Fuzzy Learning Control Research Articles

Synthesis of Neuro-Fuzzy Controller for Dynamic Objects under Conditions of Uncertainty

Consider the problem of synthesis of fuzzy controller having adaptive properties of dynamical systems operating in conditions of uncertainty performance of facility management and environmental parameters. Provide a given quality of regulation in the control loop is enabled the function generator, and to bring those adaptive traits - being assessed the rate of change of the loop error. For learning fuzzy controller with a function generator algorithm is proposed to use the interactive adaptation. Calculation formulas for determining the weights of synapses so that when you spent training a neural network to reduce the time of regulation and deregulation in the system. After selecting the membership function and carrying out mathematical modeling of the process control system, there is the emergence of insensitivity to change the duration of the transition process and improving the quality of the transition process.

Fuzzy iterative learning control based on genetic algorithm

In order to improve the control precision and to speed up the convergence rate of the controlled system,a kind of fuzzy PD type iterative learning control algorithm was put forward based on genetic algorithm.In the proposed approach,the iterative learning controller was designed by fuzzy Takagi-Sugeno-Kang(TSK) system,the parameters of fuzzy TSK system were calculated by genetic algorithm,and then appropriate updating law was created.Appropriate iterative learning control algorithm of controlled system was designed and compared with PD iterative learning control algorithm and fuzzy PID iterative learning control algorithm,and then the proposed algorithm was used in double joint manipulator simulation.The simulation results show the effectiveness of the proposed algorithm.

Fuzzy Control Technique of Auxiliary Ventilation in Heading Laneway

Aimed at air quantity requirement and energy conservation at normal ventilation as well as safety and quickly at methane discharging, this study discussed the design process of self learning fuzzy controller based on basic fuzzy model. Firstly, the control system block is described. Then basic fuzzy model is introduced. Auxiliary ventilation is time-varying parameters, nonlinear and large hysteresis system. Therefore, the design includes the normalization, reference model, time-delay, sample cycle, calculation method and control rule modification. The partial fan speed adjustment adopts Direct Torque Control (DTC). Based on theoretical analysis and simulation, combing DSP chip with Intelligent Power Module (IPM), auxiliary ventilation fuzzy control adjusting speed experiment system is established. Experimental results have shown that the system can safely and efficiently fulfill the function of normal ventilation and methane discharging.

Fuzzy SVM learning control system considering time properties of biped walking samples

To learn biped walking dynamics accurately, and then compensate time-varying external disturbances timely, a time-sequence-based fuzzy SVM (TSF-SVM) learning control system considering time properties of biped walking samples is proposed. For the first time, time-sequence-based triangular and Gaussian fuzzy membership functions have been proposed for the single support phase (SSP) and the double support phase (DSP), respectively, according to time properties of different biped phases, which provides an effective way to formulate time properties of biped walking samples in the context of time-varying external disturbances. In addition, a time-sequence-based moving learning window (TS-MLW) is proposed for online training of the proposed TSF-SVM. The performance of the proposed TSF-SVM is compared with other typical intelligent methods; simulation results demonstrate that the proposed method is more sensitive to occasional external disturbances, which increases the stability margin and prevents the robot from falling down.

Learning fuzzy controller and extended Kalman filter for sensorless induction motor robust against resistance variation

This paper presents a new sensorless vector controlled induction motor drive robust against rotor resistance variation. Indeed, the speed and rotor resistance are estimated using extended Kalman filter (EKF). Then, we introduce a new fuzzy logic speed controller based on learning by minimizing cost function. This strategy is based on a topology control self-organized and an algorithm for modifying the knowledge base of fuzzy corrector. The learning mechanism addresses the consequences of corrector rules, which are modified according to the comparison between the current speed of machine and an output signal or a desired trajectory. Thus, fuzzy associative memory is constructed to meet the criteria imposed in problems either control or pursuit. The consequent algorithm updating consists of a regulator mechanism allowing a fast and robust learning without unnecessarily compromising the control signal and steady-state performance. The performance of this new strategy is satisfactory, even in the presence of noise or when there are variations in the parameters of induction motor drive.

A Self Learning Fuzzy Logic Controller for Ship Steering System

A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high efficiency of transportation, the convenience of manoeuvring ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.

