Adaptive control of nonlinear dynamic systems using &thetas;-adaptive neural networks

Author

Yu, Ssu-Hsin ; Annaswamy, Anuradha M.

Author_Institution

Dept. of Mech. Eng., MIT, Cambridge, MA, USA

Volume

4

fYear

1996

fDate

3-6 Jun 1996

Firstpage

2072

Abstract

The adaptive control of dynamic systems with nonlinear parametrization is considered. An algorithm based on a neural network, similar to the TANN algorithm proposed in Annaswamy and Yu (1996), is suggested for adjusting the control parameters. The adaptive controller is shown to lead to stability of the closed-loop system. How the neural network is trained off-line in order to lead to closed-loop stability is described in detail. The resulting improvement in performance using the neural algorithm over the extended Kalman filter algorithm is demonstrated through simulation studies

Keywords

adaptive control; closed loop systems; learning (artificial intelligence); neural nets; nonlinear dynamical systems; stability; &thetas;-adaptive neural networks; TANN algorithm; adaptive control; closed-loop stability; closed-loop system; nonlinear dynamic systems; nonlinear parametrization; Adaptive control; Adaptive systems; Control systems; Neural networks; Nonlinear dynamical systems; Optimal control; Parameter estimation; Power engineering and energy; Programmable control; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1996., IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7803-3210-5

Type

conf

DOI

10.1109/ICNN.1996.549221

Filename

549221