Showing papers on "Fuzzy control system published in 1991"

neural networks and fuzzy systems a dynamical systems approach to machine intelligence

Abstract: This work combines neural networks and fuzzy systems, presenting neural networks as trainable dynamical systems and developing mechanisms and principles of adaption, self-organization, convergence and global stability. It includes the new geometric theory of fuzzy sets, systems and associated memories, and shows how to apply fuzzy set theory to adaptive control and how to generate structured fuzzy systems with unsupervised neural techniques.

Neural-network-based fuzzy logic control and decision system

Abstract: A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

Fuzzy systems theory and its applications

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Fuzzy Expert Systems

Abstract: PREFACE. FUZZY EXPERT SYSTEMS THEORY. The Evolution From Expert Systems to Fuzzy Expert Systems. General Purpose Fuzzy Expert Systems. Inferences With Inaccuracy and Uncertainty in Expert Systems. On the Representation of Relational Production Rules in Expert Systems. Reduction Procedures for Rule-Based Expert Systems as a Tool for Studies of Properties of Expert's Knowledge. The Physiology of the Expert System. On the Processing of Imperfect Information Using Structured Frameworks. Fuzzy Linguistic Inference Network Generator. Advances in Automated Reasoning Using Possibilistic Logic. Fuzzy Associative Memory Systems. APPLICATIONS OF FUZZY EXPERT SYSTEMS. The Role of Approximate Reasoning in a Medical Expert System. Fess: A Reusable Fuzzy Expert System. Design for Designing: Fuzzy Relational Environmental Design Assistant (FREDA). On the Design of a Fuzzy Intelligent Differential Equation Solver. MILORD: A Fuzzy Expert Systems Shell. Medical Decision Making Using Classification Techniques for Establishment of Knowledge Bases. Fuzzy Expert Systems for an Intelligent Computer-Based Tutor. Expert System on a Chip: An Engine for Approximate Reasoning. A Probabilistic Logic for Expert Systems. COMEX-An Autonomous Fuzzy Expert System for Tactical Communications Networks.

Fuzzy modeling using generalized neural networks and Kalman filter algorithm

Abstract: We propose a new approach to build a, fuzzy inference system of which the parameters can be updated to achieve a desired input-output mapping. The structure of the proposed fuzzy inference system is called generalized neural networks, and its learning procedure (rules to update parameters) is basically composed of a gradient descent algorithm and Kalman filter algorithm. Specifically, we first introduce the concept of generalized neural networks (GNN's) and develop a gradient-descent-based supervised learning procedure to update the GNN's parameters. Secondly, we observe that if the overall output of a GNN is a linear combination of some of its parameters, then these parameters can be identified by one-time application of Kalman filter algorithm to minimize the squared error, According to the simulation results, it is concluded that the proposedl new fuzzy inference system can not only incorporate prior knowledge about the original system but also fine-tune the membership functions of the fuzzy rules as the training data set varies.

Hierarchical fuzzy control

Abstract: In a conventional rule based fuzzy control system, the rules are of the following form: if (condition) then (action), and all rules are essentially in a random order. The number of rules increases exponentially as the number of the system variables, on which the fuzzy rules are based, is increased. In this paper, the rules are structured in a hierarchical way so that the total number of rules will be a linear function of the system variables. The hierarchical fuzzy control algorithm developed in this paper is applied to control the feedwater flow to a steam generator of a power plant. The simulation results show that the hierarchical fuzzy controller yields superior performance over the conventional PID controller.

Fuzzy dynamic programming: an application to unit commitment

Abstract: A new approach using fuzzy dynamic programming is proposed for the unit commitment of a power system. A characteristic feature of the approach is that the errors in the forecast hourly loads can be taken into account by using fuzzy set notations, making the approach superior to the conventional dynamic programming method which assumes that the hourly loads are exactly known and there exist no errors in the forecast loads. To reach an optimal commitment strategy under the fuzzy environment, a fuzzy dynamic programming model in which the hourly loads, the cost, and system security are all expressed in fuzzy set notations is developed. The effectiveness of the approach is demonstrated by the unit commitment of the Taiwan power system, which contains six nuclear units, 48 thermal units, and 44 hydro units. >

A fuzzy set theory based control of a phase-controlled converter DC machine drive

Abstract: A description is presented of the application of fuzzy logic in a speed control system that uses a phase-controlled bridge converter and a separately excited DC machine. The fuzzy control is used to linearize the transfer characteristics of the converter in the discontinuous conduction mode occurring at light load and/or high speed. The fuzzy control is then extended to the current and speed control loops replacing the conventional proportional-integral (PI) control method. The compensation and control algorithms have been developed in detail and verified by digital simulation of a drive system. The simulation study indicates the superiority of fuzzy control over the conventional control methods. Fuzzy logic seems to have a lot of promise in the application of power electronics. >

Fuzzy modelling of power system optimal load flow

Abstract: A fuzzy model for power system operation is presented. Uncertainties in loads and generations are modeled as fuzzy numbers. System behavior under known (while uncertain) injections is dealt with by a DC fuzzy power flow model. System optimal (while uncertain) operation is calculated with linear programming procedures in which the problem nature and structure allow some efficient techniques such as Dantzig-Wolfe decomposition and dual simplex to be used. Among the results, one obtains a fuzzy cost value for system operation and possibility distributions for branch power flows and power generations. Some risk analysis is possible, as system robustness and exposure indices can be derived and hedging policies can be investigated. >

Generating fuzzy rules by learning from examples

Abstract: A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series. >

Fuzzy ART: an adaptive resonance algorithm for rapid, stable classification of analog patterns