SynRM 드라이브의 고성능 제어를 위한 RFNN 제어기 설계

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

유도전동기 드라이브의 DTC를 위한 하이브리드 퍼지제어기

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

다중 AFLC를 이용한 유도전동기 드라이브의 ANN 회전자저항 추정

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

Learning and training techniques in fuzzy control for energy efficiency in buildings

A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain. © 2011 The Author. Published by Oxford University Press. All rights reserved.

다중 AFLC를 이용한 SynRM 드라이브의 효율 최적화 제어

SynRM 효율최적화 제어는 다른 교류전동기에 비해 SynRM의 효율이 낮기 때문에 에너지 절약과 환경보존의 관점에서 매우 중요하다. 본 논문에서는 다중 AFLC를 이용하여 철손을 고려한 SynRM의 새로운 효율 최적화 제어를 제안하였다. 최대효율에서 SynRM을 구동하기 위해 토크전류와 여자전류사이의 최적 전류비를 분석하여 구한다.BR 본 논문에서는 동손과 철손을 최소로 하는 SynRM의 효율 최적화 제어를 제안하였다. 특정한 모터토크를 제공하는 d 축과 q 축 전류의 다양한 조합이 존재한다. 효율 최적화의 목적은 정상상태에서 최소 손실을 제공하는 d 축과 q 축 전류의 조합을 찾는 것이며, 제안된 제어기의 제어 성능은 다양한 동작조건의 분석을 통해 평가되었다. 분석된 결과는 제안된 알고리즘의 타당성을 입증한다.

Efficiency Optimization Control of IPMSM Drive using Multi AFLC

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems

Service robots will play an increasing and more important role in the society in the next years. One of the main challenges is to endow robots with enough autonomy to operate on real environments. To reach that goal, the design of controllers to solve simple tasks must be automatized. Engineers look for learning algorithms that are general, robust, require low expertise knowledge, and generate controllers that can run on the real robot without any tuning stage. In this paper, a framework to learn behaviors (controllers) in mobile robotics, fulfilling the previous requirements, has been used. The framework is based on two modules: dataset generation and a data-driven evolutionary-based learning algorithm to obtain fuzzy controllers. Nevertheless, the design of a fuzzy controller still requires the selection of the type of learning algorithm, and also to choose the value of some design parameters. In this paper we present an exhaustive study on a set of evolutionary-based data-driven learning algorithms, for learning fuzzy controllers in mobile robotics, that cover a wide range of the accuracy/interpretability trade-off. The study has also evaluated the influence of the values of all the design parameters over accuracy and interpretability. The objective is to analyze the performance of the different algorithms for the design of behaviors in mobile robotics, and to extract some general rules that can help in the process to design new behaviors. The analysis comprises two different behaviors (wall-following and moving object following) and more than 450 tests, both in simulation and on a Pioneer II AT robot. Results have shown very good performances in complex and realistic conditions for the different combinations of algorithms and parameters.

Self learning fuzzy controllers using iterative learning tuner

This paper describes the design of an adaptive fuzzy controller using iterative learning to tune input membership functions and scaling factor(s). The control scheme consists of a fuzzy controller and learning control laws. People's perception about the meaning of a linguistic variable differs from person to person or even from expert to expert. This difference in perception usually leads to different fuzzy control designs. Some where within these designs lays the required design which meets a specific performance criterion. This paper proposes an approach to tackle this uncertainty in perception, to find the required design using membership function modification. The membership function is adaptively adjusted through iterative learning technique. The results show that the scheme is robust, cost effective and very simple to implement. It makes use of the nonlinearity inherent in the fuzzy systems. This scheme can be used to design fuzzy controllers for different plants by finding the right membership functions to ensure the required design specifications. Designing fuzzy controllers with desired performance specifications is not a trivial task. Even the specification of linguistic variables, key concept in fuzzy system design, can be different from different experts. This scheme tries to fill this gap. Adaptive fuzzy techniques are computationally heavy to implement. The proposed scheme lays out a unique adaptive procedure for designing fuzzy controllers through iterative learning process.

