Showing papers on "Fuzzy control system published in 1994"
TL;DR: Today, the authors have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience.
Abstract: Prof. Zadeh presented a comprehensive lecture on fuzzy logic, neural networks, and soft computing. In addition, he lead a spirited discussion of how these relatively new techniques may be applied to safety evaluation of time variant and nonlinear structures based on identification approaches. The abstract of his lecture is given as follows.
1,390 citations
01 Jan 1994
TL;DR: The basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system is described.
Abstract: FUZZY sets were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in everyday life. They are a generalization of conventional set theory, one of the basic structures underlying computational mathematics and models. Computational pattern recognition has played a central role in the development of fuzzy models because fuzzy interpretations of data structures are a very natural and intuitively plausible way to formulate and solve various problems. Fuzzy control theory has also provided a wide variety of real, fielded system applications of fuzzy technology. We shall have little more to say about the growth of fuzzy models in control, except to the extent that pattern recognition algorithms and methods described in this book impact control systems. Collected here are many of the seminal papers in the field. There will be, of course, omissions that are neither by intent nor ignorance; we cannot reproduce all of the important papers that have helped in the evolution of fuzzy pattern recognition (there may be as many as five hundred) even in this narrow application domain. We will attempt, in each chapter introduction, to comment on some of the important papers that not been included and we ask both readersmore » and authors to understand that a book such as this simply cannot {open_quotes}contain everything.{close_quotes} Our objective in Chapter 1 is to describe the basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system.« less
812 citations
TL;DR: A fuzzy controller is designed which guarantees stability of the control system under a condition and the simulation results show that the designed fuzzy controller smoothly achieves backing up control of the truck-trailer from all initial positions.
Abstract: A robust stabilization problem for fuzzy systems is discussed in accordance with the definition of stability in the sense of Lyapunov. We consider two design problems: nonrobust controller design and robust controller design. The former is a design problem for fuzzy systems with no premise parameter uncertainty. The latter is a design problem for fuzzy systems with premise parameter uncertainty. To realize two design problems, we derive four stability conditions from a basic stability condition proposed by Tanaka and Sugeno: nonrobust condition, weak nonrobust condition, robust condition, and weak robust condition. We introduce concept of robust stability for fuzzy control systems with premise parameter uncertainty from the weak robust condition. To introduce robust stability, admissible region and variation region, which correspond to stability margin in the ordinary control theory, are defined. Furthermore, we develop a control system for backing up a computer simulated truck-trailer which is nonlinear and unstable. By approximating the truck-trailer by a fuzzy system with premise parameter uncertainty and by using concept of robust stability, we design a fuzzy controller which guarantees stability of the control system under a condition. The simulation results show that the designed fuzzy controller smoothly achieves backing up control of the truck-trailer from all initial positions. >
773 citations
Book•
01 Jan 1994TL;DR: This book provides a unified description of several adaptive neural and fuzzy networks and introduces the associative memory class of systems - which describe the similarities and differences existing between fuzzy and neural algorithms.
