Title :
A design of hybrid neural net-based GMVC and its application
Author :
Oki, Toshitaka ; Yamamoto, Toru ; Kaneda, Masahiro ; Shimizu, Akira
Author_Institution :
Dept. of Commun. Eng., Okayama Prefectural Univ., Okayama, Japan
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
In this paper, a design method for a nonlinear system is proposed, using a linear control scheme for the approximated linear model of the controlled object and a NN compensating the modeling error. In this scheme, the linear controller is designed based on the approximated linear model. And the augmented controlled object which consists of the controlled object and the NN behave same as the approximated linear model, after the NN is trained enough. The effectiveness of the proposed method is evaluated on a temperature control of a water bath which has a nonlinear property.
Keywords :
approximation theory; compensation; control system synthesis; linear systems; neurocontrollers; nonlinear control systems; temperature control; NN compensation; generalized minimum variance control; hybrid neural net-based GMVC design; linear controller design; linear model approximation; nonlinear system design method; water bath temperature control; Artificial neural networks; Design methodology; Nonlinear systems; Polynomials; Temperature control; generalized minimum variance control; neural network; nonlinear system; process control; temperature control system;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
