body can be reduced to 55%. Fuzzy control is an effective control method for active suspension system.

  3. Flatness-based adaptive fuzzy control of an autonomous submarine model

    Science.gov (United States)

    Rigatos, Gerasimos; Siano, Pierluigi; Raffo, Guilherme

    2015-12-01

    The article presents a differential flatness theory-based method for adaptive control of autonomous submarines. A proof is provided about the differential flatness properties of the submarine's model (having as state variables the vessel's depth and its pitch angle). This also means that all its state variables and its control inputs can be written as differential functions of the flat output. Making use of its differential flatness features, the submarine's dynamic model is transformed into the multivariable linear canonical (Brunovsky) form. In the transformed model, the control inputs consist of unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning rate for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Furthermore, with the use of Lyapunov stability analysis it is proven that an H-infinity tracking performance is succeeded for the feedback control loop. This implies enhanced robustness to model uncertainty and to external perturbations. Simulation experiments are carried out to further confirm the efficiency of the proposed adaptive fuzzy control scheme.

  4. Photovoltaic System Regulation Based on a PID Fuzzy Controller to Ensure a Fixed Settling Time

    Directory of Open Access Journals (Sweden)

    Paula A. Ortiz-Valencia

    2013-11-01

    Full Text Available The main objective of the controllers in photovoltaic systems (PV is to ensure the maximum extraction of the available power. Those controllers usually combine the action of a maximum power point tracking algorithm (MPPT with a voltage regulator, which has the function of rejecting disturbances at the panel terminals. Such controllers are commonly based on PI and PID structures, it requiring linearized models at an operating point. But, due to disturbances generated by the environment and the load, the operating point of the system changes drastically, which hinder to obtain the desired system performance. This paper proposes to regulate the PV system using a Fuzzy PID controller, which adapts to changes in solar irradiance and load oscillations. This characteristic guarantees a constant settling time, which is required to precisely define the period of the MPPT algorithm. In the case of classical linear controllers, the period of the MPPT algorithm is set to the worst case (longest period which generates additional power losses by slowing down the tracking of the optimal operating point. Therefore, the solution proposed in this paper improves the overall system efficiency. Finally, such a solution is validated through simulations in Matlab®.

  5. A Feasibility Study of Fuzzy FES Controller Based on Cycle-to-Cycle Control: An Experimental Test of Knee Extension Control

    Science.gov (United States)

    Watanabe, Takashi; Masuko, Tomoya; Arifin, Achmad; Yoshizawa, Makoto

    Functional Electrical Stimulation (FES) can be effective in assisting or restoring paralyzed motor functions. The purpose of this study is to examine experimentally the fuzzy controller based on cycle-to-cycle control for FES-induced gait. A basic experimental test was performed on controlling maximum knee extension angle with normal subjects. In most of control trials, the joint angle was controlled well compensating changes in muscle responses to electrical stimulation. The results show that the fuzzy controller would be practical in clinical applications of gait control by FES. An automatic parameter tuning would be required practically for quick responses in reaching the target and in compensating the change in muscle responses without causing oscillating responses.

  6. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  7. Unity power factor converter based on a fuzzy controller and predictive input current.

    Science.gov (United States)

    Bouafassa, Amar; Rahmani, Lazhar; Kessal, Abdelhalim; Babes, Badreddine

    2014-11-01

    This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant.

  8. Adaptive Fuzzy Control for CVT Vehicle

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    On the simple continuously variable transmission (CVT) driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

    Science.gov (United States)

    Wu, Zhengping; Wu, Hao

    With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

  11. Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

    Science.gov (United States)

    Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

    2017-09-01

    This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

  12. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    Science.gov (United States)

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  13. Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation

    Directory of Open Access Journals (Sweden)

    Huanqing Wang

    2014-01-01

    Full Text Available The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller structure is simpler and only one adaptive parameter needs to be updated online. Simulation results are used to illustrate the effectiveness of the proposed approach.

  14. A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation.

    Science.gov (United States)

    Fayek, H M; Elamvazuthi, I; Perumal, N; Venkatesh, B

    2014-09-01

    A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Improvement of Performance Range of Centrifugal Compressors Gas by Surge Line Modification Using Active Controller Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Pezhman Mohammadi

    2012-04-01

    Full Text Available In this work, surge of prevention is a critical problem in oil and gas industries, particularly when return gas flow or gas flow reduces in transportation of gas pipelines. This paper is illustrated new results about surge control of centrifugal compressors .surge phenomenon is flow unsteady state in compressors which causes damages seriously in compressor construction. Furthermore, it also demonstrates in comparison with anti surge control ،active surge control expands stability range.Active surge control which based on fuzzy logic،is the main idea that used in this investigation. Using fuzzy controller causes an improvement in compressor's condition and increase performance range of the compressor, in addition to prevention of any instability in compressor. The simulation results is also satisfactory.

  16. Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

    Institute of Scientific and Technical Information of China (English)

    谭冠政; 曾庆冬; 李文斌

    2004-01-01

    A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

  17. Neuro-fuzzy Control of Integrating Processes

    Directory of Open Access Journals (Sweden)

    Anna Vasičkaninová

    2011-11-01

    Full Text Available Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perceptionof natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plantswith time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control.Designed fuzzy controllers lead to better closed-loop control responses then classical PID controllers.

  18. On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems

    Directory of Open Access Journals (Sweden)

    M. S. N. Murty

    2008-04-01

    Full Text Available We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.

  19. Design of Immune-Algorithm-Based Adaptive Fuzzy Controllers for Active Suspension Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-04-01

    Full Text Available The aim of this paper is to integrate the artificial immune systems and adaptive fuzzy control for the automobile suspension system, which is regarded as a multiobjective optimization problem. Moreover, the fuzzy control rules and membership controls are then introduced for identification and memorization. It leads fast convergence in the search process. Afterwards, by using the diversity of the antibody group, trapping into local optimum can be avoided, and the system possesses a global search capacity and a faster local search for finding a global optimal solution. Experimental results show that the artificial immune system with the recognition and memory functions allows the system to rapidly converge and search for the global optimal approximate solutions.

  20. FUZZY GLOBAL SLIDING MODE CONTROL BASED ON GENETIC ALGORITHM AND ITS APPLICATION FOR FLIGHT SIMULATOR SERVO SYSTEM

    Institute of Scientific and Technical Information of China (English)

    LIU Jinkun; HE Yuzhu

    2007-01-01

    To alleviate the chattering problem, a new type of fuzzy global sliding mode controller (FGSMC) is presented. In this controller, the switching gain is estimated by fuzzy logic system based on the reachable conditions of sliding mode controller(SMC), and genetic algorithm (GA) is used to optimize scaling factor of the switching gain, thus the switch chattering of SMC can be alleviated.Moreover, global sliding mode is realized by designing an exponential dynamic sliding surface.Simulation and real-time application for flight simulator servo system with Lugre friction are given to indicate that the proposed controller can guarantee high robust performance all the time and can alleviate chattering phenomenon effectively.

  1. Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liyang Wang

    2016-01-01

    Full Text Available Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR- based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF. Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

  2. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks

    Directory of Open Access Journals (Sweden)

    Vicente Hernández Díaz

    2015-09-01

    Full Text Available The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT and Cyber-Physical Systems (CPS are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container, and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

  3. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks.

    Science.gov (United States)

    Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M

    2015-09-18

    The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

  4. A Laboratory Testbed for Embedded Fuzzy Control

    Science.gov (United States)

    Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

    2011-01-01

    This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses…

  5. A Laboratory Testbed for Embedded Fuzzy Control

    Science.gov (United States)

    Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

    2011-01-01

    This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses…

  6. Voice Coil Motor Position Control Based on Feed-forward Fuzzy PID

    Institute of Scientific and Technical Information of China (English)

    尹峰松

    2016-01-01

    Conventional PID algorithm is unable to track the response with high frequency,and has obvious overshoot in some voice coil motor practical applications.So,combined with the fuzzy PID control theory,we can obtain the precise control by the method.Meanwhile,through the feed-forward control,the performance of quick response and dynamic tracking can be improved.Thus,this control method not only maintains the excellent performance of the controller,but also improves the stability of the system.

  7. Fuzzy Control of Chaotic System with Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang

    2002-01-01

    A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.

  8. Design and Simulation of Dynamic Voltage Restorer based on Fuzzy Controller Optimized by ANFIS

    Directory of Open Access Journals (Sweden)

    Brahim Ferdi

    2014-03-01

    Full Text Available The fuzzy logic controller (FLC appears to be the unique solution when the process is too complex for analysis by conventional techniques or when the available information data are interpreted qualitatively, inexactly or with uncertainty. In literature, the proposed FLC in general consists of two inputs (error and derivative of error and one output. The number of membership functions is chosen in most cases to be five or seven regardless of the approach used for the design. In this paper, we propose Adaptive Neuro-Fuzzy Inference System (ANFIS approach to optimize the two inputs one output FLC with seven membership functions to one input one output FLC with three membership functions without compromising accuracy. The study is applied to control a Dynamic Voltage Restorer (DVR in voltage sag/swell mitigation. The results of simulation using MATLAB/SIMULINK show that the performance of the optimal FLC generated by ANFIS is comparable with the initial given FLC.

  9. Integrated fuzzy-PI controlled current source converter based D-STATCOM

    Directory of Open Access Journals (Sweden)

    M. Deben Singh

    2016-12-01

    Full Text Available The distribution static synchronous compensator (D-STATCOM is a shunt-connected current injection custom power (CP device that can be used for improving quality and reliability of the power supplied to electric consumers under dynamic conditions of the distribution system. The CP devices such as dynamic voltage restorer and unified power quality conditioner including D-STATCOM are mostly designed and implemented using voltage source converter topology. The performance of D-STATCOM can be improved, if it is realized by using a current source converter topology which can generate a controllable current directly at its output terminals. Although the current source converter (CSC topology offers many promising advantageous features, not much research publications with CSC-based approach in the field of CP devices has been reported over the last one decade. This paper presents a novel method of realizing a D-STATCOM using CSC topology with an integrated fuzzy-PI controller for not only mitigating voltage sag but also capable of maintaining improved DC-link voltage profile. The model has been simulated in the MATLAB/SIMULINK environment. The simulation results under steady state and dynamic load perturbation provide excellent output characteristics that support the validity of the proposed model.

  10. Implementation of a Small Type DC Microgrid Based on Fuzzy Control and Dynamic Programming

    Directory of Open Access Journals (Sweden)

    Chin-Hsing Cheng

    2016-09-01

    Full Text Available A DC microgrid (DC-MG is a novel power system that uses DC distribution in order to provide high quality power. The study system is made by a photovoltaic array (PV, a wind generator (WG, a fuel cell (FC, and an energy storage system (ESS to establish a small type DC microgrid, with the bus being established by DC/DC converters with fuzzy controllers. An overall power dispatch was designed for the proposed system to distribute the power flows among the different energy sources and the storage unit in the system in order to satisfy the load requirements throughout an entire 24-h period. The structure of a power supervisor based on an optimal power dispatch algorithm is here proposed. Optimization was performed using dynamic programming (DP. In this paper, a system configuration of a DC microgrid is analyzed in different scenarios to show the efficacy of the control for all devices for the variable weather conditions with different DC loads. Thus, the voltage level and the power flow of the system are shown for different load conditions.

  11. Variable universe adaptive fuzzy control on the quadruple inverted pendulum

    Institute of Scientific and Technical Information of China (English)

    LI; Hongxing(

    2002-01-01

    [1]Magana,M.E.,Fuzzy-logic control of an inverted pendulum with vision feedback,IEEE Transactions on Education,1998,41(2):165.[2]Chen,C.S.,Chen,W.L.,Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system,IEEE Transactions on Industrial Electronics,1998,45(2):297.[3]Cheng,F.Y.,Zhong,G.M.,Li,Y.S.et al.,Fuzzy control of a double-inverted pendulum,Fuzzy Sets and System,1996,79(3):315-321.[4]Zhang,H.M.,Ma,X.W.,Xu,W.et al.,Design fuzzy controllers complex systems with an application to 3-stage inverted pendulums,Information Sciences,1993,72:271.[5]Zhang,M.L.,Hao,J.K.,Hei,W.D.,Personification intelligence control and triple inverted pendulum,Journal of Aeronautics (in Chinese),1995,16(4):654.[6]Li,H.X.,To see the success of fuzzy logic from mathematical essence of fuzzy control,Fuzzy Systems and Mathematics (in Chinese),1995,9(4):1-14.[7]Li,H.X.,Mathematical essence of fuzzy controls and design of a kind of high precision fuzzy controllers,Control Theory and Application (in Chinese),1997,14(6):868.[8]Li,H.X.,Adaptive fuzzy controllers based on variable universe,Science in China,Ser.E,1999,42(1):10.[9]Li,H.X.,Interpolation mechanism of fuzzy control,Science in China,Ser.E,1998,41(3):312.[10]Li,H.X.,The equivalence between fuzzy logic systems and feedforward neural networks,Science in China,Ser.E,2000,43(1):42.

