Robust control for nonlinear systems by universal learning network considering fuzzy criterion and second order derivatives

Author

Ohbayashi, Masanao ; Hirasawa, Kotaro ; Toshimitsu, Katsuyuki ; Murata, Junichi ; Hu, Jinglu

Author_Institution

Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

968

Abstract

Control systems using neural networks have been used in many fields, but some problems remain unsolved. One of the problems which should be overcome is to enhance the robustness of the neural network control systems. In the paper, a robust control method is proposed, which is based on the second order derivatives of the universal learning network and fuzzy criterion function

Keywords

control system synthesis; learning (artificial intelligence); neurocontrollers; nonlinear control systems; robust control; fuzzy criterion; neural network control systems; nonlinear systems; robust control; second order derivatives; universal learning network; Control systems; Design methodology; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Neural networks; Nonlinear systems; Robust control; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.685902

Filename

685902