A nonlinear optimal feedback controller using neural networks

Author

He, Shouling ; Reif, Konrad ; Unbehauen, Roif

Author_Institution

Lehrstuhl fur Allgemeine und Theor. Elektrotech., Erlangen-Nurnberg Univ., Germany

Volume

4

fYear

1996

fDate

11-13 Dec 1996

Firstpage

3818

Abstract

A general optimal feedback controller can be obtained by solving the Hamilton Jacobi Bellman dynamic programming equation. But for a nonlinear dynamic system, it is a difficult task. We propose a practical and effective method for constructing an approximate optimal feedback controller, where multilayer neural networks are employed in identification of nonlinear systems and Taylor expansions are exploited to get the approximate optimal solution for the nonlinear feedback controller. Two examples are given to demonstrate the effectiveness of the proposed method

Keywords

control system synthesis; discrete time systems; dynamic programming; feedback; multilayer perceptrons; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; optimal control; Taylor expansions; approximate optimal feedback controller; identification; multilayer neural networks; nonlinear dynamic system; nonlinear optimal feedback controller; Adaptive control; Control systems; Jacobian matrices; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear equations; Nonlinear systems; Optimal control; Taylor series;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.577246

Filename

577246