  12. Study of BLDCM Control System Based on Fuzzy-PI%基于Fuzzy-PI的BLDCM控制系统的研究

    Institute of Scientific and Technical Information of China (English)

    晟广旭; 章松发; 宋森森; 李珍国

    2011-01-01

    无刷直流电机(BLDCM)以其效率高、噪声低、易维护等优点在工业控制的各个领域得到越来越广泛的应用。但BLDCM是一个高阶、强耦合的系统,有的领域对控制系统的要求越来越高,采用传统PI控制方法,难以达到理想的控制效果。因此,本文以数字信号处理器(DSP2812)为核心建立控制平台,主要研究的是基于Fuzzy-PI控制的BLDCM控制系统。介绍了无刷直流电机的基本组成、基本运行原理及其调速方法,给出了无刷直流电机的数学模型。在MATLAB中,利用M文件实现BLDCM传统电流环、转速环和模糊PI转速环控制系统的仿真。仿真结果表明,系统超调量小,具有较高的控制精度和较好的鲁棒性,从而验证所用控制方法的可行性。%Brushless dc motor (BLDCM) with its high efficiency, low noise, easy maintenance in industrial control of each areas to be more and more widely. But BLDCM is a high order, strong coupling, and now the system of control system demand is higher and higher, used the traditional PI control method, it is difficult to achieve the ideal control effect. Therefore, in this paper, the digital signal processor (DSP2812) as the core establish control platform, the main research is based on Fuzzy PI control-of BLDCM control system. Introduction of brushless dc motor basic composition, basic operation principle and its control method are given, brushless dc motor of the mathematics model. In MATLAB, use M-file realize traditional current loop, rotational speed BLDCM ring and fuzzy PI speed loop control system simulation. The simulation results show that the system is small overshoot, has the high control accuracy and good robustness, which validated the control the feasibility of the method.

  13. Genetic algorithm-based fuzzy-PID control methodologies for enhancement of energy efficiency of a dynamic energy system

    Energy Technology Data Exchange (ETDEWEB)

    Jahedi, G. [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave, Tehran (Iran, Islamic Republic of); Ardehali, M.M., E-mail: ardehali@aut.ac.i [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave, Tehran (Iran, Islamic Republic of)

    2011-01-15

    The simplicity in coding the heuristic judgment of experienced operator by means of fuzzy logic can be exploited for enhancement of energy efficiency. Fuzzy logic has been used as an effective tool for scheduling conventional PID controllers gain coefficients (F-PID). However, to search for the most desirable fuzzy system characteristics that allow for best performance of the energy system with minimum energy input, optimization techniques such as genetic algorithm (GA) could be utilized and the control methodology is identified as GA-based F-PID (GA-F-PID). The objective of this study is to examine the performance of PID, F-PID, and GA-F-PID controllers for enhancement of energy efficiency of a dynamic energy system. The performance evaluation of the controllers is accomplished by means of two cost functions that are based on the quadratic forms of the energy input and deviation from a setpoint temperature, referred to as energy and comfort costs, respectively. The GA-F-PID controller is examined in two different forms, namely, global form and local form. For the global form, all possible combinations of fuzzy system characteristics in the search domain are explored by GA for finding the fittest chromosome for all discrete time intervals during the entire operation period. For the local form, however, GA is used in each discrete time interval to find the fittest chromosome for implementation. The results show that the global form GA-F-PID and local form GA-F-PID control methodologies, in comparison with PID controller, achieve higher energy efficiency by lowering energy costs by 51.2%, and 67.8%, respectively. Similarly, the comfort costs for deviation from setpoint are enhanced by 54.4%, and 62.4%, respectively. It is determined that GA-F-PID performs better in local from than global form.

  14. Comparing the Power System Stabilizer Based on Sliding Mode Control with the Fuzzy Power System Stabilizer for Single Machine Infinite Bus System (SMIB

    Directory of Open Access Journals (Sweden)

    Atabak Kolabi

    2013-01-01

    Full Text Available This study compares the power system stabilizer based on sliding mode control with the fuzzy power system stabilizer for Single Machine Infinite Bus System (SMIB. Using the sliding mode control, a range is obtained for the changes in system parameters; and a stabilizer is designed to have a proper performance in this wide range. The purpose of designing the sliding mode stabilizer and fuzzy stabilizer is the increased stability and improving the dynamic response of the single machine system connected to the infinite bus in different working conditions. In this study, simulation results are compared in case of conventional PSS, no PSS, PSS based on sliding mode control and PSS based fuzzy logic. The results of simulations performed on the model of nonlinear system shows good performance of sliding mode controller and the Fuzzy controller. SMIB system was selected because of its simple structure, which is very useful in understanding the effects and implications of the PSS.

  15. Fuzzy Vibration Control of a Smart Plate

    Science.gov (United States)

    Muradova, Aliki D.; Stavroulakis, Georgios E.

    2013-04-01

    Vibration suppression of a smart thin elastic rectangular plate is considered. The plate is subjected to external disturbances and generalized control forces, produced, for instance, by electromechanical feedback. A nonlinear controller is designed, based on fuzzy inference. The initial-boundary value problem is spatially discretized by means of the time spectral method. The implicit Newmark-beta method is employed for time integration. Two numerical algorithms are proposed. The techniques have been implemented within MATLAB with the use of the Fuzzy Logic Toolbox. Representative numerical results are given.

  16. Analysis of one dimensional and two dimensional fuzzy controllers

    Institute of Scientific and Technical Information of China (English)

    Ban Xiaojun; Gao Xiaozhi; Huang Xianlin; Wu Tianbao

    2006-01-01

    The analytical structures and the corresponding mathematical properties of the one dimensional and two dimensional fuzzy controllers are first investigated in detail.The nature of these two kinds of fuzzy controllers is next probed from the perspective of control engineering. For the one dimensional fuzzy controller, it is concluded that this controller is a combination of a saturation element and a nonlinear proportional controller, and the system that employs the one dimensional fuzzy controller is the combination of an open-loop control system and a closedloop control system. For the latter case, it is concluded that it is a hybrid controller, which comprises the saturation part, zero-output part, nonlinear derivative part, nonlinear proportional part, as well as nonlinear proportional-derivative part, and the two dimensional fuzzy controller-based control system is a loop-varying system with varying number of control loops.

  17. Application of Fuzzy Self-Optimizing Control Based on Differential Evolution Algorithm for the Ratio of Wind to Coal Adjustment of Boiler in the Thermal Power Plant

    National Research Council Canada - National Science Library

    Ting Hou; Liping Zhang; Yuchen Chen

    2014-01-01

    .... In this paper, a kind of fuzzy self-optimizing control based on differential evolution algorithm is proposed, which applied in the power plant boiler system, the boiler combustion efficiency has been...

  18. Fuzzy logic control of telerobot manipulators

    Science.gov (United States)

    Franke, Ernest A.; Nedungadi, Ashok

    1992-01-01

    Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

  19. Stability analysis and H(infinity) controller design of discrete-time fuzzy large-scale systems based on piecewise Lyapunov functions.

    Science.gov (United States)

    Zhang, Hongbin; Feng, Gang

    2008-10-01

    This paper is concerned with stability analysis and H(infinity) decentralized control of discrete-time fuzzy large-scale systems based on piecewise Lyapunov functions. The fuzzy large-scale systems consist of J interconnected discrete-time Takagi-Sugeno (T-S) fuzzy subsystems, and the stability analysis is based on Lyapunov functions that are piecewise quadratic. It is shown that the stability of the discrete-time fuzzy large-scale systems can be established if a piecewise quadratic Lyapunov function can be constructed, and moreover, the function can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. The H(infinity) controllers are also designed by solving a set of LMIs based on these powerful piecewise quadratic Lyapunov functions. It is demonstrated via numerical examples that the stability and controller synthesis results based on the piecewise quadratic Lyapunov functions are less conservative than those based on the common quadratic Lyapunov functions.

  20. Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation

    Science.gov (United States)

    Ullah, Muhammed Zafar

    Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems. One of the major problems in portable or electric vehicle world is secondary cell charging, which shows non-linear characteristics. Portable-electronic equipment, such as notebook computers, cordless and cellular telephones and cordless-electric lawn tools use batteries in increasing numbers. These consumers demand fast charging times, increased battery lifetime and fuel gauge capabilities. All of these demands require that the state-of-charge within a battery be known. Charging secondary cells Fast is a problem, which is difficult to solve using conventional techniques. Charge control is important in fast charging, preventing overcharging and improving battery life. This research work provides a quick and reliable approach to charger design using Neural-Fuzzy technology, which learns the exact battery charging characteristics. Neural-Fuzzy technology is an intelligent combination of neural net with fuzzy logic that learns system behavior by using system input-output data rather than mathematical modeling. The primary objective of this research is to improve the secondary cell charging algorithm and to have faster charging time based on neural network and fuzzy logic technique. Also a new architecture of a controller will be developed for implementing the charging algorithm for the secondary battery.

  1. A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

    Directory of Open Access Journals (Sweden)

    Xian-xia Zhang

    2012-01-01

    Full Text Available Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design.

  2. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

    Science.gov (United States)

    Jani, Yashvant

    1992-01-01

    The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

  3. Intelligent Control for USV Based on Improved Elman Neural Network with TSK Fuzzy

    Directory of Open Access Journals (Sweden)

    Shang-Jen Chuang

    2014-01-01

    Full Text Available In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN algorithms. Takagi-Sugeno-Kang (TSK fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose.

  4. Fuzzy logic controllers on chip

    OpenAIRE

    Acosta, Nelson; Simonelli, Daniel Horacio

    2002-01-01

    This paper analyzes a fuzzy logic (FL) oriented instruction set (micro)controller and their implementations on FIPSOC1. VHDL code is synthesized using a small portion of FIPSOC FPGA2. This circuits are used from the mP8051 FIPSOC built-in microcontroller to provide efficient arithmetic operations such as multipliers, dividers, minimums and maximums.

  5. INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER

    Institute of Scientific and Technical Information of China (English)

    ZHU Liye; FANG Yuan; ZHANG Weidong

    2008-01-01

    According to a type of normal nonlinear system, an indirect adaptive fuzzy (IAF) controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy (IAAF) controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system's global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process's shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.

  6. Methodology for allocation of remotely controlled switches in distribution networks based on a fuzzy multi-criteria decision making algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Bernardon, D.P.; Sperandio, M.; Garcia, V.J.; Russi, J. [UNIPAMPA - Federal University of Pampa (Brazil); Canha, L.N.; Abaide, A.R. [UFSM - Federal University of Santa Maria (Brazil); Daza, E.F.B. [AES Sul (Brazil)

    2011-02-15

    Continuity in power supply for the consumers is a permanent concern from the utilities, pursued with the development of technological solutions in order to improve the performance of network restoration conditions. Using remotely controlled switches corresponds to one possible approach to reach such an improvement and giving some convenient remote resources such as the fault detect, isolation and transfer loads. This paper presents a methodology implemented in a computer programming language for allocation these devices in electric distribution systems based on multi-criteria fuzzy analysis. The main contributions are focus on considering the impact of installing remote-controlled switches in the reliability indexes and algorithm of fuzzy multi-criteria decision making for the switches allocation. The effectiveness of the proposed algorithm is demonstrated with case studies involving actual systems of the AES Sul utility located in the south of Brazil. (author)

  7. Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

    Directory of Open Access Journals (Sweden)

    Xian-Xia Zhang

    2013-01-01

    Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

  8. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system

    Science.gov (United States)

    Li, Yezi; Xiao, Cheng; Sun, Jinhao

    2013-03-01

    PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.

  9. 基于模糊控制的自适应光学校正技术∗%Adaptive optics correction technique based on fuzzy control

    Institute of Scientific and Technical Information of China (English)

    刘章文; 李正东; 周志强; 袁学文

    2016-01-01

    In an adaptive optics system, proportion-integration-differentiation (PID) controller is widely used for correcting wave front, but the controller is strictly dependent on the response model of deformable mirror. In this paper, a novel wave front correction method is proposed. The method, combining fuzzy control and PID control, does not depend on the response model of the deformable mirror. Based on rapid wave front reconstruction, the wave front evaluation indexes, extracted from the reconstructed wave front, are employed for the input of fuzzy controller and PID controller. Thus, the model response matrix of deformable mirror is not required. Each actuator of deformable mirror corresponds to an independent fuzzy PID controller. By designing the fuzzy controller, including fuzzy rule base selection and fuzzy reasoning, the three parameters of PID controller, the proportional kp, the integral ki and the differential kd, are adjusted automatically. A high rapid DSP hardware platform is constructed to verify the method. Test results show that the method can be used to correct the diffraction limit multiplication factorβ of the light spot from 10–12 to 3–4, which is basically the same as the traditional PID control, but its stability is better. Because the model does not need to calibrate the deformable mirror, the installation of the deformable mirror is easier.

  10. Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications

    Directory of Open Access Journals (Sweden)

    Stefan Preitl

    2006-07-01

    Full Text Available The paper deals with both theoretical and application aspects concerningIterative Feedback Tuning (IFT algorithms in the design of a class of fuzzy controlsystems employing Mamdani-type PI-fuzzy controllers. The presentation is focused on twodegree-of-freedom fuzzy control system structures resulting in one design method. Thestability analysis approach based on Popov’s hyperstability theory solves the convergenceproblems associated to IFT algorithms. The suggested design method is validated by realtimeexperimental results for a fuzzy controlled nonlinear DC drive-type laboratoryequipment.

  11. Rapid Modelling and Grinding of Workpieces’ Inner-surface by Robot with Impedance Model Based Fuzzy Force Control Algorithm

    Directory of Open Access Journals (Sweden)

    Wang Fei

    2017-01-01

    Full Text Available To achieve rapid automatic grinding of workpieces’ inner-surface by industrial robot, a rapid translational detection strategy of workpieces’ inner-surface and fuzzy force control algorithm of grinding are proposed in this paper. The rapid translational detection strategy introduces a way to establish an inner-surface’s model quickly by recording key points of the axial section contour which reflects big curvature changes of the contour. The established model is feasible but imprecision. The force control algorithm is based on impedance model. To promote adaptability to the imprecision of the established inner-surface’s model, a fuzzy adjusting strategy is introduced in the force control algorithm. By adopting an adjusting factor, which determined by force response and a fuzzy logic, the strategy can adjust the reference trajectory of impedance model in time. Taking advantage of proposed detection and force control method, grinding experiments shows that the contact normal force maintains approximately constant, the relative mean error is within 6.5%, and the material removal thickness of the inner-surface is approximately consistent. The proposed strategy’s feasibility is verified.

