Showing papers on "Fuzzy control system published in 1993"

ANFIS: adaptive-network-based fuzzy inference system

Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

A fuzzy-logic-based approach to qualitative modeling

Abstract: This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.

An Introduction to Fuzzy Control

Abstract: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

Stable adaptive fuzzy control of nonlinear systems

Abstract: A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided, so that controller designers can determine the bounds based on their requirements. The direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the Duffing chaotic system to track a trajectory. The simulation results show that the controller worked without using any fuzzy control rules, and that after fuzzy control rules were incorporated the adaptation speed became much faster. It is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control. >

Functional equivalence between radial basis function networks and fuzzy inference systems

Abstract: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent. >

Fuzzy gain scheduling of PID controllers

Abstract: This paper describes the development of a fuzzy gain scheduling scheme of PID controllers for process control. Fuzzy rules and reasoning are utilized online to determine the controller parameters based on the error signal and its first difference. Simulation results demonstrate that better control performance can be achieved in comparison with Ziegler-Nichols controllers and Kitamori's PID controllers. >

Fuzzy control of pH using genetic algorithms

Abstract: Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic algorithm (GA) alters the membership functions employed by a conventional FLC, an approach that is contrary to the tactic generally used to provide FLC’s with adaptive capabilities in which the rule set is altered. GA’s are search algorithms based on the mechanics of natural genetics that are able to rapidly locate near-optimal solutions to difficult problems. The Bureau-developed technique is used to produce an adaptive GA-FLC for a laboratory acid-base experiment. Nonlinearities in the laboratory system are associated with the logarithmic pH scale (pH is proportional to the logarithm of HJO’ ions) and changing process dynamics are introduced by altering system parameters such as the desired set point and the concentration and buffering capacity of input solutions. Results indicate that FLC’s augmented with GA’s offer a powerful alternative to conventional process control techniques in the nonlinear, rapidly changing pH systems commonly found in industry.

The paradoxical success of fuzzy logic

Abstract: This paper investigates the question of which aspects of fuzzy logic are essential to its practical usefulness. We show that as a formal system, a standard version of fuzzy logic collapses mathematically to two-valued logic, while empirically, fuzzy logic is not adequate for reasoning about uncertain evidence in expert systems. Nevertheless, applications of fuzzy logic in heuristic control have been highly successful. We argue that the inconsistencies of fuzzy logic have not been harmful in practice because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems.

Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques

Abstract: This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of applications.

Virtual worlds as fuzzy cognitive maps

Abstract: Fuzzy cognitive maps (FCMs) can structure virtual worlds. FCMs link causal events, values, goals, and trends in a fuzzy feedback dynamical system. They direct actors in virtual worlds as the actors react to events and to one another. In nested FCMs each causal concept can control its own FCM. This combines levels of fuzzy systems that can choose goals or move objects. Adaptive FCMs change as causal patterns change. They adapt with differential Hebbian learning. FCMs are applied to an undersea virtual world of dolphins. >

A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control

Abstract: In this tutorial, the utility of a fuzzy system is demonstrated by providing a broad overview, emphasizing analog mode hardware, along with a discussion of the author's original work. First, the difference between deterministic words and fuzzy words is explained as well as fuzzy logic. The description of the system using mathematical equations, linguistic rules, or parameter distributions (e.g., neural networks) is discussed. Fuzzy inference and defuzzification algorithms are presented, and their hardware implementation is discussed. The fuzzy logic controller was used to stabilize a glass with wine balanced on a finger and a mouse moving around a plate on the tip of an inverted pendulum. >

