Adaptive neurocontrol of MIMO systems based on stability theory

Author

Renders, Jean-Michel ; Saerens, Marco ; Bersini, Hugues

Author_Institution

Lab. d´´Autom., Univ. Libre de Bruxelles, Belgium

Volume

4

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2476

Abstract

In this paper, we prove the input-output stability of a certain class of nonlinear discrete MIMO systems controlled by a multilayer neural net with a simple weight adaptation strategy. The proof is based on the Lyapunov formalism. The stability statement is, however, only valid if the initial weight values are not too far from their optimal values that allow perfect model matching. We therefore propose to initialize the weights with values that solve the linear problem. This extends our previous work (Renders, 1993; Saerens, Renders and Bersini, 1993), where SISO systems were considered

Keywords

Lyapunov methods; MIMO systems; adaptive control; discrete systems; multilayer perceptrons; neurocontrollers; nonlinear control systems; stability; Lyapunov formalism; adaptive neurocontrol; input-output stability; model matching; multilayer neural net; nonlinear discrete MIMO systems; simple weight adaptation strategy; Adaptive control; Control systems; Cybernetics; Delay; Laboratories; MIMO; Multi-layer neural network; Neural networks; Nonlinear control systems; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374609

Filename

374609