  12. Fuzzy Mathematics for Raw Silk Size Control

    Institute of Scientific and Technical Information of China (English)

    HU Zheng-yu; YU Hai-feng; GU Ping

    2008-01-01

    With photographing and experiments,this paper divides the cocoon layers into three categories according to their colors,establishes three-color membership function based on fuzzy mathemtics,constructs fuzzy sets which satisfy the range of size contrd by using the ordinary set and attached fiequency of three color cocoons combination,then achieves the ordinary sets of range of size control by choosing λ-cut.Under these ordinary sets,each end does duality relative level,then sets up relative matrix and overall sequence and finds the membership function to iudge whether the size cmtrol is normal.

  13. Performance Comparison of Conventional Controller with Fuzzy Logic Controller using Chopper Circuit and Fuzzy Tuned PID Controller

    Directory of Open Access Journals (Sweden)

    Mohammed Shoeb Mohiuddin

    2014-09-01

    Full Text Available It is often difficult to develop an accurate mathematical model of DC motor due to unknown load variation, unknown and unavoidable parameter variations or nonlinearities due to saturation temperature variations and system disturbances. Fuzzy logic application can handle such nonlinearities so that the controller design is fundamentally robust which is not possible in conventional controllers. The knowledge base of a fuzzy logic controller (FLC encapsulates expert knowledge and consists of the Data base (membership functions and Rule-Base of the controller. Optimization of both these knowledge base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater numbers of inputs, more formal methods of knowledge base optimization are required. In this work, we study the challenging task of controlling the speed of DC motor. The feasibility of such controller design is evaluated by simulation in the MATLAB/Simulink environment. In this study Conventional Proportional Integral Derivative controller, Fuzzy logic controller using a chopper circuit and Fuzzy tuned PID controller are analyzed and compared. Simulation software like MATLAB with Simulink has been used for modeling and simulation purpose. The performance comparison of conventional controller with Fuzzy logic controller using chopper circuit and Fuzzy tuned PID controller has been done in terms of several performance measures Such as Settling time, Rise time and Overshoot.

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

    OpenAIRE

    2015-01-01

    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 netwo...

  15. Image matching navigation based on fuzzy information

    Institute of Scientific and Technical Information of China (English)

    田玉龙; 吴伟仁; 田金文; 柳健

    2003-01-01

    In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.

  16. Fuzzy chaos control for vehicle lateral dynamics based on active suspension system

    Science.gov (United States)

    Huang, Chen; Chen, Long; Jiang, Haobin; Yuan, Chaochun; Xia, Tian

    2014-07-01

    The existing research of the active suspension system (ASS) mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.

  17. Real Time Implementation of PID and Fuzzy PD Controllers for DC-Servo Motor Based on Lab View Environment

    Directory of Open Access Journals (Sweden)

    Safaa M. Z. Al-Ubaidi

    2012-06-01

    Full Text Available This paper presents an implementation of conventional PID (CPID controller using Ziegler-Nichols rules and fuzzy PD (FPD controller for position servo motor control based on Lab View (Laboratory Virtual Instrument Engineering Workbench Environment through Data Acquisition (DAQ Device PCI- 6521 of National Instrument's and Data Acquisition Accessory Board Model (CB-68LP.CPID controller is perhaps the most well-known and most widely used in industrial applications. However, it has been known that CPID controller generally don’t work well for non-linear systems, higher order and time-delayed linear system and particularly complex and vague system. To overcome these difficulties, this paper proposes to use the FPD controller for a servo motor system instead of CPID. The parameters of servo motor used are completely unknown. The FPD structure has two-input single-output and fairly similar characteristic to its conventional counterpart and provides good performance. Simple rules base are used for FPD (nine rules only. Performance evaluation was carried out via a comparison study for the proposed control scheme and other existing control scheme, such as CPID controller. The critical point for this experiment on position system is a steady state error and settling time.  The performance showing that the FPD has less settling time and zero steady state error over its CPID. The algorithms of FPD and CPID controllers are implemented using PID, Fuzzy Logic and simulation toolkits of the Lab View environment.

  18. Fuzzy Approximation-Based Global Pinning Synchronization Control of Uncertain Complex Dynamical Networks.

    Science.gov (United States)

    Li, Xiao-Jian; Yang, Guang-Hong

    2017-04-01

    This paper is concerned with the global pinning synchronization problem of uncertain complex dynamical networks with communication constraints. First, an adaptive fuzzy controller is designed within a given compact set. In addition, a robust controller is introduced outside the compact set to pull back the system states. Then, a new pinning control scheme is given such that the global synchronization can be ensured. Moreover, via the Lyapunov theory and graph theory, the synchronization errors are proved to be asymptotically convergent. Especially, in an uncertainty-free environment, the proposed control scheme includes two easy-to-implement pinning control strategies as special cases, which improve the existing results from the view point of reducing the number of feedback controllers. Finally, two simulation examples are provided to validate the theoretical results.

  19. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    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.

  20. A Literature Review on the Fuzzy Control Chart; Classifications & Analysis

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Zavvar Sabegh

    2014-08-01

    Full Text Available Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of the control chart under a fuzzy environment with proposing several classifications and analysis. Moreover, our research considered both attribute and variable control chart by analyzing the related researches based on the content analysis method, to classify past and current developments in the fuzzy control chart. This work has included a distribution of articles according to the journal, the case studies related to fuzzy control chart, the percentage of types of fuzzy control charts used in the literature, performance evaluation of the fuzzy control chart and summary of key points of each review paper. Finally, this paper discusses some future research direction and our overviews. The results of this study can help researchers become familiar with well-known journals, fuzzy control charts used in sample case studies, and to extract key points of each paper in minimum time.

  1. Lyapunov Based-Distributed Fuzzy-Sliding Mode Control for Building Integrated-DC Microgrid with Plug-in Electric Vehicle

    DEFF Research Database (Denmark)

    Ghiasi, Mohammad Iman; Aliakbar Golkar, Masoud; Hajizadeh, Amin

    2017-01-01

    This paper presents a distributed control strategy based on Fuzzy-Sliding Mode Control (FSMC) for power control of an infrastructure integrated with a DC-Microgrid, which includes photovoltaic, fuel cell and energy storage systems with Plug-in Electric Vehicles (PEVs). In order to implement...... the proposed control strategy, first a general nonlinear modeling of a DC-Microgrid based on related DC-DC converters to each DC power sources is introduced. Secondly, a power management strategy based on fuzzy control for regulating the power flow between the hybrid DC sources, PEVs is proposed. Third...

  2. Fuzzy multinomial control chart and its application

    Science.gov (United States)

    Wibawati, Mashuri, Muhammad; Purhadi, Irhamah

    2016-03-01

    Control chart is a technique that has been used widely in industry and services. P chart is the simplest control chart. In this chart, item is classified into two categories as either conforming and non conforming. This chart based on binomial distribution. In practice, each item can classify in more than two categories such as very bad, bad, good and very good. Then to monitor the process we used multinomial p control chart. However, if the classification is an element of vagueness, the fuzzy multinomial control chart (FM) is more appropriately used. Control limit of FM chart obtained multinomial distribution and the degree of membership using fuzzy trianguler are 0, 0.25. 0.5 and 1. This chart will be applied to the data glass and will compare with multinomial p control chart.

  3. 基于Matlab模糊控制器HDL代码的自动生成%Fuzzy Controller HDL Code Automatically Generated Based on Matlab

    Institute of Scientific and Technical Information of China (English)

    诸葛俊贵

    2012-01-01

    This article, taking the Water Level Control in a Tank as an example, proposes a fuzzy controller automatic HDL code generation method based on Matlab. The generated code can be ported to the FPGA control system. The method is divided into four steps : ( 1 ) The fuzzy controller is designed using Fuzzy Logic Toolbox in the Matlab. (2) The fuzzy controller is transformed into the form of Lookup Table. (3) The controller Lookup table is implemented with the state machine. (4) The fuzzy controller implementation with state machine was translated into HDL code by HDL Coder.%以水箱液位控制为例,提出了一种基于Maflab的模糊控制器HDL代码自动生成方法,生成的代码可以移植到FPGA控制系统上。该方法分4个步骤:(1)利用Maflab的FuzzyLogic工具箱设计模糊控制器。(2)将模糊控制器转换为LookupTable的形式。(3)将制作好的控制器查找表用状态机实现。(4)用HDLCoder将状态机实现的模糊控制器翻译成HDL代码。

  4. Adaptive fuzzy tracking control for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

    Directory of Open Access Journals (Sweden)

    Peng Fei Wang

    2016-10-01

    Full Text Available The design of an adaptive fuzzy tracking control for a flexible air-breathing hypersonic vehicle with actuator constraints is discussed. Based on functional decomposition methodology, velocity and altitude controllers are designed. Fuzzy logic systems are applied to approximate the lumped uncertainty of each subsystem of air-breathing hypersonic vehicle model. Every controllers contain only one adaptive parameter that needs to be updated online with a minimal-learning-parameter scheme. The back-stepping design is not demanded by converting the altitude subsystem into the normal output-feedback formulation, which predigests the design of a controller. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the inputs are saturated. Finally, reference trajectory tracking simulation shows the effectiveness of the proposed method in its application to air-breathing hypersonic vehicle control.

  5. Type-2 Fuzzy Logic in Intelligent Control Applications

    CERN Document Server

    Castillo, Oscar

    2012-01-01

    We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters 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 can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. Th...

  6. Free-flying dynamics and control of an astronaut assistant robot based on fuzzy sliding mode algorithm

    Science.gov (United States)

    Gao, Qing; Liu, Jinguo; Tian, Tongtong; Li, Yangmin

    2017-09-01

    Space robots can perform some tasks in harsh environment as assistants of astronauts or substitutions of astronauts. Taking the limited working time and the arduous task of the astronauts in the space station into account, an astronaut assistant robot (AAR-2) applied in the space station is proposed and designed in this paper. The AAR-2 is achieved with some improvements on the basis of AAR-1 which was designed before. It can exploit its position and attitude sensors and control system to free flight or hover in the space cabin. And it also has a definite environmental awareness and artificial intelligence to complete some specified tasks under the control of astronauts or autonomously. In this paper, it mainly analyzes and controls the 6-DOF motion of the AAR-2. Firstly, the system configuration of AAR-2 is specifically described, and the movement principles are analyzed. Secondly, according to the physical model of the AAR-2, the Newton - Euler equation is applied in the preparation of space dynamics model of 6-DOF motion. Then, according to the mathematical model's characteristics which are nonlinear and strong coupling, a dual closed loop position and attitude controller based on fuzzy sliding mode control is proposed and designed. Finally, simulation experiments are appropriate to provide for AAR-2 control system by using Matlab/Simulink. From the simulation results it can be observed that the designed fuzzy sliding mode controller can control the 6-DOF motion of AAR-2 quickly and precisely.

  7. An Interval Type-2 Fuzzy System with a Species-Based Hybrid Algorithm for Nonlinear System Control Design

    Directory of Open Access Journals (Sweden)

    Chung-Ta Li

    2014-01-01

    Full Text Available We propose a species-based hybrid of the electromagnetism-like mechanism (EM and back-propagation algorithms (SEMBP for an interval type-2 fuzzy neural system with asymmetric membership functions (AIT2FNS design. The interval type-2 asymmetric fuzzy membership functions (IT2 AFMFs and the TSK-type consequent part are adopted to implement the network structure in AIT2FNS. In addition, the type reduction procedure is integrated into an adaptive network structure to reduce computational complexity. Hence, the AIT2FNS can enhance the approximation accuracy effectively by using less fuzzy rules. The AIT2FNS is trained by the SEMBP algorithm, which contains the steps of uniform initialization, species determination, local search, total force calculation, movement, and evaluation. It combines the advantages of EM and back-propagation (BP algorithms to attain a faster convergence and a lower computational complexity. The proposed SEMBP algorithm adopts the uniform method (which evenly scatters solution agents over the feasible solution region and the species technique to improve the algorithm’s ability to find the global optimum. Finally, two illustrative examples of nonlinear systems control are presented to demonstrate the performance and the effectiveness of the proposed AIT2FNS with the SEMBP algorithm.

  8. Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

    Institute of Scientific and Technical Information of China (English)

    Thang-Long MAI; Yaonan WANG

    2014-01-01

    In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion/force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomic-constraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

  9. Type-1 and Type-2 Fuzzy Logic and Sliding-Mode Based Speed Control of Direct Torque and Flux Control Induction Motor Drives - A Comparative Study

    Science.gov (United States)

    Ramesh, Tejavathu; Panda, A. K.; Kumar, S. Shiva

    2013-08-01

    In this research study, the performance of direct torque and flux control induction motor drive (IMD) is presented using five different speed control techniques. The performance of IMD mainly depends on the design of speed controller. The PI speed controller requires precise mathematical model, continuous and appropriate gain values. Therefore, adaptive control based speed controller is desirable to achieve high-performance drive. The sliding-mode speed controller (SMSC) is developed to achieve continuous control of motor speed and torque. Furthermore, the type-1 fuzzy logic speed controller (T1FLSC), type-1 fuzzy SMSC and a new type-2 fuzzy logic speed controller are designed to obtain high performance, dynamic tracking behaviour, speed accuracy and also robustness to parameter variations. The performance of each control technique has been tested for its robustness to parameter uncertainties and load disturbances. The detailed comparison of different control schemes are carried out in a MATALB/Simulink environment at different speed operating conditions, such as, forward and reversal motoring under no-load, load and sudden change in speed.

