Showing papers on "Fuzzy control system published in 1997"

Fuzzy Control

Abstract: From the Publisher: Fuzzy control is emerging as a practical alternative to conventional methods of solving challenging control problems. Written by two authors who have been involved in creating theoretical foundations for the field and who have helped assess the value of this new technology relative to conventional approaches, Fuzzy Control is filled with a wealth of examples and case studies on design and implementation. Computer code and MATLAB files can be downloaded for solving the book's examples and problems and can be easily modified to implement the reader's own fuzzy controllers or estimators. Drawing on their extensive experience working with industry on implementations, Kevin Passino and Stephen Yurkovich have written an excellent hands-on introduction for professionals and educators interested in learning or teaching fuzzy control.

Fuzzy and neural approaches in engineering

Abstract: From the Publisher: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.

Multiple Model Approaches to Modelling and Control

Abstract: 1. Basic Principles: The Operating Regime Approach 2. Modelling: Fuzzy Set Methods for Local Modelling Identification 3. Modelling of Electrically Stimulated Muscle 4. Process Modelling Using a Functional State Approach 5. Markov Mixtures of Experts 6. Active Learning With Mixture Models 7. Local Learning in Local Model Networks 8. Side Effects of Normalising Basic Functions 9. Control: Heterogeneous Control Laws 10. Local Laguerre Models 11. Multiple Model Adaptive Control 12. H Control Using Multiple Linear Models 13. Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno Fuzzy Models

Foundations of neuro-fuzzy systems

Abstract: From the Publisher: Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.

Are artificial neural networks black boxes

Abstract: Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

A new approach to fuzzy modeling

Abstract: This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm.

Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications

Abstract: From the Publisher: Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background.Sponsored by:IEEE Neural Networks Council

General-purpose fuzzy controller for DC-DC converters

Abstract: In this paper, a general-purpose fuzzy controller for DC-DC converters is investigated. Based on a qualitative description of the system to be controlled, fuzzy controllers are capable of good performances, even for those systems where linear control techniques fail, e.g., when a mathematical description is not available or is in the presence of wide parameter variations. The presented approach is general and can be applied to any DC-DC converter topologies. Controller implementation is relatively simple and can guarantee a small-signal response as fast and stable as other standard regulators and an improved large-signal response. Simulation results of buck-boost and Sepic converters show control potentialities.

Implementation of a fuzzy controller for DC-DC converters using an inexpensive 8-b microcontroller

Abstract: This paper presents an implementation of a fuzzy controller for DC-DC power converters using an inexpensive 8-bit microcontroller. An "on-chip" analog-to-digital (A/D) converter and PWM generator eliminate the external components needed to perform these functions. Implementation issues include limited on-chip program memory of 2 kB, unsigned integer arithmetic and computational delay. The duty cycle for the DC-DC power converter can only be updated every eight switching cycles because of the time required for the A/D conversion and the control calculations. However, it is demonstrated here that stable responses can be obtained for both buck and boost power converters under these conditions. Another important result is that the same microcontroller code, without any modifications, can control both power converters because their behavior can be described by the same set of linguistic rules. The contribution shows that a nonlinear controller such as fuzzy logic can be inexpensively implemented with microcontroller technology.

Environmental/economic dispatch using fuzzy logic controlled genetic algorithms

Abstract: The paper presents the application of a fuzzy logic controlled genetic algorithm (FCGA) to power system environmental/economic dispatch. The authors first propose an improved genetic algorithm with two fuzzy controllers based on some heuristics to adaptively adjust the crossover probability and mutation rate during the optimisation process. The implementation of the fuzzy crossover and mutation controllers is described. The proposed FCGA can be applied to wide range of optimisation problems. The validity of the proposed algorithm is illustrated on environmental/economic dispatch of a six-generator power system. Its performance is compared with conventional GAs and the Newton-Raphson method. The results are very encouraging.

Size reduction by interpolation in fuzzy rule bases

Abstract: Fuzzy control is at present still the most important area of real applications for fuzzy theory. It is a generalized form of expert control using fuzzy sets in the definition of vague/linguistic predicates, modeling a system by If...then rules. In the classical approaches it is necessary that observations on the actual state of the system partly match (fire) one or several rules in the model (fired rules), and the conclusion is calculated by the evaluation of the degrees of matching and the fired rules. Interpolation helps reduce the complexity as it allows rule bases with gaps. Various interpolation approaches are shown. It is proposed that dense rule bases should be reduced so that only the minimal necessary number of rules remain still containing the essential information in the original base, and all other rules are replaced by the interpolation algorithm that however can recover them with a certain accuracy prescribed before reduction. The interpolation method used for demonstration is the Lagrange method supplying the best fitting minimal degree polynomial. The paper concentrates on the reduction technique that is rather independent from the style of the interpolation model, but cannot be given in the form of a tractable algorithm. An example is shown to illustrate possible results and difficulties with the method.