Fuzzy Control Systems

Abstract: Foreword (L.A. Zadeh). Preface (A. Kandel and G. Langholz). General Theory: Learning Algorithms for Neuro-Fuzzy Networks (P.Y. Glorennec). Towards a Unified Theory of Intelligent Autonomous Control Systems (L.J. Kohout). Reasoning by Analogy in Fuzzy Controllers (W. Pedrycz). Information Complexity and Fuzzy Control (A. Ramer and V. Kreinovich). Alternative Structures for Knowledge Representation in Fuzzy Logic Controllers (R.R. Yager). Methodologies and Algorithms: Dynamic Analysis of Fuzzy Logic Control Structures (A. Garcia-Cerezo, A. Ollero, and J. Aracil). Intelligent Fuzzy Controller for Event-Driven, Real Time Systems and its VLSI Implementation (J. Grantner, M. Patyra, and M.S. Stachowicz). Constraint-Oriented Fuzzy Control Schemes for Cart-Pole Systems by Goal Decoupling and Genetic Algorithms (O. Katai, M. Ida, T. Sawaragi, S. Iwai, S. Kohno, and T. Kataoka). A Self Generating and Tuning Method for Fuzzy Modeling Using Interior Penalty Method and Its Application to Knowledge Acquisition of Fuzzy Controller (R. Katayama, Y. Kajitani, and Y. Nishida). Fuzzy Control of VSS Type and Its Robustness (S. Kawaji and N. Matsunaga). The Composition of Heterogeneous Control Laws (B. Kuipers and K. Astrom). Synthesis of Nonlinear Controllers Via Fuzzy Logic (R. Langari). Fuzzy Controls under Product-Sum-Gravity Methods and New Fuzzy Control Methods (M. Mizumoto). Fuzzy Modeling for Adaptive Process Control (Y. Nakamori). Fuzzy Controller with Matrix Representation (M. Nakatsuyama, J.H. Yan, and H. Kaminaga). A Self-Tuning Method of Fuzzy Reasoning by Genetic Algorithm (H. Nomura, I. Hayashi, and N. Wakami). Hybrid Neural-Fuzzy Reasoning Model with Application to Fuzzy Control (D. Park, A. Kandel, and G. Langholz). Learning Fuzzy Control Rules from Examples (S.G. Romaniuk and L.O. Hall). A Computational Approach to Fuzzy Logic Controller Design and Analysis Using Cell State Space Methods (S.M. Smith, B. Nokleby, and D.J. Comer). An Adaptive Fuzzy Control Model Based on Fuzzy Neural Networks (X. Zhang, P. Wang, Z. Shen, and X. Peng). Implementations and Applications: Human Friendly Fuzzy Transportation System (T. Iokibe and T. Kimura). Control of a Chaotic System Using Fuzzy Logic (C.L. Karr and E.J. Gentry). Applications of a Fuzzy Control Technique to Superconducting Actuators Using High-Tc Superconductors (M. Komori and T. Kitamura). A Fuzzy Logic Based Approach to Machine Tool Control Optimization (J.R.A. Lopez, E.A. Gutierrez, and L.C. Rosa). Fuzzy Management of Cache Memories (M.A. Manzoul). Fuzzy Controllers on Semi-Custom VLSI Chips (M.A. Manzoul). General Analysis of Fuzzy-Controlled Phase-Locked Loop (H.N. Teodorescu and A. Brezulianu). A Fuzzy Logic Controller for a Rigid Disk Drive (S. Yoshida). Author's Biographical Information. Index.

Adaptive hierarchical fuzzy controller

Abstract: A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach. >

Readings in Fuzzy Sets for Intelligent Systems

Abstract: From the Publisher: In recent years, fuzzy sets have become an important field, the development of which has been accelerated by the emergence of fuzzy control as a commercially successful methodology. This book makes available significant articles on fuzzy sets related to intelligent systems. The papers in this volume cover fundamental notions in fuzzy sets, fuzzy control, fuzzy logic and approximate reasoning, information processing, decision sciences, connections with operations research, and knowledge acquisition. Each chapter is introduced by the editors, who describe the relevance of each article and provide pointers to other literature and a short list of further readings. This collection will be of interest to researchers and professionals in artificial intelligence, engineering, decision sciences, and other fields concerned with management of uncertainty.

