Showing papers on "Fuzzy control system published in 1977"
TL;DR: In this article, a fuzzy logic is used to synthesize linguistic control protocol of a skilled operator for industrial plants, which has been applied to pilot scale plants as well as in practical situations.
Abstract: This paper describes an application of fuzzy logic in designing controllers for industrial plants. A fuzzy logic is used to synthesize linguistic control protocol of a skilled operator. The method has been applied to pilot scale plants as well as in practical situations. The merits of this method and its usefulness to control engineering are discussed. An avenue for further work in this area is described where the need is to go beyond a purely descriptive approach, and means for implementing a prescriptive or a self-organizing system are explored.
2,011 citations
TL;DR: The fuzzy control algorithm is used to implement linguistically expressed heuristic control policies directly, with a view to automating those complex and poorly-defined processes where modelling difficulties and lack of suitable measurements make manual control imperative.
740 citations
Journal Article•
TL;DR: A linguistic control algorithm is synthesized, capable of dealing with a continuously reproduced decisionmaking situation, and a fuzzy set theoretic representation of these instructions, called a fuzzy logic controller, was tried as an answer to the control modeling problem, which gave very satisfactory results.
Abstract: The system is a traffic junction and the problem of its control is considered as a classical example of nonprogrammed decisionmaking, i.e. , decisionmaking characterized by the lack of well-specified analytical means for coping with a particular problem. Thus a linguistic control algorithm is synthesized, capable of dealing with a continuously reproduced decisionmaking situation. The starting point is an adequate (though qualitative) knowledge of the system and a protocol of control instructions used by a human operator. A fuzzy set theoretic representation of these instructions which we call "a fuzzy logic controller" was tried as an answer to the control modeling problem, which gave very satisfactory results. The work done on the construction of the model of the system and the implementation of the fuzzy logic controller is presented.
440 citations
01 Oct 1977
TL;DR: It is shown that the use of a fuzzy logic controller results in a better performance than a conventional effective vehicle-actuated controller.
Abstract: Work done on the implementation of a fuzzy logic controller in a single intersection of two one-way streets is presented. The model of the intersection is described and validated, and the use of the theory of fuzzy sets in constructing a controller based on linguistic control instructions is introduced. The results obtained from the implementation of the fuzzy logic controller are tabulated against those corresponding to a conventional effective vehicle-actuated controller. With the performance criterion being the average delay of vehicles, it is shown that the use of a fuzzy logic controller results in a better performance.
380 citations
TL;DR: Fuzzy set theory is a relatively new concept which allows this qualitativeness to be expressed rigorously and its usefulness for control is assessed and a surprising number of practical successes are revealed.
329 citations
176 citations
32 citations
TL;DR: A pattern recognition system for the analysis of human sleep stages using the EEG data is presented and it is implemented in hardware for real-time applications.
26 citations
01 Dec 1977
TL;DR: In this article, it was shown that the internal model principle is valid for regulators (plant, controller and exosystem) working in fuzzy, environments, and that this principle is also valid for control systems.
Abstract: In the setting of modern algebra, it is shown that the internal model principle is valid for regulators (plant, controller and exosystem) working in fuzzy, environments.
14 citations
01 Dec 1977
TL;DR: The results obtained are a further step towards an integrated 'theory of uncertainty' and give new insights into problems of inductive reasoning and processes of 'precisiation', and have been embodied in a computer program that can be applied to the modelling of sequential fuzzy data.
Abstract: The problem of deriving the structure of a non-deterministic system from its behavior is a difficult one even when that behavior is itself well-defined. When the behavior can be described only in fuzzy terms structural inference may appear virtually impossible. However, a rigorous formulation and solution of the problem for stochastic automata has recently been given [1] and, in this paper, the results are extended to fuzzy stochastic automata and grammars. The results obtained are of interest on a number of counts, (1) They are a further step towards an integrated 'theory of uncertainty'; (2) They give new insights into problems of inductive reasoning and processes of 'precisiation'; (3) They are algorithmic and have been embodied in a computer program that can be applied to the modelling of sequential fuzzy data.
9 citations
01 Dec 1977
TL;DR: A heuristic synthesis method for a class of systems using a fuzzy automaton and a computational algorithm is given on the basis of a simple learning system so as to realize the proposed method.
Abstract: A heuristic synthesis method for a class of systems is proposed by using a fuzzy automaton. The transition of system structure is represented by fuzzy relations and the interconnection among subsystems, that is system structure is determined by a heuristic policy. A computational algorithm is given on the basis of a learning system so as to realize the proposed method. Further, in order to avoid to get an ill-posed solution, a necessary condition is given with respect to the fuzzy transition matrix and the initial condition of system. An example is illustrated to show how the proposed method works.