Fuzzy Control for Inverted Pendulum Using Fuzzy Neural Networks

1995 ◽  
Vol 7 (1) ◽  
pp. 36-44 ◽  
Author(s):  
Shin-ichi Horikawa ◽  
◽  
Masahiro Yamaguchi ◽  
Takeshi Furuhashi ◽  
Yoshiki Uchikawa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Inverted Pendulum ◽  
Fuzzy Neural Networks ◽  
Fuzzy Controllers ◽  
Type Iv ◽  
Control Rules ◽  
Fuzzy Neural

Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy models. The method can comprehensively express the dynamic behavior of fuzzy control systems and makes easy to know how to modify the fuzzy controllers. This paper studies an acquisition of fuzzy controller for an inverted pendulum using the Type IV FNNs and presents a new method for describing of the behavior of the fuzzy control system. The new method expresses the dynamic ehavior of the fuzzy control system more clearly by incorporating the change of the output of the controlled object. This new rule-to-rule mapping method enables easy modification of the fuzzy control rules. The experimental results illustrate that the method is effective in designing the fuzzy controllers having good performance.

scholarly journals Fault diagnosis of sensors in the control system of a steam turbine

2019 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Mariusz Pawlak ◽  
Janusz Buchta ◽  
Andrzej Oziemski
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Control Systems ◽  
Measurement Data ◽  
Fuzzy Neural Networks ◽  
Sensor Faults ◽  
Fuzzy Neural ◽  
And Control

A diagnostic and control system for a turbine is presented. The influence of the turbine controller on regulation processes in the power system is described. Measured quantities have been characterized and methods for detecting errors have been determined. The paper presents the application of fuzzy neural networks (fuzzy-NNs) for diagnosing sensor faults in the control systems of a steam turbine. The structure of the fuzzy-NN model and the model’s method of learning, based on measurement data, are presented. Fuzzy-NNs are used to detect faults procedures. The fuzzy-NN models are created and verified.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

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