Title :
Stable adaptive control for nonlinear systems using neural networks
Author :
Yang, Shi ; Chundi, Mu ; Weisheng, Yan ; Jun, Li ; Demin, Xu
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
Stability analysis of neural-network-based nonlinear control has presented great difficulties. For a class of affine nonlinear systems with uncertainties, we employed nonlinear-parameter-neural-networks (NPNN) to approximate online the unknown nonlinearities, estimate online the NPNN approximation error´s bound, and then succeeded in designing the control law and the adaptive laws of NPNN´s weights and the NPNN approximation error´s bound. The stability of the closed-loop is proved by using Lyapunov theory. Simulation results show that the controller we proposed exhibits excellent tracking performance
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control system analysis; neurocontrollers; nonlinear control systems; stability; uncertain systems; Lyapunov theory; NPNN; affine nonlinear systems; closed-loop stability; neural networks; nonlinear-parameter-neural-networks; online approximation; online estimation; stable adaptive control; uncertainties; unknown nonlinearities; Adaptive control; Approximation error; Control nonlinearities; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Stability analysis; Uncertainty;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863380
