Showing papers on "Fuzzy control system published in 1999"

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

Abstract: This paper describes an experiment on the “linguistic” synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

Type-2 fuzzy logic systems

Abstract: We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.

Piecewise quadratic stability of fuzzy systems

Abstract: Presents an approach to stability analysis of fuzzy systems. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The approach exploits the gain-scheduling nature of fuzzy systems and results in stability conditions that can be verified via convex optimization over linear matrix inequalities. Examples demonstrate the many improvements over analysis based on a single quadratic Lyapunov function. Special attention is given to the computational aspects of the approach and several methods to improve the computational efficiency are described.

A robust self-tuning scheme for PI- and PD-type fuzzy controllers

Abstract: Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error (e) and change of error (/spl Delta/e) of the controlled variable using the most natural and unbiased membership functions (MFs). The proposed self-tuning technique is applied to both PI- and PD-type FLCs to conduct simulation analysis for a wide range of different linear and nonlinear second-order processes including a marginally stable system where even the well known Ziegler-Nichols tuned conventional PI or PID controllers fail to provide an acceptable performance due to excessively large overshoot. Performances of the proposed self-tuning FLCs are compared with those of their corresponding conventional FLCs in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error and integral-of-time-multiplied absolute error, in addition to the responses due to step set-point change and load disturbance and, in each case, the proposed scheme shows a remarkably improved performance over its conventional counterpart.

Stabilizing controller design for uncertain nonlinear systems using fuzzy models

Abstract: A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

A survey of fuzzy clustering algorithms for pattern recognition. II

Abstract: For pt.I see ibid., p.775-85. In part I an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed on the basis of the existing literature. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms. In this paper, five clustering algorithms taken from the literature are reviewed, assessed and compared on the basis of the selected properties of interest. These clustering models are (1) self-organizing map (SOM); (2) fuzzy learning vector quantization (FLVQ); (3) fuzzy adaptive resonance theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully self-organizing simplified adaptive resonance theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear parodoxical. First, only FLVQ, fuzzy ART, and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG, and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG, and FOSART are considered effective fuzzy clustering algorithms.

Robustness design of nonlinear dynamic systems via fuzzy linear control

Abstract: This study introduces a fuzzy linear control design method for nonlinear systems with optimal H/sup /spl infin// robustness performance. First, the Takagi and Sugeno fuzzy linear model (1985) is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, H/sup /spl infin// performance design can be achieved in nonlinear control systems. In the proposed fuzzy linear control method, the fuzzy linear model provides rough control to approximate the nonlinear control system, while the H/sup /spl infin// scheme provides precise control to achieve the optimal robustness performance. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. In the case that state variables are unavailable, a fuzzy observer-based H/sup /spl infin// control is also proposed to achieve a robust optimization design for nonlinear systems. A simulation example is given to illustrate the performance of the proposed design method.

A control-based middleware framework for quality-of-service adaptations

Abstract: In heterogeneous environments with performance variations present, multiple applications compete for and share a limited amount of system resources and suffer from variations in resource availability. These complex applications are desired to adapt themselves and to adjust their resource demands dynamically. On one hand, current adaptation mechanisms built within an application cannot preserve global properties such as fairness; on the other hand, adaptive resource management mechanisms built within the operating system are not aware of data semantics in the application. In this paper, we present a novel middleware control framework to enhance the effectiveness of quality-of-service (QoS) adaptation decisions by dynamic control and reconfiguration of internal parameters and functionalities of a distributed multimedia application. Our objective is to satisfy both system-wide properties (such as fairness among concurrent applications) and application-specific requirements (such as preserving the critical performance criteria). The framework is modeled by the task control model and the fuzzy control model, based on rigorous results from the control theory, and verified by the controllability and adaptivity of a distributed visual tracking application. The results show validation of the framework, i.e., critical application quality parameters can be preserved via controlled adaptation.

Adaptive fuzzy gain scheduling for load frequency control

Abstract: An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers has been proposed. A Sugeno type fuzzy inference system is used in the proposed controller. The Sugeno type fuzzy inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a very nonlinear system moves around in its operating space. The proposed adaptive controller requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions.