Abstract: This book provides a unified description of several adaptive neural and fuzzy networks and introduces the associative memory class of systems - which describe the similarities and differences existing between fuzzy and neural algorithms. Three networks are described in detail - the Albus CMAC, the B-spline network and a class of fuzzy systems - and then analysed, their desirable features (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasised and the algorithms are all evaluated on a common time series problem and applied to a common ship control benchmark. Chapters: 1. An Introduction to Learning Modelling and Control 1.1 Preliminaries 1.2 Intelligent Control 1.3 Learning Modelling and Control 1.4 Artificial Neural Networks 1.5 Fuzzy Control Systems 1.6 Book Description 2. Neural Networks for Modelling and Control 2.1 Introduction 2.2 Neuromodelling and Control Architectures 2.3 Neural Network Structure 2.4 Training Algorithms 2.5 Validation of a Neural Model 2.6 Discussion 3. Associative Memory Networks 3.1 Introduction 3.2 A Common Description 3.3 Five Associative Memory Networks 3.4 Summary 4. Adaptive Linear Modelling 4.1 Introduction 4.2 Linear Models 4.3 Performance of the Model 4.4 Gradient Descent 4.5 Multi-Layer Perceptrons and Back Propagation 4.6 Network Stability 4.7 Conclusion 5. Instantaneous Learning Algorithms 5.1 Introduction 5.2 Instantaneous Learning Rules 5.3 Parameter Convergence 5.4 The Effects of Instantaneous Estimates 5.5 Learning Interference in Associative Memory Networks 5.6 Higher Order Learning Rules 5.7 Discussion 6. The CMAC Algorithm 6.1 Introduction 6.2 The Basic Algorithm 6.3 Adaptation Strategies 6.4 Higher Order Basis Functions 6.5 Computational Requirements 6.6 Nonlinear Time Series Modelling 6.7 Modelling and Control Applications 6.8 Conclusions 7. The Modelling Capabilities of the Binary CMAC 7.1 Modelling and Generalisation in the Binary CMAC 7.2 Measuring the Flexibility of the Binary CMAC 7.3 Consistency Equations 7.4 Orthogonal Functions 7.5 Bounding the Modelling Error 7.6 Investigating the CMAC's Coarse Coding Map 7.7 Conclusion 8. Adaptive B-spline Networks 8.1 Introduction 8.2 Basic Algorithm 8.3 B-spline Learning Rules 8.4 B-spline Time Series Modelling 8.5 Model Adaptation Rules 8.6 ASMOD Time Series Modelling 8.7 Discussion 9. B-spline Guidance Algorithms 9.1 Introduction 9.2 Autonomous Docking 9.3 Constrained Trajectory Generation 9.4 B-spline Interpolants 9.5 Boundary and Kinematic Constraints 9.6 Example: A Quadratic Velocity Interpolant 9.7 Discussion 10. The Representation of Fuzzy Algorithms 10.1 Introduction: How Fuzzy is a Fuzzy Model? 10.2 Fuzzy Algorithms 10.3 Fuzzy Sets 10.4 Logical Operators 10.5 Compositional Rule of Inference 10.6 Defuzzification 10.7 Conclusions 11. Adaptive Fuzzy Modelling and Control 11.1 Introduction 11.2 Learning Algorithms 11.3 Plant Modelling 11.4 Indirect Fuzzy Control 11.5 Direct Fuzzy Control References. Appendix A. Modified Error Correction Rule Appendix B. Improved CMAC Displacement Tables Appendix C. Associative Memory Network Software Structure C.1 Data Structures C.2 Interface Functions C.3 Sample C Code Appendix D. Fuzzy Intersection Appendix E. Weight to Rule Confidence Vector Map For further information about this book (mailing/shipping costs etc.) and other neurofuzzy titles in the Prentice Hall series please contact: LIZ DICKINSON Prentice Hall Paramount Publishing International Campus 400 Maylands Avenue Hemel Hempstead, HP2 7EZ United Kingdom Tel: 0442 881900 Fax: 0442 257115 Contents
683 citations
Book•
01 Jan 1994
TL;DR: A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications.
Abstract: From the Publisher:
The strength of this book lies in its clear and precise examination of the theory of fuzzy systems. A rigorous study of the principles of fuzzy set theory supports the book's fundamental aim, which is to promote the development of fuzzy systems for successful real-world applications. The authors highlight two important application areas: approximate reasoning in knowledge-based systems, and fuzzy control. Reflecting the state of the art in fuzzy systems research, the book is both comprehensive and practical in its approach. Its illustration of key concepts is based on a detailed analysis of the underlying semantics. Each chapter is enhanced by useful historical notes and extensive references. The book presents several industrial case studies and exercises designed to increase its appeal to advanced students and researchers in computer science, applied mathematics and engineering.
587 citations
TL;DR: The similarity between fuzzy systems and mathematical approximation is discussed and an idea to improve approximation accuracy is suggested based on uniform approximation bounds.
Abstract: In this paper, the approximation properties of MIMO fuzzy systems generated by the product inference are discussed. We first give an analysis of fuzzy basic functions (FBF's) and present several properties of FBF's. Based on these properties of FBF's, we obtain several basic approximation properties of fuzzy systems: 1) basic approximation property which reveals the basic approximation mechanism of fuzzy systems; 2) uniform approximation bounds which give the uniform approximation bounds between the desired (control or decision) functions and fuzzy systems; 3) uniform convergent property which shows that fuzzy systems with defined approximation accuracy can always be obtained by dividing the input space into finer fuzzy regions; and 4) universal approximation property which shows that fuzzy systems are universal approximators and extends some previous results on this aspect. The similarity between fuzzy systems and mathematical approximation is discussed and an idea to improve approximation accuracy is suggested based on uniform approximation bounds. >
418 citations
TL;DR: The approximation capability to capture the fast changing system dynamics is enhanced and the range of the applicability of the method presented by Su et al. can be broadened.