  10. Simulation and experimental control of a 3-RPR parallel robot using optimal fuzzy controller and fast on/off solenoid valves based on the PWM wave.

    Science.gov (United States)

    Moezi, Seyed Alireza; Rafeeyan, Mansour; Zakeri, Ehsan; Zare, Amin

    2016-03-01

    In this paper, a robust optimal fuzzy controller based on the Pulse Width Modulation (PWM) technique is proposed to control a laboratory parallel robot using inexpensive on/off solenoid valves. The controller coefficients are determined using Modified Cuckoo Optimization Algorithm. The objective function of this method is considered such that the results show the position tracking by the robot with less force and more efficiency. Regarding the results of experimental tests, the control strategy with on/off valves indicates good performance such that the maximum value of RMS of error for a circular path with increasing force on the system is 3.1mm. Furthermore, the results show the superiority of the optimal fuzzy controller compared with optimal PID controller in tracking paths with different conditions and uncertainties.

  11. A kind of fuzzy control for chaotic systems

    Institute of Scientific and Technical Information of China (English)

    WANG Hong-wei; MA Guang-fu

    2007-01-01

    With a T-S fuzzy dynamic model approximating to a non-linear system, the nonlinear system can be decomposed into some local linear models. A variable structure controller based on Lyapunov theories is designed to guarantee the global stability of the T-S fuzzy model. The controlling problems of a nonlinear system can be solved by means of consisting of linear system variable structure control and fuzzy control. The validity of the control method based on the simulating result of two kinds of chaotic systems is shown here.

  12. Optimal Power Flow Using Adaptive Fuzzy Logic Controllers

    Directory of Open Access Journals (Sweden)

    Abdullah M. Abusorrah

    2013-01-01

    Full Text Available This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs devices, using adaptive fuzzy logic controller (AFLC driven by adaptive fuzzy sets (AFSs. The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC and the setting of their control parameters (QSVC are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC, driven by a fixed fuzzy set (FFS. Simulation studies were carried out and validated on the standard IEEE 30-bus test system.

  13. 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.

  14. Fuzzy Control in the Process Industry

    DEFF Research Database (Denmark)

    Jantzen, Jan; Verbruggen, Henk; Østergaard, Jens-Jørgen

    1999-01-01

    Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. Simple fuzzy controllers can...

  15. CRUISE FUZZY CONTROL FOR AUTOMOBILE WITH CVT

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    To develop cruise control system of an automobile with the metal pushing V-belt type CVT, the dynamic model of automobile travelling longitudinally is established, and the fuzzy controller of control system is designed. Considering uncertainty system parameter and exterior resistance disturbances, the stability of controller is investigated by simulating. The results of its simulation show that the fuzzy controller designed has practicability.

  16. Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

    Directory of Open Access Journals (Sweden)

    Yu-Chu Tian

    2007-12-01

    Full Text Available Wireless sensor/actuator networks (WSANs are emerging rapidly as a newgeneration of sensor networks. Despite intensive research in wireless sensor networks(WSNs, limited work has been found in the open literature in the field of WSANs. Inparticular, quality-of-service (QoS management in WSANs remains an important issue yetto be investigated. As an attempt in this direction, this paper develops a fuzzy logic controlbased QoS management (FLC-QM scheme for WSANs with constrained resources and indynamic and unpredictable environments. Taking advantage of the feedback controltechnology, this scheme deals with the impact of unpredictable changes in traffic load on theQoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adaptsampling period to the deadline miss ratio associated with data transmission from the sensorto the actuator. The deadline miss ratio is maintained at a pre-determined desired level sothat the required QoS can be achieved. The FLC-QM has the advantages of generality,scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANswith QoS support.

  17. Data fusion based on fuzzy measures

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Choquet integral based on fuzzy measure is a very popular data fusion approach. A major problem in applying the Choquet integral is how to determine a large number of fuzzy measures as the number of attributes increases. The λ-fuzzy measure proposed by Sugeno is a powerful method to resolve this problem. However, the modeling ability of the λ-fuzzy measure is too limited to satisfy actual requirements. In this paper, an extended λ-fuzzy measure is proposed using Shapley value index, and the limitation of the λ-fuzzy measure is significantly overcome under little additional computational loads. The extended fuzzy measure has stronger modeling power than the λ-fuzzy measure, straightforwardly representing interaction among attributes. We apply the extended fuzzy measure to an artificial data set and a real dataset in an iron-steel plant. The results verify the usefulness of the extended fuzzy measure compared with other main existing methods.

  18. Analysis of Aircraft Control Performance using a Fuzzy Rule Base Representation of the Cooper-Harper Aircraft Handling Quality Rating

    Science.gov (United States)

    Tseng, Chris; Gupta, Pramod; Schumann, Johann

    2006-01-01

    The Cooper-Harper rating of Aircraft Handling Qualities has been adopted as a standard for measuring the performance of aircraft since it was introduced in 1966. Aircraft performance, ability to control the aircraft, and the degree of pilot compensation needed are three major key factors used in deciding the aircraft handling qualities in the Cooper- Harper rating. We formulate the Cooper-Harper rating scheme as a fuzzy rule-based system and use it to analyze the effectiveness of the aircraft controller. The automatic estimate of the system-level handling quality provides valuable up-to-date information for diagnostics and vehicle health management. Analyzing the performance of a controller requires a set of concise design requirements and performance criteria. Ir, the case of control systems fm a piloted aircraft, generally applicable quantitative design criteria are difficult to obtain. The reason for this is that the ultimate evaluation of a human-operated control system is necessarily subjective and, with aircraft, the pilot evaluates the aircraft in different ways depending on the type of the aircraft and the phase of flight. In most aerospace applications (e.g., for flight control systems), performance assessment is carried out in terms of handling qualities. Handling qualities may be defined as those dynamic and static properties of a vehicle that permit the pilot to fully exploit its performance in a variety of missions and roles. Traditionally, handling quality is measured using the Cooper-Harper rating and done subjectively by the human pilot. In this work, we have formulated the rules of the Cooper-Harper rating scheme as fuzzy rules with performance, control, and compensation as the antecedents, and pilot rating as the consequent. Appropriate direct measurements on the controller are related to the fuzzy Cooper-Harper rating system: a stability measurement like the rate of change of the cost function can be used as an indicator if the aircraft is under

  19. Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System

    Directory of Open Access Journals (Sweden)

    Nasrodin .T. Mustapha, Momoh J. E. Salami, Nazim and M. Nasiri

    2012-10-01

    Full Text Available This paper presents an intelligent control approach for air handling unit (AHU which is an integral part of heat, ventilation, and air conditioning (HVAC system. In the past years various control design for HVAC have been proposed as this system remarkably consumes very high energy. But most of the proposed designs were focused on the control flow of heat-transfer medium such as chilled or heated water while the importance of the efficient mixture of outdoor and indoor enthalpies is sometimes ignored. These enthalpies invariably determine the best strategy to overcome thermal load in a controlled environment to satisfy human comfort, hence a control design strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor enthalpies by a proper control of AHU dampers and fans. This approach requires sensors to measure temperature and relative humidity of both outdoor and indoor environments. However, unpredictable level of disturbances coming from many sources including heat generated by occupants, electrical items and air leaking and the continuous changes of outdoor enthalpy makes it difficult to model the process. Consequently, conventional controllers are not suitable, hence the use of fuzzy logic controller (FLC is proposed in this paper. This proposed controller operates in a master and slave control loop so as to control the AHU dampers and fans with adjustable output membership function whilst at the same time a scaling-factor method is used to drive the master operation. To implement the proposed system, a small scale prototype has been designed and fabricated. This prototype is an AHU model which consists of ductwork, temperature and humidity sensors, dampers, air cooling and heating systems. A small box is used as a conditioning space in which a room temperature is measured. The control algorithm is programmed using National Instrument (NI LabVIEW and executed using NI FieldPoint. Experimental results reveal that

  20. FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module

    Energy Technology Data Exchange (ETDEWEB)

    Messai, A. [CRNB Ain Oussera, P.O. Box 180, 17200, Djelfa (Algeria); Department of Electronics, Faculty of Sciences Engineering, Blida University, Blida 90000 (Algeria); Mellit, A., E-mail: a.mellit@yahoo.co.u [Department of Electronics, Faculty of Sciences and Technology, Jijel University, Ouled-aissa, P.O. Box 98, Jijel 18000 (Algeria); Department of Electronics, Faculty of Sciences Engineering, Blida University, Blida 90000 (Algeria); Massi Pavan, A. [Department of Materials and Natural Resources, University of Trieste, Via A. Valerio, 2 - 34127 Trieste (Italy); Guessoum, A. [Department of Electronics, Faculty of Sciences Engineering, Blida University, Blida 90000 (Algeria); Mekki, H. [CRNB Ain Oussera, P.O. Box 180, 17200, Djelfa (Algeria); Department of Electronics, Faculty of Sciences Engineering, Blida University, Blida 90000 (Algeria)

    2011-07-15

    Research highlights: {yields} FL-MPPT controller is implemented on FPGA. {yields} Results obtained with ModelSim show a satisfactory performance. {yields} Results will be useful for future development in PV. -- Abstract: This paper describes the hardware implementation of a two-inputs one-output digital Fuzzy Logic Controller (FLC) on a Xilinx reconfigurable Field-Programmable Gate Array (FPGA) using VHDL Hardware Description Language. The FLC is designed for seeking the maximum power point deliverable by a photovoltaic module using the measures of the photovoltaic current and voltage. The simulation results obtained with ModelSim Xilinx Edition-III show a satisfactory performance with a good agreement between the expected and the obtained values.

  1. Static Var Compensator based on Fuzzy Logic Control for Damping Power System Oscillation

    Directory of Open Access Journals (Sweden)

    Prechanon Kumkratug

    2011-01-01

    Full Text Available Problem statement: The disturbance in power system is unavoidable situation. It causes in power system oscillation. Approach: This study applied the Static Var Compensator (SVC to damp power system oscillation. The fuzzy logic control is applied to determine the control strategy of SVC. The simulation results are tested on a Single Machine Infinite bus. The proposed method is equipped in sample system with disturbance. The generator rotor angle curve of the system without and with a SVC is plotted and compared. Results: It was found that the system without a SVC has high variation whereas that of the system with a SVC has much smaller variation. Conclusion: From the simulation results, the SVC can damp power system oscillation.

  2. Daylight illuminance control with fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Trobec Lah, Mateja; Peternelj, Joze; Krainer, Ales [University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, 1000 Ljubljana (Slovenia); Zupancic, Borut [University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana (Slovenia)

    2006-03-15

    The purpose is to take full advantage of daylight for inside illumination. The inside illuminance and luminous efficacy of the available solar radiation were analyzed. The paper deals with the controlled dynamic illuminance response of built environment in real-time conditions. The aim is controlled functioning of the roller blind as a regulation device to assure the desired inside illuminance with smooth roller blind moving. Automatic illuminance control based on fuzzy logic is realized on a test chamber with an opening on the south side. The development and design of the fuzzy controller for the corresponding positioning of the roller blind with the available solar radiation as external disturbance is the subject of this paper. (author)

  3. Novel method for the evaluation of data quality based on fuzzy control

    Institute of Scientific and Technical Information of China (English)

    Ban Xiaojuan; Ning Shurong; Xu Zhaolin; Cheng Peng

    2008-01-01

    One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of data report of national compulsory education outlay guarantee' from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

  4. Reliable Mixed H∞ and Passivity-Based Control for Fuzzy Markovian Switching Systems With Probabilistic Time Delays and Actuator Failures.

    Science.gov (United States)

    Sakthivel, Rathinasamy; Selvi, Subramaniam; Mathiyalagan, Kalidass; Shi, Peng

    2015-12-01

    This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.

  5. A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.

    Science.gov (United States)

    Savran, Aydogan; Kahraman, Gokalp

    2014-03-01

    We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.

  6. Full-order sliding mode control of uncertain chaos in a permanent magnet synchronous motor based on a fuzzy extended state observer

    Institute of Scientific and Technical Information of China (English)

    陈强; 南余荣; 郑恒火; 任雪梅

    2015-01-01

    A full-order sliding mode control based on a fuzzy extended state observer is proposed to control the uncertain chaos in the permanent magnet synchronous motor. Through a simple coordinate transformation, the chaotic PMSM model is transformed into the Brunovsky canonical form, which is more suitable for the controller design. Based on the fuzzy control theory, a fuzzy extended state observer is developed to estimate the unknown states and uncertainties, and the restriction that all the system states should be completely measurable is avoided. Thereafter, a full-order sliding mode controller is designed to ensure the convergence of all system states without any chattering problem. Comparative simulations show the effectiveness and superior performance of the proposed control method.

  7. 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.

  8. A high-speed multiplexer-based fine-grain pipelined architecture for digital fuzzy logic controllers

    Science.gov (United States)

    Rashidi, Bahram; Masoud Sayedi, Sayed

    2015-12-01

    Design and implementation of a high-speed multiplexer-based fine-grain pipelined architecture for a general digital fuzzy logic controller has been presented. All the operators have been designed at gate level. For the multiplication, a multiplexer-based modified Wallace tree multiplier has been designed, and for the division and addition multiplexer-based non-restoring parallel divider and multiplexer-based Manchester adder have been used, respectively. To further increase the processing speed, fine-grain pipelining technique has been employed. By using this technique, the critical path of the circuit is broken into finer pieces. Based on the proposed architecture, and by using Quartus II 9.1, a sample two-input, one-output digital fuzzy logic controller with eight rules has been successfully synthesised and implemented on Stratix II field programmable gate array. Simulations were carried out using DSP Builder in the MATLAB/Simulink tool at a maximum clock rate of 301.84 MHz.

