Title :
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
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;
Conference_Titel :
Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3612-7
DOI :
10.1109/ROBOT.1997.619360