A comparison of techniques for developing predictive models of software metrics

Abstract: The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.

Robust flight control : a design challenge

Abstract: Multi-objective parameter synthesis (MOPS).- Eigenstructure assignment.- Linear quadratic optimal control.- Robust quadratic stabilization.- H ? mixed sensitivity.- H ? loop-shaping.- ?-Synthesis.- Nonlinear dynamic inversion control.- Robust inverse dynamics estimation.- A model following control approach.- Predictive control.- Fuzzy logic control.- The RCAM design challenge problem description.- The classical control approach.- Multi-objective parameter synthesis (MOPS).- An Eigenstructure Assignment approach (1).- An Eigenstructure Assignment approach (2).- A model multi-model approach.- The Lyapunov approach.- An H ? approach.- A ?-synthesis approach (1).- A ?-synthesis approach (2).- Autopilot design based on the Model Following Control approach.- Flight management using predictive control.- A fuzzy control approach.- The HIRM design challenge problem description.- Design via LQ methods.- The H ? loop-shaping approach.- Design of stability augmentation system using ?-synthesis.- Design of a robust, scheduled controller using ?-synthesis.- Nonlinear dynamic inversion and LQ techniques.- The Robust Inverse Dynamics Estimation approach.- The industrial view.- Another view on the Design Challenge achievements.- Concluding remarks.

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

Abstract: To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

Abstract: This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.

Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

Abstract: This paper presents the design and experiment of a fuzzy proportional integral derivative (PID) controller for a flexible-joint robot arm with uncertainties from time-varying loads. Experimental results have shown remarkable tracking performance of this fuzzy PID controller, and have convincingly demonstrated that fuzzy logic control can be used for flexible-joint robot arms with uncertainties and it is quite robust. In this paper, the fuzzy PID controller is first described briefly, using a simple and practical PD+I controller configuration. This configuration preserves the linear structure of the conventional PD+I controller, but has nonconstant gains: the proportional, integral, and derivative gains are nonlinear functions of their input signals, which have self-tuning (adaptive) capabilities in set-point tracking performance. Moreover, these variable gains make the fuzzy PID controller robust with faster response time and less overshoot than its conventional counterpart. The proposed design was tested using a flexible-joint robot arm driven by a DC motor in a laboratory, where the arm was experienced with time-varying loads. Control performance by the conventional and fuzzy PID controllers for such a laboratory robotic system are both included in this paper for comparison.

Applications of Continuous Mathematics to Computer Science

Abstract: Preface. 1. Algorithm Complexity: Two Simple Examples. 2. Solving General Linear Functional Equations: An Application to Algorithm Complexity. 3. Program Testing: A Problem. 4. Optimal Program Testing. 5. Optimal Choice of a Penalty Function: Simplest Case of Algorithm Design. 6. Solving General Linear Differential Equations with Constant Coefficients: An Application to Constrained Optimization. 7. Simulated Annealing: 'Smooth' (Local) Discrete Optimization. 8. Genetic Algorithms: 'Non-Smooth' Discrete Optimization. 9. RISC Computer Architecture and Internet Growth: Two Applications of Extrapolation. 10. Systems of Differential Equations and Their Use in Computer-Related Extrapolation Problems. 11. Network Congestion: An Example of Non-Linear Extrapolation. 12. Neural Networks: A General Form of Non-Linear Extrapolation. 13. Expert Systems and the Basics of Fuzzy Logic. 14. Intelligent and Fuzzy Control. 15. Randomness, Chaos, and Fractals. A: Simulated Annealing Revisited. B: Software Cost Estimation. C: Electronic Engineering: How to Describe PN-Junctions. D: Log-Normal Distribution Justified: An Application to Computational Statistics. E: Optimal Robust Statistical Methods. F: How to Avoid Paralysis of Neural Networks. G: Estimating Computer Prices. H: Allocating Bandwidth on Computer Networks. I: Algorithm Complexity Revisited. J: How Can a Robot Avoid Obstacles: Case Study of Real-Time Optimization. K: Discounting in Robot Control: A Case Study of Dynamic Optimization. Index.

Design and experience with a fuzzy logic controller for automatic generation control (AGC)

Abstract: This paper describes the design, implementation and operational performance of a fuzzy controller as part of the automatic generation control (AGC) system in Eskom's National Control Centre. The fuzzy controller was implemented in the control ACE (area control error) calculation, which determines the shortfall or surplus generation that has to be corrected. This paper sets out the problems associated with secondary frequency control and AGC. The difficulties associated with optimising the original standard AGC controller, the design, implementation and optimisation of the fuzzy controller and the operational performance of the new controller over two years, are discussed. The fuzzy controller was integrated into the existing off-the-shelf-AGC system with only a few modifications. The operational performance over two years showed an overall improvement of over 50% in the reduction of control compared to the original AGC controller, and an initial improvement of 20% in the quality of control of the optimised original controller.