  9. Analysis of inventory difference using fuzzy controllers

    Energy Technology Data Exchange (ETDEWEB)

    Zardecki, A.

    1994-08-01

    The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented.

  10. Intelligent vehicle lateral controller design based on genetic algorithm and T-S fuzzy-neural network

    Institute of Scientific and Technical Information of China (English)

    Ruan Jiuhong; Fu Mengyin; Li Yibin

    2005-01-01

    Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (Ⅳ) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space.Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve Ⅳ lateral motion non-linearity and time-variant parameters problem.

  11. Development of Real Time Implementation of 5/5 Rule based Fuzzy Logic Controller Shunt Active Power Filter for Power Quality Improvement

    Science.gov (United States)

    Puhan, Pratap Sekhar; Ray, Pravat Kumar; Panda, Gayadhar

    2016-12-01

    This paper presents the effectiveness of 5/5 Fuzzy rule implementation in Fuzzy Logic Controller conjunction with indirect control technique to enhance the power quality in single phase system, An indirect current controller in conjunction with Fuzzy Logic Controller is applied to the proposed shunt active power filter to estimate the peak reference current and capacitor voltage. Current Controller based pulse width modulation (CCPWM) is used to generate the switching signals of voltage source inverter. Various simulation results are presented to verify the good behaviour of the Shunt active Power Filter (SAPF) with proposed two levels Hysteresis Current Controller (HCC). For verification of Shunt Active Power Filter in real time, the proposed control algorithm has been implemented in laboratory developed setup in dSPACE platform.

  12. Design New Intelligent PID like Fuzzy Backstepping Controller

    Directory of Open Access Journals (Sweden)

    Arzhang Khajeh

    2014-02-01

    Full Text Available The minimum rule base Proportional Integral Derivative (PID Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy backstepping controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 7 × 7 × 7 = 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. However backstepping controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator’s dynamic equation. This research is used to reduce or eliminate the backstepping controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

  13. Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.

    Science.gov (United States)

    Favieiro, Gabriela W; Balbinot, Alexandre

    2011-01-01

    The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.

  14. Design of Power Cable UAV Intelligent Patrol System Based on Adaptive Kalman Filter Fuzzy PID Control

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

    Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.

  15. FUZZY-LOGIC BASED CALL ADMISSION CONTROL FOR A HETEROGENEOUS RADIO ENVIRONMENT

    DEFF Research Database (Denmark)

    Ramkumar, Venkata; Mihovska, Albena D.; Prasad, Neeli R.;

    Dette dokument foreslår et nyt opkald Admission Control (CAC) algoritme, der finder forskellige typer af applikationer med forskellige QoS parametre, som en bruger og giver de nødvendige QoS til nyankomne brugere uden en forringelse af de QoS at der allerede er optaget dem. Den foreslåede CAC er...... evalueret for en heterogen radio access-teknologier (rotter) scenario. Den QoS parametre varierer afhængigt af den type af ansøgninger, og aftalen mellem udbyderen og brugeren. Den foreslåede CAC er baseret på en fuzzy logik mekanisme, der består af to etaper, i første omgang den bedste celle i hver RAT er...

  16. Advanced Control Techniques with Fuzzy Logic

    Science.gov (United States)

    2014-06-01

    AFRL-RQ-WP-TR-2014-0175 ADVANCED CONTROL TECHNIQUES WITH FUZZY LOGIC James E. Combs Structural Validation Branch Aerospace Vehicles...TECHNIQUES WITH FUZZY LOGIC 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62201F 6. AUTHOR(S) James E. Combs...unlimited. 13. SUPPLEMENTARY NOTES PA Case Number: 88ABW-2014-3281; Clearance Date: 09 Jul 2014. 14. ABSTRACT Research on the Fuzzy Logic control

  17. Design Fuzzy Input-Based Adaptive Sliding Mode Control for Vessel Lift-Feedback Fin Stabilizers with Shock and Vibration of Waves

    Directory of Open Access Journals (Sweden)

    Lihua Liang

    2017-01-01

    Full Text Available An adaptive sliding mode controller based on fuzzy input design is presented, in order to reduce the roll motion of surface vessel fin stabilizers with shock and vibration of waves. The nonlinearities and uncertainties of the system including feedback errors and disturbance induced by waves are analyzed. And the lift-feedback system is proposed, which improves the shortage of conventional fin angle-feedback. Then the fuzzy input-based adaptive sliding mode control is designed for the system. In the controller design, the Lyapunov function is adopted to guarantee the system stability. Finally, experimental results demonstrate the superior performance of the controller designed using fuzzy input, when compared to the PID controller used in practical engineering.

  18. Adaptive Controllers for Permanent Magnet Brushless DC Motor Drive System using Adaptive-Network-based Fuzzy Interference System

    Directory of Open Access Journals (Sweden)

    V. M. Varatharaju

    2011-01-01

    Full Text Available Problem statement: The tuning methodology for the parameters of adaptive speed controller causes a transient deviation of the response from the set reference following variation in load torque in a permanent-magnet brushless DC (BLDC motor drive system. Approach: This study develops a mathematical model of the BLDC drive system, firstly. Secondly, discusses a design of the closed loop drive system employing the Adaptive-Network-based Fuzzy Interference System (ANFIS. The nonlinear simulation model of the BLDC motors drive system with ANFIS control based is simulated in the MATLAB/SIMULINK platform. Results: The necessitated data for training the ANFIS control is generated by simulation of the system with conventional PI controller. Conclusion: The simulated electromagnetic torque and rotor speed signify the superiority of the proposed technique over the classical method.

  19. Novel Approach to Fuzzy Logic Controller Design for Systems With Deadzones

    OpenAIRE

    Kim, Jong-Hwan; Park, Jong-Hwan; Lee, Seon-Woo; Chong, Edwin K. P.

    1992-01-01

    Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a conventional fuzzy logic controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this report, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator...

  20. 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.

  1. Lyapunov Based-Distributed Fuzzy-Sliding Mode Control for Building Integrated-DC Microgrid with Plug-in Electric Vehicle

    DEFF Research Database (Denmark)

    Ghiasi, Mohammad Iman; Aliakbar Golkar, Masoud; Hajizadeh, Amin

    2017-01-01

    This paper presents a distributed control strategy based on Fuzzy-Sliding Mode Control (FSMC) for power control of an infrastructure integrated with a DC-Microgrid, which includes photovoltaic, fuel cell and energy storage systems with Plug-in Electric Vehicles (PEVs). In order to implement the p...

  2. Fuzzy-Logic-Based Gain-Scheduling Control for State-of-Charge Balance of Distributed Energy Storage Systems for DC Microgrids

    DEFF Research Database (Denmark)

    Aldana, Nelson Leonardo Diaz; Dragicevic, Tomislav; Vasquez, Juan Carlos

    2014-01-01

    -charge or deep-discharge in one of the energy storage units. Primary control in a microgrid is responsible for power sharing among units; and droop control is typically used in this stage. This paper proposes a modular and decentralized gain-scheduling control strategy based on fuzzy logic that ensures balanced...

  3. The Self-Organising Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    A marginally stable test system, with a large dead time and an integrator, is stabilised by a self-organising fuzzy controller in a simulation study. It acts as a case study, to explain the self-organising controller to engineering students. The paper is one of a series of tutorial papers...... for a course in fuzzy control....

  4. The Self-Organising Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    A marginally stable test system, with a large dead time and an integrator, is stabilised by a self-organising fuzzy controller in a simulation study. It acts as a case study, to explain the self-organising controller to engineering students. The paper is one of a series of tutorial papers...... for a course in fuzzy control....

  5. Applied intelligent systems: blending fuzzy logic with conventional control

    Science.gov (United States)

    Filev, Dimitar; Syed, Fazal U.

    2010-05-01

    The aim of this paper is to show that design of applied intelligent control systems requires different types of blending between fuzzy logic and conventional control systems. Two alternative automotive applications - a manufacturing process control problem and an advisory system for fuel efficient driving - that benefit from both fuzzy and control theories are reviewed and different levels of prioritisations of both approaches are discussed based on the specificity of the applications.

  6. Design and Implementation an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

    Directory of Open Access Journals (Sweden)

    Mohsen Taheri

    2010-04-01

    Full Text Available Research on humanoid robotics in Mechatronics and Automation Laboratory, Electrical and Computer Engineering, Islamic Azad University Khorasgan branch (Isfahan of Iran was started at
    the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. This paper describes the hardware and software design of the kid size humanoid robot systems of the PERSIA Team in 2009. The robot has 20 actuated degrees of freedom based on Hitec HSR898. In this paper we have tried to focus on areas such as mechanical structure, Image processing unit, robot controller, Robot AI and behavior
    learning. In 2009, our developments for the Kid size humanoid robot include: (1 the design and construction of our new humanoid robots (2 the design and construction of a new hardware and software controller to be used in our robots. The project is described in two main parts: Hardware and Software. The software is developed a robot application which consists walking controller, autonomous motion robot, self localization base on vision and Particle Filter, local AI, Trajectory Planning, Motion Controller and Network. The hardware consists of the mechanical structure and the driver circuit board. Each robot is able to walk, fast walk, pass, kick and dribble when it catches
    the ball. These humanoids have been successfully participating in various robotic soccer competitions. This project is still in progress and some new interesting methods are described in the current report.

  7. Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.

    Science.gov (United States)

    Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S

    2015-07-01

    In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation.

  8. Fuzzy-logic-based supervisory controller for the management of energy in a hybrid system; Control supervisorio difuso para sistemas hibridos de generacion electrica

    Energy Technology Data Exchange (ETDEWEB)

    Lagunas, J.; Caratozzolo, P.; Ortega, C.; Gonzalez, R.

    2004-07-01

    This paper presents and validates the use of a fuzzy-logic-based supervisory controller for the management of energy in a hybrid system. The general configuration of the hybrid system is presented as well as the operational objectives of the supervisory controller. The inputs and outputs of the controller are also presented along with the hierarchical structure employed in order to reduce the number of rules in the knowledge base. The results obtained are compared against those of a conventional controller. Simulations were carried out using Matlab. (Author)

  9. Application of Adaptive Fuzzy PID Leveling Controller

    Directory of Open Access Journals (Sweden)

    Ke Zhang

    2013-05-01

    Full Text Available Aiming at the levelling precision, speed and stability of suspended access platform, this paper put forward a new adaptive fuzzy PID control levelling algorithm by fuzzy theory. The method is aided design by using the SIMULINK toolbox of MATLAB, and setting the membership function and the fuzzy-PID control rule. The levelling algorithm can real-time adjust the three parameters of PID according to the fuzzy rules due to the current state. It is experimented, which is verified the algorithm have better stability and dynamic performance.

  10. Fuzzy Behaviors for Control of Mobile Robots

    Directory of Open Access Journals (Sweden)

    Saleh Zein-Sabatto

    2003-02-01

    Full Text Available In this research work, an RWI B-14 robot has been used as the development platform to embody some basic behaviors that can be combined to build more complex robotics behaviors. Emergency, avoid-obstacle, left wall- following, right wall-following, and move-to-point behaviors have been designed and embodied as basic robot behaviors. The basic behaviors developed in this research are designed based on fuzzy control technique and are integrated and coordinated to from complex robotics system. More behaviors can be added into the system as needed. A robot task can be defined by the user and executed by the intelligent robot control system. Testing results showed that fuzzy behaviors made the robot move intelligently and adapt to changes in its environment.

  11. Adaptive control design for a class of nonlinear systems based on fuzzy logic systems with scalers and saturators

    Science.gov (United States)

    Wang, Yin-He; Luo, Liang; Fan, Yong-Qing; Zhang, Yun; Liu, Xiao-Ping; Zhang, Si-Ying

    2014-03-01

    Many practical engineering applications require various types of fuzzy logic systems (FLSs) to design adaptive controllers for nonlinear systems with uncertainties. In this article, we will consider a fundamental theoretical question: is it possible to find a unified adaptive control design method suited to various types of FLSs? In order to solve this problem, we will introduce scalers and saturators at the input and output terminals of FLSs to form the extended FLSs (EFLS). The scalers and saturators have adjustable parameters. By designing the updated laws of these parameters and the estimate values of the fuzzy approximate accuracies, stable adaptive fuzzy controllers can be realised for a class of nonlinear systems with unknown homogeneous drift functions and gains. The proposed design method is only dependent on the outputs of EFLS and the above updated laws, thus increasing its adaptability. The fuzzy control scheme introduced in this article is suitable for all fuzzy systems with or without fuzzy rules. Simulations will also be used to show the validity of the method proposed in this article.

  12. Fuzzy logic-based diversity-controlled self-adaptive differential evolution

    Science.gov (United States)

    Amali, S. Miruna Joe; Baskar, S.

    2013-08-01

    This article presents a novel method using a fuzzy system (FS) to control the population diversity during the various phases of evolution. A local search is applied at regular intervals on an individual selected at random to aid the population in convergence. This diversity control methodology is applied to vary the crossover rate of self-adaptive differential evolution (SaDE). Three variants of the SaDE algorithm are proposed: (1) diversity-controlled SaDE (DCSaDE); (2) SaDE with local search (SaDE-LS); and (3) diversity-controlled SaDE with local search (DCSaDE-LS). The performance of the proposed algorithms is analysed using a set of unconstrained benchmark functions with respect to average function evaluations, success rate and the mean of the objectives of 30 independent trials. The DCSaDE-LS algorithm had a better success rate for high-dimensional multimodal problems and conserved the number of function evaluations required for most of the problems. It is compared with other popular algorithms and the outcome of the proposed DCSaDE-LS algorithm is validated using non-parametric statistical tests. MATLAB codes for the proposed algorithms may be obtained on request.

