Title :
Neural networks direct adaptive control for a class of MIMO uncertain nonlinear systems
Author :
Hu, Tingliang ; Zhu, Jihong ; Hu, Chunhua ; Sun, Zengqi
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
This paper presents a direct adaptive control scheme based on multi-layer neural networks for a class of multi-input multi-output (MIMO) nonlinear systems with unknown nonlinearity. The on-line updating rules of the neural networks parameters are obtained by Lyapunov stability theory. All signals in the closed-loop system are bounded and the output tracking error converges to a small neighborhood of zero. In this sense the closed-loop system is stable. The effectiveness of the control scheme is verified by a simulation of two link manipulator.
Keywords :
Lyapunov methods; MIMO systems; adaptive control; closed loop systems; manipulators; neural nets; nonlinear systems; stability; uncertain systems; Lyapunov stability theory; MIMO uncertain nonlinear system; closed-loop system; direct adaptive control scheme; multiinput multioutput nonlinear system; multilayer neural network; neural network; two link manipulator simulation; Adaptive control; Control systems; Lyapunov method; MIMO; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Stability; Uncertainty;
Conference_Titel :
Soft Computing in Industrial Applications, 2005. SMCia/05. Proceedings of the 2005 IEEE Mid-Summer Workshop on
Print_ISBN :
0-7803-8942-5
DOI :
10.1109/SMCIA.2005.1466964