Nonsingleton fuzzy logic systems: theory and application

Abstract: In this paper, we present a formal derivation of general nonsingleton fuzzy logic systems (NSFLSs) and show how they can be efficiently computed. We give examples for special cases of membership functions and inference and we show how an NSFLS can be expressed as a "nonsingleton fuzzy basis function" expansion and present an analytical comparison of the nonsingleton and singleton fuzzy logic systems formulations. We prove that an NSFLS can uniformly approximate any given continuous function on a compact set and show that our NSFLS does a much better job of predicting a noisy chaotic time series than does a singleton fuzzy logic system (FLS).

Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives

Abstract: Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al automatic fuzzy tuning and its applications, H. Ishigami et al an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al on-line self-structuring fuzzy inference systems for function approximation, H. Bersini fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang intelligent systems for fraud detection, J. Kingdon genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.

Design of a stable fuzzy controller for an articulated vehicle

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

Bi-objective power dispatch using fuzzy satisfaction-maximizing decision approach

Abstract: A fuzzy satisfaction-maximizing decision approach is presented to solve the bi-objective power dispatch problem regarding both minimum fuel cost and minimum environmental impact of NO/sub x/ emission. Based on the fuzzy utility functions of system operators and a fuzzy satisfaction-maximizing scheme, a set of noninferior solutions are obtained. For each noninferior solution achieved, the associated marginal rate of substitution (MRS) is then calculated to help the operators more rationally determine the weight factors of the objectives considered and hence the final best compromise solution. Numerical results of the IEEE 30-bus 6-generator test systems are presented to demonstrate the proposed fuzzy satisfaction-maximizing decision approach.

Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming

Abstract: Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary search technique, evolutionary programming (EP), has been adopted and improved for this particular application. To improve the performance of EP, a fuzzy controlled EP (FCEP), based on heuristic information, is first proposed. The mutation fuzzy controller adaptively adjusts the mutation rate during the simulated evolutionary process. The status of each switch in distribution systems is naturally represented by a binary control parameter 0 or 1. The length of string is much shorter than those proposed by others. A chain-table and combined depth-first and breadth-first search strategy is employed to further speed up the optimisation process. The equality and inequality constraints are imbedded into the fitness function by penalty factors which guarantee the optimal solutions searched by the FCEP are feasible. The implementation of the proposed FCEP for feeder reconfiguration is described in detail. Numerical results are presented to illustrate the feasibility of the proposed FCEP.

A fuzzy logic approach to unit commitment

Abstract: The application of fuzzy logic to the unit commitment problem is demonstrated. This method allows a qualitative description of the behavior of a power system, the system's characteristics, and response without the need for exact mathematical formulations. It is demonstrated, through a numerical example, that a fuzzy-logic-based approach achieves a logical and feasible economical cost of operation of the power system, which is the major objective of unit commitment.

Fuzzy logic in autonomous robotics: behavior coordination

Abstract: Most current architectures for autonomous robots are based on a decomposition of the control problem into small units of control, or behaviors. While this decomposition has a number of advantages, it brings about the problem of having to coordinate the execution of different units in order to obtain a globally coherent behavior. In this paper, we discuss how fuzzy logic can be used, and has been used, to address this problem.

Fuzzy evolutionary computation

Abstract: Editor's Preface. Part 1: Fundamentals. 1.1. Evolutionary Algorithms Z. Michalewicz, et al. 1.2. On the Combination of Fuzzy Logic And Evolutionary Computation: A Short Review and Bibliography O. Cordon, et al. 1.3. Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components M.A. Lee, H. Esbensen. Part 2: Methodology and Algorithms. 2.1. GA Algorithms in Intelligent Robots T. Fukuda, et al. 2.2. Development of If-Then Rules with the Use of DNA Coding T. Furuhashi. 2.3. Genetic-Algorithm-Based Approaches to Classification Problems H. Ishibuchi, et al. 2.4. Multiobjective Fuzzy Satisficing Methods for 0-1 Knapsack Problems Through Genetic Algorithms M. Sakawa, T. Shibano. 2.5. Multistage Evolutionary Optimization of Fuzzy Systems - Application to Optimal Fuzzy Control J. Kacprzyk. 2.6. Evolutionary Learning in Neural Fuzzy Control Systems D.A. Linkens, H.O. Nyongesa. 2.7. Stable Identification and Adaptive Control - A Dynamic Fuzzy Logic System Approach G. Vukovich, J.X. Lee. 2.8. Evolutionary Based Learning of Fuzzy Controllers L. Magdalena, J.R. Velasco. 2.9. GA-Based Generation of Fuzzy Rules O. Nelles. Part 3: Bibliography. 3.1. An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic J.T. Alander. Subject Index.