Some applications of soft computing methods in system modelling and control

Author

Lantos, Béla

Author_Institution

Dept. of Process Control, Tech. Univ. Budapest, Hungary

fYear

1997

Firstpage

469

Lastpage

474

Abstract

The paper deals with the application of fuzzy systems, artificial neural networks (neural systems) and genetic algorithms to solve modelling and control problems in system engineering. The first part of the paper deals with the design of classical PID and fuzzy PID-type controllers for nonlinear systems with (approximately) known dynamic model. The optimal controllers are designed based on genetic algorithms. The second part considers the neural control of a SCARA robot. The third part deals with the fuzzy control of a special class of MIMO nonlinear systems and generalizes the method of Wang (1994) for such systems

Keywords

control engineering; control system synthesis; fuzzy systems; genetic algorithms; modelling; neural nets; neurocontrollers; optimal control; systems engineering; three-term control; MIMO nonlinear systems; SCARA robot; artificial neural networks; fuzzy PID controllers; fuzzy systems; genetic algorithms; nonlinear systems; optimal controllers; soft computing methods; system control; system engineering; system modelling; Artificial neural networks; Computer applications; Control system synthesis; Fuzzy control; Fuzzy systems; Genetic algorithms; Genetic engineering; Nonlinear systems; Systems engineering and theory; Three-term control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Engineering Systems, 1997. INES '97. Proceedings., 1997 IEEE International Conference on

Conference_Location

Budapest

Print_ISBN

0-7803-3627-5

Type

conf

DOI

10.1109/INES.1997.632463

Filename

632463