Stable local neural control of uncertain systems

Author

Mears, Mark J. ; Polycarpou, Marios M.

Author_Institution

AFRL/VAAD, WPAFB, OH, USA

Volume

2

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

1691

Abstract

Tracking control of a class of nonlinear uncertain, multi-input, multiple-output systems is addressed in this paper. The control system architecture uses neural networks for function approximation, certainty equivalent control inputs to cancel plant dynamics and smoothed sliding mode control to insure that the trajectories remain bounded. Lyapunov analysis is used to derive equations for the sliding mode control, neural network training, and to show uniform ultimate boundedness of the closed loop system. A simple simulation example is used to illustrate control system performance

Keywords

Lyapunov methods; MIMO systems; closed loop systems; function approximation; neural nets; neurocontrollers; variable structure systems; Lyapunov analysis; certainty equivalent control inputs; closed loop system; control system architecture; control system performance; function approximation; multi-input multiple-output systems; neural network training; simulation example; sliding mode control; smoothed sliding mode control; stable local neural control; trajectories; uncertain systems; Closed loop systems; Control system synthesis; Control systems; Function approximation; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear equations; Sliding mode control; Uncertain systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

Conference_Location

Phoenix, AZ

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.830267

Filename

830267