Title :
Adaptive-neural control of a class of unknown nonlinear discrete-time systems
Author_Institution :
Dept. of Mech. & Marine Eng., Nat. Taiwan Ocean Univ., China
Abstract :
An adaptive controller based on neural networks is derived for controlling a class of unknown nonlinear discrete-time systems. The learning algorithm, Widrow-Hoff delta rule, is used to minimize the error signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stable. The effectiveness of the proposed control scheme is also demonstrated by a simulation example
Keywords :
adaptive control; closed loop systems; discrete time systems; learning (artificial intelligence); multilayer perceptrons; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; stability; uncertain systems; Widrow-Hoff delta rule; adaptive neural control; learning algorithm; unknown nonlinear discrete-time systems; Adaptive control; Control systems; Electronic mail; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Oceans; Programmable control; Vectors;
Conference_Titel :
SICE '98. Proceedings of the 37th SICE Annual Conference. International Session Papers
Conference_Location :
Chiba
DOI :
10.1109/SICE.1998.742979
