Direct adaptive regulation using recurrent neural networks: modeling error-external disturbance effects

Author

Rovithakis, George A. ; Christodoulou, Manolis A.

Author_Institution

Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

Volume

3

fYear

1994

Firstpage

2482

Abstract

A robust direct nonlinear adaptive state regulator, for unknown plants that are modeled by recurrent neural networks is discussed. The unavoidable appearance of a modeling error which is not a priori bounded, as well as the effects of both additive and multiplicative external disturbances on the closed loop system are examined. Generally, under certain modifications on the control and update laws, uniform boundedness of all signals in the closed loop is ensured

Keywords

adaptive control; closed loop systems; neurocontrollers; nonlinear dynamical systems; recurrent neural nets; robust control; adaptive control; closed loop system; direct nonlinear adaptive state regulator; error-external disturbance effects; modeling error; nonlinear dynamical systems; recurrent neural networks; robust control; Adaptive control; Backpropagation; Computer errors; Control nonlinearities; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Recurrent neural networks; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on

Conference_Location

Lake Buena Vista, FL

Print_ISBN

0-7803-1968-0

Type

conf

DOI

10.1109/CDC.1994.411514

Filename

411514