Abstract: An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm is established in the Lyapunov sense, with tracking errors converging to a neighborhood of zero. Simulation results for an unstable nonlinear plant are included to demonstrate that incorporating the linguistic fuzzy information from human experts results in superior tracking performance. >
353 citations
TL;DR: The performance and the robustness of this kind of FC stems from their property of driving the system into the sliding mode (SM), in which the controlled system is invariant to parameter fluctuations and disturbances.
295 citations
TL;DR: The design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller, derived from the conventional continuous-time linear PD controller, and the fuzzification, control-rule base, and defuzzification in the design are discussed in detail.
Abstract: This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PD controller, which has the same linear structure in the proportional and the derivative parts but has nonconstant gains: both the proportional and derivative gains are nonlinear functions of the input signals. The new fuzzy PD controller thus preserves the simple linear structure of the conventional PD controller yet enhances its self-tuning control capability. Computer simulation results have demonstrated this advantage of the fuzzy PD controller, particularly when the process to be controlled is nonlinear. After a detailed stability analysis, where a simple and realistic sufficient condition for the bounded-input/bounded-output stability of the overall feedback control system was derived, several computer simulation results are compared with the conventional PD controller. Although the conventional and fuzzy PD controllers are not exactly comparable, the authors compare them in order to have a sense of how well the fuzzy PD controller performs. For this reason, in the simulations several first-order and second-order linear systems, with or without time-delays, are first used to test the performance of the fuzzy PD controller for step reference inputs: the fuzzy PD control systems show remarkable performance, as well as (if not better than) the conventional PD control systems. Moreover, the fuzzy PD controller is compared to the conventional PD controller for a particular second-order linear system, showing the advantage of the fuzzy PD controller over the conventional one in the sense that in order to obtain the same control performance the conventional PD controller has to employ an extremely large gain while the fuzzy controller uses a reasonably small gain. Finally, in the case of nonlinear systems, the authors provide some examples to show that the fuzzy PD controller can track the set-points satisfactorily but the conventional PD controller cannot. >
TL;DR: Born in the United States around 1965, fuzzy set theory has grown to become a major scientific domain collectively referred to in this article as «fuzzy systems,» which include fuzzy sets, logic, algorithms, and control.
Abstract: Born in the United States around 1965, fuzzy set theory has grown to become a major scientific domain collectively referred to in this article as «fuzzy systems,» which include fuzzy sets, logic, algorithms, and control. For the past few years, particularly in japan, approximately 1,000 commercial and industrial fuzzy systems have been successfully developed. The number of industrial and commercial applications worldwide appears likely to increase significantly in the neat future (see Table 1) [6, 18]. Interest in the U.S. and other countries has also been growing recently, as indicated by both the first IEEE conference on fuzzy systems held in March 1992 and the first IEEE Transactions on Fuzzy Systems, which premiered in February 1993 (see Table 2)
TL;DR: The new method employed to identify the rule relevant nodes before the rules are extracted makes FuNe I suitable for applications with large number of inputs, and optimization of the knowledge base in possible including the tuning of membership functions.
20 Jun 1994
TL;DR: In this article, a new maximum power point tracker (MPPT) using fuzzy set theory is proposed to improve the energy conversion efficiency of photovoltaic systems, where a fuzzy algorithm based on linguistic rules describing the operator's control strategy is applied to control the step-up converter for the MPPT.