  13. Design of a Single Input Fuzzy Logic Controller Based SVC for Dynamic Performance Enhancement of Power Systems

    Directory of Open Access Journals (Sweden)

    DR.D. PADMA SUBRAMANIAN

    2014-10-01

    Full Text Available This paper presents a design of a Single Input Fuzzy Logic Controller (SFLC based Static VAR Compensator (SVC for Dynamic performance enhancement of power systems. The SFLC uses only one input which is the signed distance and has the advantage of reduced number of rules. Improvement of dynamic response by the controller is illustrated in a bifurcation perspective. Bifurcation diagrams of steady state as well as periodic solutions are constructed using continuation method. From the bifurcation diagrams, the existence of various bifurcation points such as, unstable Hopf bifurcation (UHB, stable Hopf bifurcation (SHB, saddle node bifurcation (SNB and period doubling bifurcation (PDB are identified. With the use of tools of nonlinear dynamics, voltage collapse points, and chaotic solutions due to period doublings are unearthed. The effectiveness of the SFL controller over the conventional controller for SVC in delaying the incidence of Hopf bifurcation (HBF, SNB and hence increasing the loadability limit is illustrated for the test system.

  14. Fuzzy Logic-Based Audio Pattern Recognition

    Science.gov (United States)

    Malcangi, M.

    2008-11-01

    Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

  15. Fuzzy MCDM Based on Fuzzy Relational Degree Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper presents a new fuzzy multiple criteria (both qualitative and quantitative) decision-making (MCDM) method based on fuzzy relational degree analysis. The concepts of fuzzy set theory are used to construct a weighted suitability decision matrix to evaluate the weighted suitability of different alternatives versus various criteria. The positive ideal solution and negative ideal solution are then obtained by using a method of ranking fuzzy numbers, and the fuzzy relational degrees of different alternatives versus positive ideal solution and negative ideal solution are calculated by using the proposed arithmetic. Finally, the relative relational degrees of various alternatives versus positive ideal solution are ranked to determine the best alternative. A numerical example is provided to illustrate the proposed method at the end of this paper.

  16. A Novel Optical Morse Code-Based Electronic Lock Using the Ambient Light Sensor and Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Chin-Tan Lee

    2017-02-01

    Full Text Available In this work, a novel electronic lock that can encode and decode optical signals, modulated using Morse code conventions, was developed to build a smart home security system based on the Internet of Things (IoT. There are five topics of interest in this research: (1 optical Morse code encoder; (2 optical Morse code decoder; (3 ambient light sensor circuit; (4 fuzzy controller; (5 cloud monitoring system. We take advantage of the light-emitting components as the encoder, which are readily available in hand-held mobile devices (e.g., Smart phones and photoresistors and a microcontroller as the decoder. By Wi-Fi transferring, even without a personal computer, real-time information about this lock can be uploaded to the cloud service platform, and helps users to ensure home safety on the remote monitoring system. By using the ambient light sensor and fuzzy controller in this novel optical Morse code-based electronic lock, experimental results show that the reliability of this system is much improved from 65% to 100%. That means that it is highly resistant to different illumination conditions in the work environment, and therefore all functions, including coding, emitting, receiving, decoding, uploading and cloud monitoring, can work well. Furthermore, besides the convenience and cost reduction, by incorporating traditional keys into smart phones, as a consumer electronics, our proposed system is suitable for users of all ages because of a user-friendly operation interface.

  17. Development of an object-oriented software based on fuzzy-logic for controlling temperatures in PAC experiments

    Energy Technology Data Exchange (ETDEWEB)

    Lapolli, Andre L.; Yamagishi, Sueli; Domienikan, Claudio; Schoueri, Roberto M.; Carbonari, Artur W.; Saxena, Rajendra N., E-mail: alapolli@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2009-07-01

    The Hyperfine Interaction Laboratory at Instituto de Pesquisas Energeticas e Nucleares (IPEN) has been using Perturbed Angular Correlation (PAC) technique for studying material science for more than 20 years. One of the important aspects of the research involves the study of the behavior of measured properties of samples as a function of temperature. For temperatures higher than room temperature a small resistance furnace is used to heat the sample. The need to carry out the PAC measurement at predefined temperatures steps in a programmed manner is obvious. The present work describes a procedure for the furnace temperature control and automatic data acquisition at different temperatures based on fuzzy logic. The procedure consists in determining the linguistic input (temp, DELTAtemp) and output (pow) variables and their pertinence functions. After defining the variables, an object.oriented program is written in Java language which is an interface between principal data acquisition program and electronic temperature controller of the mini furnace. In addition to the implementation of the class that involves the fuzzy logic and classes with strategic algorithms defined for each temperature range there are classes of communication between systems based on modbus protocol RTU (Remote Terminal Unit) connected to serial interface RS-488. In this manner the applied technology for the development of software permits higher software life requiring only small alterations or implementation of classes in the use with new equipment. (author)

  18. Fusion Control Strategy Based on I-Fuzzy-Smith Algorithm for Complex Process with Large Lag%大时滞复杂过程中基于I-Fuzzy-Smith算法的融合控制策略

    Institute of Scientific and Technical Information of China (English)

    倪元敏

    2013-01-01

    Aimed at the puzzle to actualize the high quality control in complex process with large lag,the paper presented a sort of fusion control strategy based on the integration of integral, fuzzy and Smith estimator control algorithm.In the paper,it studied the cybernetics characteristic of complex process with large lag and its control puzzle,integrated itself superiority of integral, fuzzy and Smith estimator control,gave a sort of I-Fuzzy-Smith fusion control algorithm.It took a complex process of large inertia with large lag as an example,under the environment of Matlab, and made the detailed simulation experiment in contrast such as robustness etc.The system re-sponse curve demonstrated that the I-Fuzzy-Smith based control algorithm excels than others in dynamical and steady control quality and robustness.The research result shows that the pro-posed control algorithm is high in control precision,better in dynamical and steady control quality, and very strong robustness.%针对大时滞复杂过程难以实施高品质控制,提出了一种基于I-Fuzzy-Smith算法的融合控制策略。研究了大时滞复杂过程的控制论特性与控制难点,以及集成模糊、Smith预估和积分控制各自的优势,给出了将其融合的I-Fuzzy-Smith控制算法。以大惯性大时滞过程为例,基于Matlab仿真对融合控制进行了比较研究,系统响应验证了I-Fuzzy-Smith控制算法的动、静态控制品质和鲁棒性优于另外两种算法。研究结果表明:提出的融合策略控制精度高,系统动、静态控制品质好,表现出很强的鲁棒性。

  19. Fuzzy Adaptive Control System of a Non-Stationary Plant

    Science.gov (United States)

    Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio

    2016-08-01

    This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

  20. Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications

    Directory of Open Access Journals (Sweden)

    Radu-Emil Precup

    2006-01-01

    Full Text Available The development of fuzzy control systems is usually performed by heuristicmeans, incorporating human skills, the drawback being in the lack of general-purposedevelopment methods. A major problem, which follows from this development, is theanalysis of the structural properties of the control system, such as stability, controllabilityand robustness. Here comes the first goal of the paper, to present a stability analysismethod dedicated to fuzzy control systems with mechatronics applications based on the useof Popov’s hyperstability theory. The second goal of this paper is to perform the sensitivityanalysis of fuzzy control systems with respect to the parametric variations of the controlledplant for a class of servo-systems used in mechatronics applications based on theconstruction of sensitivity models. The stability and sensitivity analysis methods provideuseful information to the development of fuzzy control systems. The case studies concerningfuzzy controlled servo-systems, accompanied by digital simulation results and real-timeexperimental results, validate the presented methods.

  1. A fuzzy control design case: The fuzzy PLL

    Science.gov (United States)

    Teodorescu, H. N.; Bogdan, I.

    1992-01-01

    The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.

  2. Hybrid Kalman Filter/Fuzzy Logic based Position Control of Autonomous Mobile Robot

    Directory of Open Access Journals (Sweden)

    Nitin Afzulpurkar

    2008-11-01

    Full Text Available This paper describes position control of autonomous mobile robot using combination of Kalman filter and Fuzzy logic techniques. Both techniques have been used to fuse information from internal and external sensors to navigate a typical mobile robot in an unknown environment. An obstacle avoidance algorithm utilizing stereo vision technique has been implemented for obstacle detection. The odometry errors due to systematic-errors (such as unequal wheel diameter, the effect of the encoder resolution etc. and/or non-systematic errors (ground plane, wheel-slip etc. contribute to various motion control problems of the robot. During the robot moves, whether straight-line and/or arc, create the position and orientation errors which depend on systematic and/or non-systematic odometry errors. The main concern in most of the navigating systems is to achieve the real-time and robustness performances to precisely control the robot movements. The objective of this research is to improve the position and the orientation of robot motion. From the simulation and experiments, we prove that the proposed mobile robot moves from start position to goal position with greater accuracy avoiding obstacles.

  3. Performance analysis of electronic power transformer based on neuro-fuzzy controller.

    Science.gov (United States)

    Acikgoz, Hakan; Kececioglu, O Fatih; Yildiz, Ceyhun; Gani, Ahmet; Sekkeli, Mustafa

    2016-01-01

    In recent years, electronic power transformer (EPT), which is also called solid state transformer, has attracted great interest and has been used in place of the conventional power transformers. These transformers have many important functions as high unity power factor, low harmonic distortion, constant DC bus voltage, regulated output voltage and compensation capability. In this study, proposed EPT structure contains a three-phase pulse width modulation rectifier that converts 800 Vrms AC to 2000 V DC bus at input stage, a dual active bridge converter that provides 400 V DC bus with 5:1 high frequency transformer at isolation stage and a three-phase two level inverter that is used to obtain AC output at output stage. In order to enhance dynamic performance of EPT structure, neuro fuzzy controllers which have durable and nonlinear nature are used in input and isolation stages instead of PI controllers. The main aim of EPT structure with the proposed controller is to improve the stability of power system and to provide faster response against disturbances. Moreover, a number of simulation results are carried out to verify EPT structure designed in MATLAB/Simulink environment and to analyze compensation ability for voltage harmonics, voltage flicker and voltage sag/swell conditions.

  4. Aircraft Attitude Control by Fuzzy Control

    Science.gov (United States)

    Kato, Akio; Matsuba, Takashi

    The fuzzy control law to improve dutch roll characteristics of aircraft was designed and its control performance was evaluated. First, the control law was designed for a small-high speed aircraft at low altitude and low-speed flight conditions. The control law was then applied to flight conditions from minimum speed to supersonic speed and from sea level to high altitude. The control performance for these conditions was evaluated. Furthermore, this control law was adapted to a large transport aircraft with no parameter changes. The evaluation showed good control performance to improve the dutch roll characteristics under all flight conditions for both small high-speed aircraft and large transport aircraft without the parameter changes. This means that the fuzzy control proved to provide effective flexible application to aircraft stability augmentation. If an aircraft in actual flight is in strong air turbulence, inputs to the fuzzy controller may exceed the limit of its effective range. To cope with this problem, the countermeasures were introduced, their methods tested, and their effectiveness proved.

  5. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor......This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable...

  6. Tuning of a neuro-fuzzy controller by genetic algorithm.

    Science.gov (United States)

    Seng, T L; Bin Khalid, M; Yusof, R

    1999-01-01

    Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

  7. Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

    Institute of Scientific and Technical Information of China (English)

    Min WANG; Xiuying WANG; Bing CHEN; Shaocheng TONG

    2007-01-01

    In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.

  8. Fast Response Three Phase Induction Motor Using Indirect Field Oriented Control (IFOC Based On Fuzzy-Backstepping

    Directory of Open Access Journals (Sweden)

    Rizana Fauzi

    2015-06-01

    Full Text Available Induction Motor in Electrical drive system at a accelleration speed for example in electric cars have a hard speed setting is set on a wide range, causing an inconvenience for motorists and a fast response is required any change of speed. It is necessary for good system performance in control motor speed and torque at low speed or fast speed response, which is operated by Indirect Field Oriented Control (IFOC. Speed control on IFOC methods should be better to improving the performance of rapid response in the induction motor. In this paper presented a method of incorporation of Fuzzy Logic Controller and Backstepping (Fuzzy-Backstepping to improve the dynamically response speed and torque in Induction Motor on electric car, so we get smoothness at any speed change and braking as well as maximum torque of induction motor. Test results showed that Fuzzy-Backstepping can increase the response to changes speed in electric car. System testing is done with variations of the reference point setting speed control system, the simulation results of the research showed that the IFOC method is not perfect in terms of induction motor speed regulation if it’s not use speed control. Fuzzy-Backstepping control is needed which can improve the response of output, so that the induction motor has a good performance, small oscillations when start working up to speed reference. Keywords: Fuzzy-Backstepping, IFOC, induction motor

  9. Priority-based queuing and transmission rate management using a fuzzy logic controller in WSNs

    Directory of Open Access Journals (Sweden)

    Imen Bouazzi

    2017-06-01

    Full Text Available Wireless sensor networks (WSNs operate under challenging conditions, such as maintaining message latency and the reliability of data transmission and maximizing the battery life of sensor nodes. The aim of this study is to propose a fuzzy logic algorithm for solving these issues, which are difficult to address with traditional techniques. The idea, in this study, is to employ a fuzzy logic scheme to optimize energy consumption and minimize packet drops. We demonstrated how fuzzy logic can be used to tackle this specific communication problem with minimal computational complexity. In this context, the implementation of a fuzzy logic in the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA mechanism is achieved through filling the queue length and the traffic rate at each node. Through simulations, we show that our proposed technique has a better performance in terms of energy consumption compared to the basic implementation of CSMA/CA.

