Title :
A neuro-fuzzy method for tracking control
Author :
Su, Ching-Tzong ; Lii, Guor-Rurng ; Hwung, Hong-Rong
Author_Institution :
Inst. of Electr. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan
Abstract :
The purpose of this paper is to propose a new approach to be used in optimal position control. This method uses fuzzy control system and works with genetic algorithms (GAs) to meet the requirement of optimal position control. Based on the unsupervised training of self-organizing neural network, the fuzzy expert experiences are learned. The neuro-fuzzy controller (NFC) then applies, these experiences learned to determine the output control force. By virtue of the evolution rule of genetic algorithms, the best expert experiences are extracted and employed to achieve the optimal position control. Application of the proposed method to the inverted pendulum system is also presented. The simulation results show that the controller has satisfactory performance
Keywords :
fuzzy control; fuzzy neural nets; genetic algorithms; neurocontrollers; optimal control; position control; self-organising feature maps; tracking; unsupervised learning; GA; fuzzy control system; fuzzy expert experiences; genetic algorithms; inverted pendulum system; neuro-fuzzy controller; optimal position control; self-organizing neural network; tracking control; unsupervised training; Biological control systems; Control systems; Force control; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Genetic algorithms; Neural networks; Optimal control; Position control;
Conference_Titel :
Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
0-7803-3104-4
DOI :
10.1109/ICIT.1996.601680