A neural network robust controller for a class of nonlinear MIMO systems

Author

Meddah, D.Y. ; Benallegue, A. ; Cherif, A.R.

Author_Institution

Lab. PARC, Univ. Pierre et Marie Curie, Paris, France

Volume

3

fYear

1997

fDate

20-25 Apr 1997

Firstpage

2645

Abstract

A neural network-based robust adaptive tracking controller is proposed for a class of nonlinear multi-input multi-output (MIMO) systems. The nonlinear system is treated as a partially known system. The known dynamics are used to design a nominal feedback controller, and a neural network-based adaptive compensator is designed to compensate the effects of the system uncertainties. By this scheme, both strong robustness with respect to unknown dynamics and asymptotic convergence to zero of the output tracking error are obtained

Keywords

MIMO systems; adaptive control; compensation; feedback; function approximation; neural nets; neurocontrollers; nonlinear systems; robots; robust control; stability; uncertain systems; MIMO systems; adaptive control; compensation; dynamics; function approximation; neural network; nominal feedback control; nonlinear systems; robotic manipulators; robust control; stability; tracking; uncertain systems; Adaptive control; Adaptive systems; Control systems; MIMO; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on

Conference_Location

Albuquerque, NM

Print_ISBN

0-7803-3612-7

Type

conf

DOI

10.1109/ROBOT.1997.619360

Filename

619360