Intelligent Control: Aspects of Fuzzy Logic and Neural Nets

Abstract: Index: 1 An Introduction to Intelligent Control 11 Preliminaries 12 Intelligent Control Requirements and Architectures 13 Approaches to Intelligent Control 14 Knowledge Based Systems 15 Fuzzy Logic 16 Fuzzy Logic in Control 17 Neurocontrollers 18 Higher Level Intelligent Controllers 19 Bibliographical Notes 2 Introductory Fuzzy Logic 21 Fuzzy Sets and Logic 22 Fuzzy Inference and Composition 23 Defuzzification 3 Fuzzy Logic Controller Structure and Design 31 Introduction 32 Applications of Fuzzy Set Theory 33 Fuzzy Logic Controller Structural Issues 34 Design Requirements of Fuzzy Logic Controllers 4 The Static Fuzzy Logic Controller 41 Introduction 42 Controller Design by Verbalisation or Expert Interrogation 43 The Fuzzy PID Controller 44 Parametrically Determined Fuzzy PID Controllers 45 Linguistic Rule Inversion Fuzzy Logic Controllers 46 Cluster Based Fuzzy Logic Controllers 5 Self-Organising Fuzzy Logic Control 51 Introduction 52 Control Rule Base SOFLICs 53 Rule Based SOFLIC Applications 54 Systematic Design of Control Rule Based SOFLIC 6 Indirect Self-Organising Fuzzy Logic Controllers 61 Introduction 62 Self-Organising Fuzzy Models and Predictors 63 Relation Causality Inversion 64 Controller Design 65 Adaptive Fuzzy Controller 66 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 67 Nested and Hybrid Fuzzy Controllers 7 Case Studies of Indirect Adaptive Fuzzy Control 71 Regulation of a Ship's Heading 72 Track Control of a City Bus 73 Autonomous Road Vehicle Control and Guidance 74 Observations on Indirect Fuzzy Adaptive Control 8 Neural Network Approximation Capability for Control and Modelling 81 Introduction 82 Approximation Capability of Artificial Neural Networks 83 Multilayer Perceptrons in Neurocontrol 84 Radial Basis Functions in Modelling and Control 9 The B-spline Neural Network and Fuzzy Logic 91 Introduction 92 Polynomial Basis Functions 93 B-splines for Guidance 94 Multivariate Basis Functions 95 Weighted Adaptation 96 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 97 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 98 Conclusions Appendix: Mathematical Prerequisites A1 Metric Spaces A2 Normed Metric Spaces A3 Algebras A4 Approximation in Normed Spaces Contents

A rule-based fuzzy logic controller for a PWM inverter in a stand alone wind energy conversion scheme

Abstract: The paper presents a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a stand-alone wind energy conversion scheme (SAWECS). The self-excited induction generator used in SAWECS has the inherent problem of fluctuations in the magnitude and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback the variable magnitude, variable frequency voltage at the generator terminals is rectified and the DC power is transferred to the load through a PWM inverter. The objective is to track and extract maximum power from the wind energy system and transfer this power to the local isolated load, This is achieved by using the fuzzy logic controller which regulates the modulation index of the PWM inverter based on the input signals: the power error; and its rate of change. These input signals are fuzzified, that is defined by a set of linguistic labels characterized by their membership functions predefined for each class. Using a set of 49 rules which relate the fuzzified input signals to the fuzzy controller output, fuzzy set theory and associated fuzzy logic operations, the fuzzy controller's output is obtained. The fuzzy set describing the controller's output (in terms of linguistic labels) is defuzzified to obtain the actual analog (numerical) output signal which is then used to control the PWM inverter and ensure complete utilization of the available wind energy. The proposed rule-based fuzzy logic controller is simulated and the results are experimentally verified on a scaled down laboratory prototype of the SAWECS.