Abstract: Studies on photovoltaic systems are increasing because of a large, secure, essentially exhaustible and broadly available resource as a future energy supply. However, the output power induced in the photovoltaic modules is influenced by an intensity of solar cell radiation, temperature of the solar cells and so on. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the input source. In this paper, a new maximum power point tracker (MPPT) using fuzzy set theory is proposed to improve energy conversion efficiency. A fuzzy algorithm based on linguistic rules describing the operator's control strategy is applied to control the step-up converter for the MPPT. Fuzzy logic control based on coarse and fine mode has been incorporated in order to reduce not only the time required to track the maximum power point but also the fluctuation of power. The MPPT algorithm is implemented by a 16 bit single chip 80C196KB microcontroller. Simulation and experimental results show that performance of the fuzzy controller in a maximum power tracking of a photovoltaic array is better than that of controller based upon the hill climbing method. >
20 Jun 1994
TL;DR: In this article, a method for the estimation of changes in stator resistance during the operation of the induction machine is presented, which is implemented using proportional integral control and fuzzy logic control schemes.
Abstract: Direct torque control (DTC) of induction machines uses the stator resistance of the machine for estimation of the stator flux. Variations of stator resistance due to changes in temperature or frequency make the operation of DTC difficult at low speeds. A method for the estimation of changes in stator resistance during the operation of the machine is presented. The estimation method is implemented using proportional-integral (PI) control and fuzzy logic control schemes. The estimators observe the machine stator current vector to detect the changes in stator resistance. The performance of the two methods are compared using simulation and experimental results. Results obtained have shown improvement in DTC at low speeds.
26 Jun 1994
TL;DR: An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible.
Abstract: An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm is established in the Lyapunov sense, with tracking errors converging to a neighborhood of zero. Simulation results for an unstable nonlinear plant are included to demonstrate that incorporating the linguistic fuzzy information from human experts results in superior tracking performance. >
TL;DR: A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed, and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty, which may include modeling error and disturbances.
Abstract: A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discrete-time nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried out, and the main result is that the closed loop is globally stable and robust with respect to unstructured uncertainty, which may include modeling error and disturbances. In addition, bounds on the asymptotic and transient performance are given. The main assumptions on the system and model are that they must not have strong nonminimum-phase effects, except time-delay, and the unstructured uncertainty must not be too large. A simulation example illustrates some of the properties of the modeling method and model based control structure. >
TL;DR: An improved technique of hybrid modelling biochemical production processes is described, composed of a set of dynamical differential equations, an artificial neural network and a fuzzy expert system, demonstrating the applicability of a hybrid model for state estimation, prediction, feed rate optimization, and process control.
Book•
01 Jun 1994
TL;DR: A complete, step-by-step guide to developing fuzzy and neural applications; applications for financial forecasting, modeling of chaotic dynamics, pattern classification, and control of unstable systems; provides a wealth of sample code for all applications; critically evaluates how well each application works; and features a guide to creating a complete object-oriented interactive interface framework through which all applications can be run.
Abstract: From the Publisher:
Many books discuss the theory of neural and fuzzy systems, but this is the only one that gives you everything you need to actually design and implement neural and fuzzy programs for real-world scientific, engineering, and financial applications. Each chapter is self-contained and takes the reader through all the steps - from data preparation to the presentation of results - necessary to develop a complete working application, many of which feature interactive graphics. In addition to basics such as backpropagation for feedforward networks, the book also covers a number of advanced methods, including genetic algorithms, simulated annealing, and conjugate gradient methods. A complete, step-by-step guide to developing fuzzy and neural applications; applications for financial forecasting, modeling of chaotic dynamics, pattern classification, and control of unstable systems; provides a wealth of sample code for all applications; critically evaluates how well each application works; and features a guide to creating a complete object-oriented interactive interface framework through which all applications can be run.
TL;DR: Algorithms for constructing fuzzy rules from input-output training data, which require only a single pass through the training set, are examined to produce a computationally efficient method of learning.
Abstract: Fuzzy inference systems and neural networks both provide mathematical systems for approximating continuous real-valued functions. Historically, fuzzy rule bases have been constructed by knowledge acquisition from experts while the weights on neural nets have been learned from data. This paper examines algorithms for constructing fuzzy rules from input-output training data. The antecedents of the rules are determined by a fuzzy decomposition of the input domains. The decomposition localizes the learning process, restricting the influence of each training example to a single rule. Fuzzy learning proceeds by determining entries in a fuzzy associative memory using the degree to which the training data matches the rule antecedents. After the training set has been processed, similarity to existing rules and interpolation are used to complete the rule base. Unlike the neural network algorithms, fuzzy learning algorithms require only a single pass through the training set. This produces a computationally efficient method of learning. The effectiveness of the fuzzy learning algorithms is compared with that of a feedforward neural network trained with back-propagation. >
TL;DR: A self-organizing fuzzy controller to augment a sliding-mode control (SOFSMC) scheme for a class of nonlinear systems and the results show that both alleviation of chatter and robust performance are achieved.