Computing with Words in Information/Intelligent Systems 1

Abstract: M Sugeno: Foreword- Neuro-Fuzzy and Genetic Systems for Computing with Words: S Mitaim, B Kosko: Neural Fuzzy Intelligent Agents S Siekmann, R Neuneier, H-G Zimmermann, R Kruse: Neuro Fuzzy Systems for Data Analysis J Leski, E Czogala: A New Fuzzy Interference System Based on Artificial Neural Network and its Applications O Cordon, A Gonzales, F Herrera, R Perez: Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling- Tools for Linguistic Data Modeling and Analysis: H Lee, H Tanaka: Fuzzy Graphs with Linguistic Input-Outputs by Fuzzy Approximation Models MA Gil, PA Gil, DA Ralescu: Fuzzy Random Variables: Modeling Linguistic Statistical Data- Linguistic Models in System Reliability, Quality Control and Risk Analysis: T Onisawa, A Ohmori: Linguistic Model of System Reliability Analysis P Grzegorzewski, O Hryniewicz: Lifetime Tests for Vague Data C Huang, D Ruan: Systems Analytic Models for Fuzzy Risk Estimation- Linguistic Models in Decision Making, Optimization and Control: H Kiendl: Decision Analysis by Advanced Fuzzy Systems J kacprzyk, H Nurmi, M Fedrizzi: Group Decision Making and a Measure of Consensus under Fuzzy Preferences and a Fuzzy Linguistic Majority S Chanas, D Kuchta: Linear Programming with Words JJ Buckley, T Feuring: Computing with Words in Control R Kowalczyk: On Linguistic Fuzzy Constraint Satisfaction Problems- Linguistic and Imprecise Information in Databases and Information Systems: G Chen: Data Models for Dealing with Linguistic and Imprecise Information FE Petry, M Cobb, A Morris: Fuzzy Set Approaches to Model Uncertainty in Spatial Data and Geographic Information Systems JC Cubero, JM Medina, OPons, MA Vila: Computing Fuzzy Dependencies with Linguistic Labels J Kacprzyk, S Zadrozny: The Paradigm of Computing with Words in Intelligent Database Querying W Pedrycz: Lingusitic Data Mining RA Bustos, TD Gedeon: Evaluation of Connectionist Information Retrieval in a Legal Document Collection- Applications Information in Databases and Information Systems: ME Cohen, DL Hudson: Using Linguistic Models in Medical Decision Making JM Mendel, S Murphy, LC Miller, M Martin, N Karnik: The Fuzzy Logic Advisor for Social Judgements: A First Attempt J Zelger, AG de Wet, A-M Pothas, D Petkov: Conceptualisation with GABEK: Ideas on Social Change in South Africa F Herrera, E Lopez, C Manadana, M Rodriguez: A Linguistic Decision Model to Suppliers Selection in International Purchasing L Zerrouki, B Bouchon-Meunier, R Fondacci: Fuzzy System for Air Traffic Flow Management G Michalik, W Mielczarski: A Fuzzy Approach to Contracting Electrical Energy in Competitive Electricity Markets D Ruan: Fuzzy Logic and Intelligent Computing in Nuclear Engineering A Filippidis, LC Jain, NM Martin: Computational Intelligence Techniques in Landmine Detection

On stability of fuzzy systems expressed by fuzzy rules with singleton consequents

Abstract: Addresses the issue of stability of a fuzzy system described by fuzzy rules with singleton consequents. It first presents two canonical forms of a fuzzy system: a parametric expression and a state-space expression. A fuzzy system with singleton consequents is found to be a piecewise-polytopic-affine system. Then the paper gives stability theorems for a discrete-time system as well as for a continuous time system. It also gives a brief survey on the stability issues of fuzzy control systems.

Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems

Abstract: In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

A recurrent self-organizing neural fuzzy inference network

Abstract: A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Semantic constraints for membership function optimization

Abstract: The optimization of fuzzy systems using bio-inspired strategies, such as neural network learning rules or evolutionary optimization techniques, is becoming more and more popular. In general, fuzzy systems optimized in such a way cannot provide a linguistic interpretation, preventing us from using one of their most interesting and useful features. This paper addresses this difficulty and points out a set of constraints that when used within an optimization scheme obviate the subjective task of interpreting membership functions. To achieve this a comprehensive set of semantic properties that membership functions should have is postulated and discussed. These properties are translated in terms of nonlinear constraints that are coded within a given optimization scheme, such as backpropagation. Implementation issues and one example illustrating the importance of the proposed constraints are included.

Analysis and design of hierarchical fuzzy systems

Abstract: In this letter, the hierarchical fuzzy systems are analyzed and designed. In the analysis part, we prove that the hierarchical fuzzy systems are universal approximators and analyze the sensitivity of the fuzzy system output with respect to small perturbations in its inputs. In the design part, we derive a gradient descent algorithm for tuning the parameters of the hierarchical fuzzy system to match the input-output pairs. The algorithm is simulated for two examples and the results show that the algorithm is effective and the hierarchical structure gives good approximation accuracy.

Introduction to Neuro-Fuzzy Systems

Abstract: Fuzzy Systems.- Artificial Neural Networks.- Fuzzy Neural Networks.- Appendix: Case study: A portfolio problem Exercises.