  10. Simulation of fuzzy control systems for nonferrous alloy vacuum counter-pressure casting

    Institute of Scientific and Technical Information of China (English)

    YAN Qing-song; CAI Qi-zhou; WEI Bo-kang; YU Huan; YU Zi-rong

    2005-01-01

    Through simulation analyses of vacuum counter-pressure casting fuzzy control systems based on MATLAB, fuzzy control systems designed by simulation can track technical route established well. When transmission functions of vacuum counter-pressure casting controlled objects are changed in operation, fuzzy control systems can carry on self-regulation and stabilize quickly, and embody the advantages of fleet response velocity and little adjusting quantity. The design of vacuum counter-pressure casting fuzzy control systems is accelerated and improved greatly by simulation based on MATLAB. Meanwhile, their design is accurate and reliable. Moreover, microstructure and properties of thin-wall aluminum alloy castings are improved effectively by using fuzzy control systems.

  11. Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.

    Science.gov (United States)

    Ayoubi, Mohammad A.; Swei, Sean Shan-Min; Nguyen, Nhan T.

    2014-01-01

    This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM).

  12. New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance

    Directory of Open Access Journals (Sweden)

    Mikael Collan

    2015-01-01

    Full Text Available This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated.

  13. FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION

    Directory of Open Access Journals (Sweden)

    Imad Zein

    2014-01-01

    Full Text Available Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP, which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT . Over the past years many MPPT techniques have been published and based on that the main paper’s objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller

  14. Fuzzy modeling and control theory and applications

    CERN Document Server

    Matía, Fernando; Jiménez, Emilio

    2014-01-01

    Much work on fuzzy control, covering research, development and applications, has been developed in Europe since the 90's. Nevertheless, the existing books in the field are compilations of articles without interconnection or logical structure or they express the personal point of view of the author. This book compiles the developments of researchers with demonstrated experience in the field of fuzzy control following a logic structure and a unified the style. The first chapters of the book are dedicated to the introduction of the main fuzzy logic techniques, where the following chapters focus on concrete applications. This book is supported by the EUSFLAT and CEA-IFAC societies, which include a large number of researchers in the field of fuzzy logic and control. The central topic of the book, Fuzzy Control, is one of the main research and development lines covered by these associations.

  15. Fuzzy Controllers for a Gantry Crane System with Experimental Verifications

    Directory of Open Access Journals (Sweden)

    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.

  16. Fuzzy logic applied to the control of the energy consumption in intelligent buildings; Logica fuzzy aplicada ao controle do consumo de energia eletrica em edificios inteligentes

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Herbert R. do N.

    1998-02-01

    This work shows a study on the using of fuzzy control algorithms for the energy optimization of a standard building. The simulation of this type of control was performed using a central conditioned air model and the fuzzy control architecture already used in various control projects. This situation allowed a comparative study among the the control algorithms normally used in conditioned air installations, and the control performed through the building automation system, using an algorithm based on fuzzy logic.

  17. Use of UPFC device controlled by fuzzy logic controllers for decoupled power flow control

    Directory of Open Access Journals (Sweden)

    Ivković Sanja

    2014-01-01

    Full Text Available This paper investigates the possibility of decoupled active and reactive power flow control in a power system using a UPFC device controlled by fuzzy logic controllers. A Brief theoretical review of the operation principles and applications of UPFC devices and design principles of the fuzzy logic controller used are given. A Matlab/Simulink model of the system with UPFC, the fuzzy controller setup, and graphs of the results are presented. Conclusions are drawn regarding the possibility of using this system for decoupled control of the power flow in power systems based on analysis of these graphs.

  18. Study on the Fuzzy COntrol Strategy of Automobile with CVT

    Institute of Scientific and Technical Information of China (English)

    HuJianjun; QINDatong; 等

    2002-01-01

    In order to study the dynamic characteristics of automobile with a CVT system, a bond graph analysis model of continuously variable transmission is established.On the base of the simulation state space equations that are established with bond graph theory,a fuzzy control strategy with an expert system of starting process has been introduced.Considering uncertain system parameters and exterior resistance disturbing,the effect of the profile of membership function and the defuzzification algorthm on the capacity of the fuzzy controller has been studied.The result of simulation proves that the proposed fuzzy controller is effective and feasible,Such controller has been employed in the actual control and has proved practicable.The study lays a foundation for design of the fuzzy controller for automobile with a CVT system.

  19. Fuzzy logic applications to expert systems and control

    Science.gov (United States)

    Lea, Robert N.; Jani, Yashvant

    1991-01-01

    A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

  20. Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology

    Science.gov (United States)

    Bonissone, Piero P.

    1995-06-01

    We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.

  1. Fuzzy-PI-based centralised control of semi-isolated FP-SEPIC/ZETA BDC in a PV/battery hybrid system

    Science.gov (United States)

    Mahendran, Venmathi; Ramabadran, Ramaprabha

    2016-11-01

    Multiport converters with centralised controller have been most commonly used in stand-alone photovoltaic (PV)/battery hybrid system to supply the load smoothly without any disturbances. This study presents the performance analysis of four-port SEPIC/ZETA bidirectional converter (FP-SEPIC/ZETA BDC) using various types of centralised control schemes like Fuzzy tuned proportional integral controller (Fuzzy-PI), fuzzy logic controller (FLC) and conventional proportional integral (PI) controller. The proposed FP-SEPIC/ZETA BDC with various control strategy is derived for simultaneous power management of a PV source using distributed maximum power point tracking (DMPPT) algorithm, a rechargeable battery, and a load by means of centralised controller. The steady state and the dynamic response of the FP-SEPIC/ZETA BDC are analysed using three different types of controllers under line and load regulation. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. The power balance between the ports is achieved by pseudorandom carrier modulation scheme. The response of the FP-SEPIC/ZETA BDC is also validated experimentally using hardware prototype model of 500 W system. The effectiveness of the control strategy is validated using simulation and experimental results.

  2. Fuzzy Controller Parameters´ Proposal in Matlab Enviroment

    Directory of Open Access Journals (Sweden)

    Štefan Koprda

    2015-10-01

    Full Text Available  In this paper we deal with the creation and design of fuzzy controller using Matlab. As the founder of fuzzy theory is considered Azerbaijani Prof. Lotfi Zadeh. In his article in 1965 is first time specified term "fuzzy" [8]. Defining of Zadeh fuzzy sets is based on the efforts of experts to build multivalued logic. Multivalued logic (Lukasiewicz logic allows to work with imprecise (vague terms and eliminates gaps of "classical" two-valued logic that we utilized in the art since ancient times. The use of vague terms can not be avoided especially when describing the behavior of complex systems. Their exact analytical description would be technically infeasible or prohibitively complicated, and therefore for realization very expensive. Cause of fuzzy theory can also be formulated by means of the so-called Law incompatibility (incompatibility by Druckmüller [2].

  3. Fuzzy robust attitude controller design for hydrofoil catamaran

    Institute of Scientific and Technical Information of China (English)

    Ren Junsheng; Yang Yansheng

    2005-01-01

    A robust attitude controller for hydrofoil catamaran throughout its operating envelope is proposed, based on Tagaki-Sugeno (T-S) fuzzy model. Firstly, T-S fuzzy model and robust attitude control strategy for hydrofoil catamaran is presented by use of linear matrix inequality (LMI) techniques. Secondly, a nonlinear mathematical model of hydrofoil catamaran is established, acting as the platform for further researches. The specialty in interpolation of T-S fuzzy model guarantees that feedback gain can be obtained smoothly, while boat's speed is shifting over the operating envelope. The external disturbances are also attenuated to achieve H∞ control performance, meanwhile. Finally, based on such a boat,HC200B-A1, simulation researches demonstrate the design procedures and the effectiveness of fuzzy robust attitude controller.

  4. Smart Spectrometer for Distributed Fuzzy Control

    CERN Document Server

    Benoit, Eric

    2009-01-01

    If the main use of colour measurement is the metrology, it is now possible to find industrial control applications which uses this information. Using colour in process control leads to specific problems where human perception has to be replaced by colour sensors. This paper relies on the fuzzy representation of colours that can be taken into account by fuzzy controllers. If smart sensors already include intelligent functionalities like signal processing, or configuration, only few of them include functionalities to elaborate the fuzzy representation of measurements. In this paper, we develop a solution where the numeric processing is performed locally by the sensor, and where fuzzy processing is exported towards another computing resource by means of the CAN network. This paper presents the concept and the application to a smart fuzzy spectrometer.

  5. 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...

  6. Robust H∞ Control for a Class of Uncertain Switched Fuzzy Time-Delay Systems Based on T-S Models

    Directory of Open Access Journals (Sweden)

    Yang Cui

    2013-01-01

    Full Text Available The problem of robust H∞ control for a class of uncertain switched fuzzy time-delay systems is discussed for system described by T-S fuzzy model with Lyapunov stable theory and linear matrix inequality approach. A sufficient condition in terms of the LMI is derived such that the stability of the closed-loop systems is guaranteed. The continuous state feedback controller is built to ensure the asymptotically stable closed-loop system for all allowable uncertainties, with the switching law designed to implement the global asymptotic stability of uncertain switched fuzzy time-delay systems. In this model, each and every subsystem of the switched systems is an uncertain fuzzy one to which the parallel distributed compensation (PDC controller of each sub fuzzy system system is proposed with its main condition given in a more solvable form of convex combinations. Such a switched control system is highly robust to varying parameters. A simulation shows the feasibility and effectiveness of the design method.

  7. Implementation of Real-Time Machining Process Control Based on Fuzzy Logic in a New STEP-NC Compatible System

    Directory of Open Access Journals (Sweden)

    Po Hu

    2016-01-01

    Full Text Available Implementing real-time machining process control at shop floor has great significance on raising the efficiency and quality of product manufacturing. A framework and implementation methods of real-time machining process control based on STEP-NC are presented in this paper. Data model compatible with ISO 14649 standard is built to transfer high-level real-time machining process control information between CAPP systems and CNC systems, in which EXPRESS language is used to define new STEP-NC entities. Methods for implementing real-time machining process control at shop floor are studied and realized on an open STEP-NC controller, which is developed using object-oriented, multithread, and shared memory technologies conjunctively. Cutting force at specific direction of machining feature in side mill is chosen to be controlled object, and a fuzzy control algorithm with self-adjusting factor is designed and embedded in the software CNC kernel of STEP-NC controller. Experiments are carried out to verify the proposed framework, STEP-NC data model, and implementation methods for real-time machining process control. The results of experiments prove that real-time machining process control tasks can be interpreted and executed correctly by the STEP-NC controller at shop floor, in which actual cutting force is kept around ideal value, whether axial cutting depth changes suddenly or continuously.

  8. Study on the idity fuzzy neural network controller based on improved genetic algorithm of intelligent temperature control system in vegetable greenhouse

    Science.gov (United States)

    Zhang, Su; Yuan, Hongbo; Zhou, Yuhong; Wang, Nan

    2009-07-01

    In order to create the environment that the suitable crop grows, direct against the characteristic of the system of the greenhouse. The aim of the research was to study the intelligent temperature control system in vegetable greenhouse. Based on computer automatic control ,a kind of intelligent temperature control system in vegetable greenhouse was designed. The design thought of systematic hardwares such as temperature collection system, temperature display, control system, heater control circuit in the heater were expounded in detail The control algorithm of the system was improved and system simulation was made by using MATLAB finally. The control algorithm of the system was improved by a new fuzzy neural network controller. The stimulation curve showed that the system had better controlling and tracking performances ,higher accuracy of controlling the temperature. And this system and host epigyny computer could constitute the secondary computer control system which was favorable for realizing the centralized management of the production.

  9. Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance Evaluation

    Institute of Scientific and Technical Information of China (English)

    Vineet Kumar; A.P.Mittal

    2010-01-01

    In this paper,a parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller is proposed.It is derived from the conventional parallel proportional-integral-derivative (PID) controller.It preserves the linear structure of a conventional parallel PID controller,with analytical formulas.The final shape of the controller is a discrete-time fuzzy version of a conventional parallel PID controller.Computer simulations are performed to evaluate the performance of the FP+FI+FD controller for setpoint tracking and load-disturbance rejection for some complex processes,such as first-and second-order processes with delay,inverse response process with and without delay and higher order processes.Also,the performance of the proposed fuzzy controller is evaluated experimentally on highly nonlinear liquid-flow process with a hysteresis characteristic due to a pneumatic control valve.The simulation and real time control is done using National InstrumentTM hardware and software (LabVIEWTM).The response of the FP+FI+FD controller is compared with the conventional parallel PID controller,tuned with the Ziegler-Nichols (Z-H) and (A)str(o)mH(a)gglund (A-H) tuning technique.It is observed that the FP+FI+FD controller performed much better than the conventional PI/PID controller.Simulation and experimental results demonstrate the effectiveness of the proposed parallel FP+FI+FD controller.

  10. FUZZY SLIDING MODE CONTROLLER FOR DOUBLY FED ...

    African Journals Online (AJOL)

    2010-12-31

    Dec 31, 2010 ... motor (DFIM) with a fuzzy sliding mode controller (FSMC). ... becoming a major candidate in high-performance motion control applications, where ..... residual vibrations in high frequencies [17] (chattering phenomenon).

  11. Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch

    OpenAIRE

    Mahdad, Belkacem; Srairi, Kamel; BOUKTIR, Tarek; Benbouzid, Mohamed

    2010-01-01

    International audience; This paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure as a local search to expl...