Abstract: A fuzzy ART (adaptive resonance theory) system is introduced which incorporates computations from fuzzy set theory into ART 1. For example, the intersection ( intersection ) operator used in ART 1 learning is replaced by the MIN operator ( V-product ) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitude of individual feature activations. >

Design of a fuzzy controller using input and output mapping factors

Abstract: A complete design procedure for a fuzzy three-term PID controller is presented. A plant model is not required to achieve this design. A reduced look-up table containing the rules along with the quantization and tuning procedures by means of input and output mapping factors are introduced. The scaling factors of the output error time sequences are preselected arbitrarily and the search for an optimal input-to-output mapping factors ratio is performed through a phase diagram analysis. the applicability of the design procedure is demonstrated through computer simulations. >

Application of fuzzy logic control to a manipulator

Abstract: The authors describe a control strategy for robotic manipulators that incorporates a proportional-plus-integral (PI) controller with a simple fuzzy logic (FL) controller. In the proposed strategy, the PI controller is used to ensure fast transient response and zero steady-state error for step inputs, or end-point control, whereas the FL controller is used to enhance the damping characteristics of the overall system. The main advantage of the proposed control energy is that only the current and previous measurements and a set of simple control rules are required; as such, it can be readily implemented. The proposed FL controller is described, and its effectiveness is demonstrated through simulations involving the control of a two-arm two-link manipulator. >

Task sequence planning using fuzzy Petri nets

Abstract: This paper discusses the problem of representation and planning of operations sequences in a robotic system using fuzzy Petri nets. In the fuzzy Petri net representation, objects whose internal states are altered during a process are termed soft objects, and the process steps where alterations may occur are labeled key transitions. A correct sequence is defined as a sequence which is feasible, complete, and satisfies precedence relations. In this formulation, the internal state of an object is represented by a global fuzzy variable attached to the token related to the degree of completion of the process. All correct operations sequences must satisfy process sequence constraints imposed by transition reasoning rules. The correct precedence relationships and the characteristics of completeness for operations in all feasible sequences are guaranteed by the prime number marking algorithm which marks the fuzzy Petri net. The use of transition reasoning rules in this application simplifies the representation and search problems for task planning where correct sequences do not depend on exact knowledge of internal states, but only their precedence relations. >

Fuzzy associative memories

Abstract: Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

Design and implementation of a fuzzy controller for a high performance induction motor drive

Abstract: A limit-cycle controlled induction motor drive with a fuzzy controller has been designed and implemented. The torque and flux of the proposed drive system are regulated by the limit-cycle control technique. It follows that very quick torque response can be achieved. Since the dynamic model of this type of drive system is not easy to obtain, a fuzzy controller was developed and used in the speed control feedback loop to obtain good dynamic rotor speed response. The fuzzy algorithms in the proposed controller are systematically found from intuition and experience about the drive systems. The experimental results indicate that good dynamic speed performance can be achieved by the proposed controller. Moreover, since the rotor parameters are not needed in the implementation of the drive system, and due to the inherent feature of high adaptive capability possessed by the fuzzy controller, the performance of the controller drive system is rather insensitive to the parameter and operating condition changes. >

A method of indoor mobile robot navigation by using fuzzy control

Abstract: In the design of an autonomous mobile robot (AMR), it is necessary to describe schemes of monitoring the status of the robot and suitable procedures of handling various situations. this paper presents a sensor-based navigation method using fuzzy control, whose purpose is to construct expert knowledge for efficient and better piloting of the AMRs. This method provides a function for tracing a planned path by sensing the distance of an AMR from its planned path and the difference between its angle and that of the planned path, and another function that allows an AMR to avoid stationary and moving obstacles by sensing how far an open area extends ahead of it. Fuzzy control is also used to select suitable rules for tracing a path or avoiding obstacles according to the situation, which is determined from sensor information by using fuzzy control. The effectiveness of the established rules and the effect of fuzzy control on AMR navigation are discussed on the basis of simulations. >

Sliding mode controller design based on fuzzy inference for non-linear systems

Abstract: The authors propose a method of nonlinear feedback control, introducing fuzzy inference into the sliding mode control The fuzzy inference is introduced to treat the nonlinearity of the system and to reduce the control chattering A nonlinear system is expressed by fuzzy modeling which is composed of the weighted average of linear systems with fuzzy inference Stability analysis of the fuzzy control system is carried out on the basis of fuzzy stability theory based on Lyapunov's direct method The proposed control method is applied numerically to the stabilizing control of an electric power system with an automatic voltage regulator and a governor As a result, the large oscillation which indicates the nonlinear phenomenon is stabilized; the control chattering is reduced; and a good control effect which includes control stability is obtained >

Vector control of induction motor with fuzzy PI controller

Abstract: A description is presented of the fuzzy proportional-plus-integral controller for the vector control system of an induction motor, and the performance of the system using this controller is discussed. The theoretical process of the fuzzy inference and the guide to a design of the controller are presented. This controller is applied to the laboratory model drive system with 0.75 kW induction motor. The simulation results demonstrate the good performance of this system. >

Rule-based fuzzy logic controller for a PWR-type nuclear power plant

Abstract: A rule-based fuzzy logic regulator was developed for a pressurized-water-reactor-type nuclear power plant. It is based on knowledge acquisition through numerical simulations and on the use of a validated mathematical model of the H.B. Robinson power plant. Production rules were used for knowledge representation, and fuzzy sets were implemented using broken lines. Due to the nature of the rules, forward chaining was selected as the inferencing mechanism. The gain and sampling interval values were adjusted using an error criterion. The behavior of this rule-based controller was investigated under normal and noisy operating conditions and in the presence of drift in process variables. It was observed that there was negligible degradation in the performance of the controller in the presence of noise and drift in process variables. >