Trajectory tracking for a serial robot manipulator passing through singular configurations based on the adaptive kinematics Jacobian method

This paper discusses the use of artificial neural networks (ANNs) as a method of trajectory tracking control for a robotic system. Using an ANN does not require any prior knowledge of the kinematics model of the system being controlled; the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify an explicit robot system model. In this approach, disadvantages of some schemes such as the fuzzy learning control, for example, have been elevated. Off-line training was performed for a geometric trajectory that is free of obstacles. Studying the kinematics Jacobian of serial manipulators by using ANNs has two problems: one of these is the selection of the appropriate configuration of the network and the other is the generation of a suitable training dataset. In this approach, although this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in the gear train. Then two network configurations were compared to find the best configuration to be used. Finally, the simulation results were verified experimentally using a general six-degree-of-freedom (DOF) serial robot manipulator.

Learning Fuzzy Control of Nonlinear Processes

Due to high nonlinearities and time-varying dynamics of today's control systems fuzzy learning controllers find appliance in practice. The present paper proposes a method for the synthesis of the learning fuzzy controllers where an expert knowledge about a process is applied to form a learning mechanism that is used to acquire information for the knowledge base of the main fuzzy controller. According to the proposed method an expert knowledge is used to describe how the controller should learn to control rather than to control the process. The results of experiments on heating system and level/pressure system prove the practical relevance of the design strategy of a learning fuzzy controller.

Reinforcement learning combined with a fuzzy adaptive learning control network (FALCON-R) for pattern classification

Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster, is able to escape from local minima, and has excellent disturbance rejection capability.

Coordinated secondary voltage control to eliminate voltage violations in power system contingencies

In order to achieve more efficient voltage regulation in a power system, coordinated secondary voltage control has been proposed, bringing in the extra benefit of enhancement of power system voltage stability margin. This paper investigates a new potential application of coordinated secondary voltage control by multiple FACTS voltage controllers in eliminating voltage violations in power system contingencies. The study is presented by the example New England ten-machine power system with two SVCs and two STATCOMs installed. The coordinated secondary voltage control is assigned to the SVCs and STATCOMs in order to eliminate voltage violations in system contingencies. In the paper, it is proposed that the secondary voltage control is implemented by a learning fuzzy logic controller. A key parameter of the controller is trained by P-type learning algorithm via offline simulation with the assistance of injection of artificial loads in controller's adjacent locations. A multiagent collaboration protocol, which is graphically represented as a finite-state machine, is proposed in the paper for the coordination among multiple SVCs and STATCOMs. As an agent, each SVC or STATCOM can provide multilocation coverage to eliminate voltage violations at its adjacent nodes in the power system. Agents can provide collaborative support to each other which is coordinated according to the proposed collaboration protocol.

Learning fuzzy control rules by α‐constrained Simplex method

AbstractThe learning of fuzzy control rules can be defined as a constrained nonlinear optimization problem so that its objective function is indifferentiable. As an optimization method of this problem, “α‐constrained Powell method,” which optimizes a given initial rule, is proposed. However, the difficulty with the learning by the α‐constrained Powell method is that it is necessary to have initial rules of certain accuracy formulated by human experts, thus requiring a very large number of control experiments. In this study, we propose an α‐constrained Simplex method, in which the α‐level comparison is added to the Simplex method, which is a nonlinear optimization method. The α‐constrained Simplex method is an optimization method for constrained problems using the α‐level comparison that takes account of the constraint satisfaction instead of ordinary order relation used in the Simplex method, which is a direct search method based only on values of the objective function. In order to demonstrate the effectiveness of this method, we carried out computer simulation of the fuzzy control rules for a pole‐cart system. We have shown that this method makes it possible to achieve a fast learning of highly accurate rules from mechanically created rules based on a relatively small number of experiments, without relying on any existing rules. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(6): 80–90, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1207

Hybrid Intelligent Control for Ship Steering

Abstract This paper is concerned with the application of hybrid intelligent control techniques for improving the performance of ship steering. Hybrid intelligent controllers can make full use of the advantages of a variety of intelligent algorithms. In this paper, optimization with genetic algorithms is used in off-line learning periods and reinforcement learning and neural fuzzy control are integrated in on-line learning periods. This combination overcomes the need for measurement data as is the case in conventional hybrid intelligent algorithms. According to a new definition of fitness function, it is shown that the optimized result obtained is more suitable to the actual situation. Similarly, simulation results show that our hybrid intelligent controller can effectively improve the ship steering performance in cases where additional sea state disturbances are present. We feel that our hybrid intelligent controller is a promising alternative to conventional autopilots.