  12. An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

    Science.gov (United States)

    Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

    2016-01-01

    This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

  13. Fuzzy attitude control of solar sail via linear matrix inequalities

    Science.gov (United States)

    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.

  14. Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

    Science.gov (United States)

    Fan, Shou-Zen; Shieh, Jiann-Shing

    2014-01-01

    We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability. PMID:25587533

  15. Optimal design and robustification of fuzzy-logic controllers for robotic manipulators using genetic algorithms

    CERN Document Server

    Moini, A

    2002-01-01

    In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.

  16. Fuzzy Control for Food Agricultural Robotics of a Degree

    Directory of Open Access Journals (Sweden)

    Lepeng Song

    2014-02-01

    Full Text Available In this study, we have a research of the fuzzy control for food agricultural robotics of a degree. Weeding robots can replace humans weeding activities, since the control system with nonlinear, robustness and a series of complex time-varying characteristics of the traditional PID control of the food agricultural robot end of the operation control effect cannot achieve the desired results, therefore, the design for the traditional use of classical PID control algorithm to control the food agricultural robot end of the operation of a series of drawbacks, combining cutting-edge control theory, fuzzy rule-based adaptive PID control strategy to control the entire system, so as to achieve the desired control effect. Experimental results show that the fuzzy adaptive PID control method for robot end postural control has better adaptability and track-ability.

  17. Design and implementation of fuzzy logic controllers. Thesis Final Report, 27 Jul. 1992 - 1 Jan. 1993

    Science.gov (United States)

    Abihana, Osama A.; Gonzalez, Oscar R.

    1993-01-01

    The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.

  18. Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures

    Directory of Open Access Journals (Sweden)

    Mohammad Najafzadeh

    2015-03-01

    Full Text Available In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO. Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS. The testing results of performances were compared with the gene-expression programming (GEP, evolutionary polynomial regression (EPR model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.

  19. Decentralized adaptive fuzzy control of robot manipulators.

    Science.gov (United States)

    Jin, Y

    1998-01-01

    This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations.

  20. Direct Adaptive Fuzzy Sliding Mode Control with Variable Universe Fuzzy Switching Term for a Class of MIMO Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Guo Haigang

    2012-01-01

    Full Text Available Combining adaptive fuzzy sliding mode control with fuzzy or variable universe fuzzy switching technique, this study develops two novel direct adaptive schemes for a class of MIMO nonlinear systems with uncertainties and external disturbances. The proposed control schemes consist of fuzzy equivalent control terms, fuzzy switching control terms (in scheme one or variable universe fuzzy switching control terms (in scheme two, and compensation control terms. The compensation control terms are used to relax the assumption on fuzzy approximation error. Based on Lyapunov stability theory, the parameters update laws are adaptively tuned online and the global asymptotic stability of the closed-loop system can be guaranteed. The major contribution of this study is to develop a novel framework for designing direct adaptive fuzzy sliding mode control scheme facing model uncertainties and external disturbances. The derived schemes can effectively solve the chattering problem and the equivalent control calculation in that environment. Simulation results performed on a two-link robotic manipulator demonstrate the feasibility of the proposed control schemes.

  1. Wavelet transform with fuzzy tuning based indirect field oriented speed control of three-phase induction motor drive

    DEFF Research Database (Denmark)

    Sanjeevikumar, P.; Daya, J.L. Febin; Wheeler, Patrick

    2015-01-01

    This manuscript presents the details about the novel controller using wavelet transform and fuzzy logic tuning for speed control of an induction motor drive. The conventional proportional-integral (PI) speed controller in an indirect vector control of induction motor drive has been replaced...... controller along with an induction motor drive incorporating the proposed controller has been developed in numerical simulation software using Matlab/Simulink environment and a set of results are provided within this paper in comparison to the conventional controller scheme....... by the proposed controller for an improved transient and steady state performances. The discrete wavelet transform has been used to decompose the error speed into different frequency components and the fuzzy logic is used to generate the scaling gains of the wavelet controller. The complete model of the proposed...

  2. A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

    Institute of Scientific and Technical Information of China (English)

    王攀; 徐承志; 冯珊; 徐爱华

    2002-01-01

    This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.

  3. Fuzzy modelling and impulsive control of the hyperchaotic Lü system

    Institute of Scientific and Technical Information of China (English)

    Zhang Xiao-Hong; Li Dong

    2009-01-01

    This paper presents a novel approach to hyperchvos control of hyperchaotic systems based on impulsive control and the Takagi-Sugeno (T-S) fuzzy model. In this study, the hyperchaotic Lü system is exactly represented by the T-S fuzzy model and an impulsive control framework is proposed for stabilizing the hyperchaotic Lü system, which is also suitable for classes of T-S fuzzy hyperchaotic systems, such as the hyperchaotic Rossler, Chen, Chua systems and so on. Sufficient conditions for achieving stability in impulsive T-S fuzzy hyperchaotic systems are derived by using Lyapunov stability theory in the form of the linear matrix inequality, and axe less conservative in comparison with existing results. Numerical simulations are given to demonstrate the effectiveness of the proposed method.

  4. Fuzzy Adaptive Repetitive Control for Periodic Disturbance with Its Application to High Performance Permanent Magnet Synchronous Motor Speed Servo Systems

    National Research Council Canada - National Science Library

    Junxiao Wang

    2016-01-01

    .... Then, the mathematical model of PMSM is given. Subsequently, a fuzzy adaptive repetitive controller based on repetitive control and fuzzy logic control is designed for the PMSM speed servo system...

  5. Wind and Wave Disturbances Compensation to Floating Offshore Wind Turbine Using Improved Individual Pitch Control Based on Fuzzy Control Strategy

    Directory of Open Access Journals (Sweden)

    Feng Yang

    2014-01-01

    Full Text Available Due to the rich and high quality of offshore wind resources, floating offshore wind turbine (FOWT arouses the attentions of many researchers. But on a floating platform, the wave and wind induced loads can significantly affect power regulation and vibration of the structure. Therefore, reducing these loads becomes a challenging part of the design of the floating system. To better alleviate these fatigue loads, a control system making compensations to these disturbances is proposed. In this paper an individual pitch control (IPC system integrated with disturbance accommodating control (DAC and model prediction control (MPC through fuzzy control is developed to alleviate the fatigue loads. DAC is mainly used to mitigate the effects of wind disturbance and MPC counteracts the effects of wave on the structure. The new individual pitch controller is tested on the NREL offshore 5 MW wind turbine mounted on a barge with a spread-mooring system, running in FAST, operating above-rated condition. Compared to the original baseline collective pitch control (CPC (Jonkman et al., 2007, the IPC system shows a better performance in reducing fatigue loads and is robust to complex wind and wave disturbances as well.

  6. 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.

  7. HYBRID FUZZY CONTROL FOR ELECTRO-HYDRAULIC ACTIVE DAMPING SUSPENSION

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A new control scheme, the hybrid fuzzy control method, for active damping suspension system is presented. The scheme is the result of effective combination of the statistical optimal control method based on the statistical property of suspension system, with the bang-bang control method based on the real-time characteristics of suspension system. Computer simulations are performed to compare the effectiveness of hybrid fuzzy control scheme with that of optimal damping control, bang-bang control, and passive suspension. It takes the effects of time-variant factors into full account. The superiority of the proposed hybrid fuzzy control scheme for active damping suspension to the passive suspension is verified in the experiment study.

  8. Fuzzy Theory Based Control Method for an In-pipe Robot to Move in Variable Resistance Environment

    Institute of Scientific and Technical Information of China (English)

    LI Te; MA Shugen; LI Bin; WANG Minghui; WANG Yuechao

    2015-01-01

    Most of the existing screw drive in-pipe robots cannot actively adjust the maximum traction capacity, which limits the adaptability to the wide range of variable environment resistance, especially in curved pipes. In order to solve this problem, a screw drive in-pipe robot based on adaptive linkage mechanism is proposed. The differential property of the adaptive linkage mechanism allows the robot to move without motion interference in the straight and varied curved pipes by adjusting inclining angles of rollers self-adaptively. The maximum traction capacity of the robot can be changed by actively adjusting the inclining angles of rollers. In order to improve the adaptability to the variable resistance, a torque control method based on the fuzzy controller is proposed. For the variable environment resistance, the proposed control method can not only ensure enough traction force, but also limit the output torque in a feasible region. In the simulations, the robot with the proposed control method is compared to the robot with fixed inclining angles of rollers. The results show that the combination of the torque control method and the proposed robot achieves the better adaptability to the variable resistance in the straight and curved pipes.

  9. Systematic methods for the design of a class of fuzzy logic controllers

    Science.gov (United States)

    Yasin, Saad Yaser

    2002-09-01

    Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

  10. A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper,an adaptive dynamic control scheme based on a fuzzy neural network is presented,that presents utilizes both feed-forward and feedback controller elements.The former of the two elements comprises a neural network with both identification and control role,and the latter is a fuzzy neural algorithm,which is introduced to provide additional control enhancement.The feedforward controller provides only coarse control,whereas the feedback oontroller can generate on-line conditional proposition rule automatically to improve the overall control action.These properties make the design very versatile and applicable to a range of industrial applications.

  11. The Fuzzy-PID Control Method for Temperature of Gas-fired Water Heater Based on Dahllin Algorithm%基于大林算法的燃气热水器温度Fuzzy-PID控制方法

    Institute of Scientific and Technical Information of China (English)

    杜福银

    2012-01-01

    Based on the mathematical model of gas-fired water heater temperature control system, proposed using Dahlin algorithm and Fuzzy-PID controllers to handle the pure delay inertial link of gas-fired water heater temperature control system. Through the detailed design process of Dahlin Fuzzy-PID controller and the example simulations of the gas-fired water heater temperature control system, the control method proposed in this paper has better robust property and higher susceptibility, which is foundation for more study.%在燃气热水器温度控制的数学模型基础上,提出利用大林算法来处理燃气热水器温度控制系统纯滞后的惯性环节,利用Fuzzy-PID算法实现给定燃气流量的控制.通过对大林控制器的详细设计过程和对燃气热水器温度控制系统的实例仿真可以看出,该控制方法具有良好的鲁棒性和较高的灵敏度,这为以后进一步研究奠定了基础.

  12. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)

  13. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers.

  14. Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems

    Science.gov (United States)

    Roopaei, Mehdi; Zolghadri, Mansoor; Meshksar, Sina

    2009-09-01

    In this article, a novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC). Making use of the SMC design framework, we propose two fuzzy systems to be used as reaching and equivalent parts of the SMC. In this way, we make use of the fuzzy logic to handle uncertainty/disturbance in the design of the equivalent part and provide a chattering free control for the design of the reaching part. To construct the equivalent control law, an adaptive fuzzy inference engine is used to approximate the unknown parts of the system. To get rid of the chattering, a fuzzy logic model is assigned for reaching control law, which acting like the saturation function technique. The main advantage of our proposed methodology is that the structure of the system is unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are required in advance. Using Lyapunov stability theory and Barbalat's lemma, the closed-loop system is proved to be stable and convergence properties of the system is assured. Simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.

  15. Simulation of Fuzzy Inductance Motor using PI Control Application

    Directory of Open Access Journals (Sweden)

    S.V.Halse

    2013-06-01

    Full Text Available Fuzzy control has been widely used in industrial controls, particularly in situations where conventional control design techniques have been difficult to apply. Number of fuzzy rules is very important for real time fuzzy control applications. This study is motivated by the increasing need in the industry to design highly reliable, efficiency and low complexity controllers. The proposed fuzzy controller is constructed by several fuzzy controllers with less fuzzy rules to carry out control tasks. Performances of the proposed fuzzy controller are investigated and compared to those obtained from the conventional fuzzy controller. Fuzzy logic control method has the ability to handle errors in control operation with system nonlinearity and its performance is less affected by system parameter variations.

  16. Control of a dc motor using fuzzy logic control algorithm | Usoro ...

    African Journals Online (AJOL)

    This study sought to establish the impact of a fuzzy logic controller (FLC) and a ... A choice of seven membership functions was designed for the error and change in ... Based on the findings, it was observed that the fuzzy speed controlled DC ...

  17. Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.

    Science.gov (United States)

    Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu

    2015-05-01

    This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

  18. CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY

    Directory of Open Access Journals (Sweden)

    Shumkov Y. A.

    2014-11-01

    Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning

  19. A Load Frequency Control in an Off-Grid Sustainable Power System Based on a Parameter Adaptive PID-Type Fuzzy Controller

    Science.gov (United States)

    Ronilaya, Ferdian; Miyauchi, Hajime

    2014-10-01

    This paper presents a new implementation of a parameter adaptive PID-type fuzzy controller (PAPIDfc) for a grid-supporting inverter of battery to alleviate frequency fluctuations in a wind-diesel power system. A variable speed wind turbine that drives a permanent magnet synchronous generator is assumed for demonstrations. The PAPIDfc controller is built from a set of control rules that adopts the droop method and uses only locally measurable frequency signal. The output control signal is determined from the knowledge base and the fuzzy inference. The input-derivative gain and the output-integral gain of the PAPIDfc are tuned online. To ensure safe battery operating limits, we also propose a protection scheme called intelligent battery protection (IBP). Several simulation experiments are performed by using MATLAB®/SimPowersystems™. Next, to verify the scheme's effectiveness, the simulation results are compared with the results of conventional controllers. The results demonstrate the effectiveness of the PAPIDfc scheme to control a grid-supporting inverter of the battery in the reduction of frequency fluctuations.

  20. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    Science.gov (United States)

    Ajay Kumar, M.; Srikanth, N.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.