Abstract: A self-organizing fuzzy controller to augment a sliding-mode control (SOFSMC) scheme for a class of nonlinear systems is proposed. The motivation behind this scheme is to combine the best features of self-organizing fuzzy control and sliding-mode control to achieve rapid and accurate tracking control of a class of nonlinear systems. The chatter encountered by most sliding-mode control schemes is greatly alleviated without sacrificing invariant properties. A stability analysis is presented; the design guidelines and the class of applicable systems are clearly identified. To verify the scheme, the authors performed experiments on its implementation in a magnetic levitation system. The results show that both alleviation of chatter and robust performance are achieved; the advantages of the scheme are indicated in comparison with the conventional sliding-mode design. >
02 Oct 1994
TL;DR: It is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously, and fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method.
Abstract: A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method. >
TL;DR: A multiregion fuzzy logic controller is proposed for nonlinear process control using an auxiliary process variable to detect the process operating regions and can give satisfactory performance in all regions.
Abstract: Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is divided into fuzzy regions such as high-gain, low-gain, large-time-constant, and small-time-constant. Then a fuzzy controller is designed based on the regional information. Using an auxiliary process variable to detect the process operating regions, the resulting multiregion fuzzy logic controller can give satisfactory performance in all regions. Rule combination and controller tuning are discussed. Application of the controller to pH control is demonstrated. >
Book•
01 Jan 1994
TL;DR: A novel two-layered fuzzy logic controller for controlling systems with deadzones that exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers and is robust to variations in deadzone nonlinearities.
Abstract: Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual "fuzzy PD" controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, 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 followed by a usual fuzzy PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples. >
TL;DR: The experimental test results of the FLCs, which are designed based on an implicit model of the vehicle, are shown, and a comparison is made to similar tests conducted using the frequency shaped LQ controller as well as PID controller.
Abstract: A fuzzy logic controller (FLC) is designed and implemented in real time on a Toyota Celica test vehicle to achieve control of the lateral motion of the vehicle. The structure of FLC is modularized as feedback, preview, and gain scheduling rule bases. The parameters of FLC are tuned manually using information from the characteristics of human driving operation and existing controllers. Three feedback FLCs with different feedback variables are designed. A fuzzy preview rule base is developed to utilize preview information regarding the upcoming radii of curvature. Also, a gain scheduling rule base is designed to choose the appropriate controller based on the velocity of the vehicle. These fuzzy logic control strategies are implemented on the test vehicle which follows automatically a multiple curved track using discrete magnetic markers on the roadway and magnetometers on the vehicle as a lateral error reference/sensing system. The experimental test results of the FLCs, which are designed based on an implicit model of the vehicle, are shown, and a comparison is made to similar tests conducted using the frequency shaped LQ controller as well as PID controller, both are designed based on an explicit model of the vehicle. >
26 Jun 1994
TL;DR: This paper proposes a new type of architecture called recurrent fuzzy system together with a learning algorithm for adapting the membership functions and aims at extending the fuzzy controllers approximation capacity to dynamic processes of unknown order.
Abstract: Besides their linguistic interface, we believe fuzzy controllers not only to be universal approximators but also more general and efficient than their similar neural counterparts: radial basis functions. Consequently like recurrent neural networks, this paper aims at extending the fuzzy controllers approximation capacity to dynamic processes of unknown order. We propose a new type of architecture called recurrent fuzzy system together with a learning algorithm for adapting the membership functions. >
TL;DR: A real-world example of designing a nonlinear fuzzy controller to regulate blood pressure in post-surgical patients is given to demonstrate the practicality and effectiveness of the design procedure.
TL;DR: In this paper, the authors present a method for creating and validating a nonlinear controller by the composition of heterogeneous local control laws appropriate to different operating regions, which can be analyzed by a combination of classical and qualitative methods.