Recurrent neuro-fuzzy networks for nonlinear process modeling

Abstract: A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

Tuning of a neuro-fuzzy controller by genetic algorithm

Abstract: Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

SLAVE: a genetic learning system based on an iterative approach

Abstract: SLAVE is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has been shown to be a useful representational tool for improving the understanding of the knowledge obtained from a human point of view. Furthermore, SLAVE uses an iterative approach for learning based on the use of a genetic algorithm (GA) as a search algorithm. We propose a modification of the initial iterative approach used in SLAVE. The main idea is to include more information in the process of learning one individual rule. This information is included in the iterative approach through a different proposal of calculus of the positive and negative example to a rule. Furthermore, we propose the use of a new fitness function and additional genetic operators that reduce the time needed for learning and improve the understanding of the rules obtained.

New methodology for analytical and optimal design of fuzzy PID controllers

Abstract: Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporated with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria. Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, second-order, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation.

Analysis and Design of Hybrid Systems

Abstract: Many physical systems today are modeled by interacting continuous and discrete event systems. Such hybrid systems contain both continuous and discrete states that influence the dynamic behavior. There has been an increasing interest in these types of systems during the last decade, mostly due to the growing use of computers in the control of physical plants but also as a result of the hybrid nature of physical processes. Hybrid system models, suitable for describing the essential dynamics of a fairly large class of physical systems in control engineering applications, are proposed in this thesis. The continuous dynamics is described by differential equations whose evolution depends on continuous states and inputs as well as discrete states. The discrete dynamics is modeled by discrete event systems dependent on discrete and continuous states and inputs. It is shown that hybrid systems can be constructed by modular decompositions. A closed-loop hybrid system structure consisting of an open-loop hybrid plant and a hybrid controller, suitable for the description of control engineering systems, is proposed. Stability is one of the most important properties of dynamic systems. A large portion of this thesis is focused on conditions ensuring stability of hybrid systems. The stability results are extensions of Lyapunov theory where the existence of an abstract energy function satisfying certain properties verifies stability. It is shown how the search for such functions can be formulated as linear matrix inequality (LMI) problems, where solutions can be found by computerized methods. Stability robustness dealing with the possibility to guarantee stability despite the presence of model uncertainties is also treated. A large number of examples illustrating different approaches is given. There are many controller structures in the industry consisting of local controllers and the design task is to decide the appropriate switching among these. A related problem occurs in the case of having discrete actuators in continuous processes. The design part of this thesis addresses the problem of how to switch between different continuous vector fields guaranteeing stability of the closed-loop system.

Fuzzy system parameters discovery by bacterial evolutionary algorithm

Abstract: This paper presents a new method for discovering the parameters of a fuzzy system; namely, the combination of input variables of the rules, the parameters of the membership functions of the variables, and a set of relevant rules from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). Nawa et al. (1997) proposed the pseudobacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a biological phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operation, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the phenomenon of transfer of strands of genes in a population of bacteria. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with this method.

A supervisory fuzzy neural network control system for tracking periodic inputs

Abstract: A supervisory fuzzy neural network (FNN) control system is designed to track periodic reference inputs in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive with a supervisory FNN position controller. The supervisory FNN controller comprises a supervisory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with online learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.

Fuzzy logic based set-point weight tuning of PID controllers

Abstract: A methodology, based on fuzzy logic, for the tuning of proportional-integral-derivative (PID) controllers is presented. A fuzzy inference system is adopted to determine the value of the weight that multiplies the set-point for the proportional action, based on the current output error and its time derivative. In this way, both the overshoot and the rise time in set-point following can be reduced. The values of the proportional gain and the integral and derivative time constant are determined according to the well-known Ziegler-Nichols formula so that a good load disturbance attenuation is also assured. The methodology is shown to be effective for a large range of processes and is valuable for industrial settings since it is intuitive, it requires only a small extra computational effort, and it is robust with regard to parameter variations. The tuning of the parameters of the fuzzy module can be easily done by hand or by means of an autotuning procedure based on genetic algorithms.

An improved stable adaptive fuzzy control method

Abstract: Stable adaptive fuzzy control is a self-tuning concept for fuzzy controllers that uses a Lyapunov-based learning algorithm, thus guaranteeing stability of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal. In the paper, two new methods for stable adaptive fuzzy control are presented. The first method is an extension of an existing concept: it is shown that a major drawback of that concept, the necessity for new adaptation at every change of the reference signal, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural network domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simulation results.

A new method for constructing membership functions and fuzzy rules from training examples

Abstract: To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the /spl alpha/-cuts of equivalence relations and the /spl alpha/-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.