On methods for improving performance of PI-type fuzzy logic controllers

Abstract: To improve limitations of fuzzy PI controller especially when applied to high order systems, we propose two types of fuzzy logic controllers that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy PI controllers. The structures of the proposed controller were motivated by the problems of fuzzy PI controllers that they generally give inevitable overshoot when one tries to reduce rise time of response especially when a system of order higher than one is under consideration. Since the undesirable characteristics of the fuzzy PI controller are caused by integrating operation of the controller, even though the integrator itself is introduced to to overcome steady state error in response, we propose two fuzzy controllers that fuzzily clear out integrated quantities according to situation. The first controller determines the fuzzy resetting rate by situations described fuzzily by error and error rate, and the second one by error and control input. The two structures both give reduced rise time as well as small overshoot. >

Receiver operating characteristic laboratory (ROCLAB): Software for developing decision strategies that account for uncertainty

Abstract: Receiver operating characteristic (ROC) methodology evaluates how well a decision strategy classifies retrospective dichotomous or fuzzy events. It also provides a rational basis for designing decision strategies for classifying prospective events. ROCLAB is a public domain software package written in Microsoft QuickBASIC for PC microprocessors that computes ROC functions and their useful derived features for discrete and fuzzy class membership data. Decision strategies that account for uncertainties related to prevalence, false classification costs, and fuzzy class membership are easily constructed with ROCLAB. ROC methodology is explained and ROCLAB features are demonstrated with examples from clinical medicine. >

Fuzzy learning control for antiskid braking systems

Abstract: Although antiskid braking systems (ABS) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades under harsh road conditions (e.g. icy/snowy roads). The use of the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions is proposed. This controller utilizes a learning mechanism that observes the plant outputs and adjusts the rules in a direct fuzzy controller so that the overall system behaves like a reference model characterizing the desired behavior. The performance of the FMRLC-based ABS is demonstrated by simulation for various road conditions (wet asphalt, icy) and transitions between such conditions (e.g. when emergency braking occurs and the road switches from wet to icy or vice versa). >

Fuzzy model reference learning control for cargo ship steering

Abstract: The use of a learning control system to maintain adequate performance of a cargo ship autopilot when there are process disturbances or variations is examined. The objective is to make an initial assessment of what advantages a fuzzy learning control approach has over conventional adaptive control approaches. The simulation results indicate that the fuzzy model reference learning controller (FMRLC) has several potential advantages over model reference adaptive control (MRAC), including improved convergence rates, use of less control energy, enhanced disturbance rejection properties, and lack of dependence on a mathematical model. Using the comparative analysis, the authors discuss how the well-developed concepts in conventional adaptive control can be used to evaluate fuzzy learning control techniques. >

Control of a cement kiln by fuzzy logic

Abstract: By applying the methodology of fuzzy logic the operational experience of manual control can be used as the basis for implementing automatic control strategies. The paper describes the application of fuzzy logic to the computer control of a rotary cement kiln. A special language – Fuzzy Control Language – facilitating computer programming with the relevant control algorithms is outlined. Based on experience gained through fuzzy control on actual operation of cement kilns it is concluded that fuzzy control is a practicable and effective way of increasing the level of coordinative control on multi-variable industrial processes.

Twenty years of fuzzy control: experiences gained and lessons learnt

Abstract: The author examines fuzzy control from its inception to its recent widespread acceptance in industrial use and reflects upon matters such as its earlier dismissal, the reasons for its appeal, the nature of scientific research and engineering innovations. The focus is on rule-based fuzzy controllers. It is argued that the dominant position of analytic control theory prevented fuzzy control from being taken seriously until its increasing application in Japan. Many of the arguments against fuzzy control were framed in the language of that dominant theory and also countered in that language. It is further argued that fuzzy control and allied other techniques such as self-organizing fuzzy control, neural networks, genetic algorithms, and so on, provide an alternate paradigm to the analytic control theory. This paradigm consists of nonanalytic approaches to control and is based on decision-making approaches from artificial intelligence. >

Adaptive fuzzy systems

Abstract: Conventional fuzzy control systems using PID (proportional-integral-derivative) control and their limitations are discussed. Ways to incorporate adaptivity are examined. The functioning of adaptive fuzzy logic and adaptive fuzzy control systems is described. The use of rule weights is explained. >