Title :
Adaptive control for a class of nonlinear discrete-time systems using neural networks
Author :
Ge, S.S. ; Li, G.Y. ; Lee, T.H.
Author_Institution :
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
In this paper, the adaptive control problem is studied for a class of discrete-time unknown nonlinear systems with general relative degree in the presence of bounded disturbances. To derive the feedback control, a causal state-space model of the plant is obtained. By using an NN observer to estimate the unavailable but predictable states of the system, a Lyapunov-based adaptive state feedback NN controller is proposed. The state feedback control avoids the possible singularity problem in adaptive nonlinear control. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). An arbitrarily small tracking error can be achieved if the size of neural networks is chosen large enough, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters
Keywords :
Lyapunov methods; adaptive control; closed loop systems; discrete time systems; neurocontrollers; nonlinear control systems; observers; state feedback; state-space methods; uncertain systems; Lyapunov-based adaptive state feedback controller; NN observer; SGUUB system; UUB system; adaptive control; bounded disturbances; causal state-space model; closed-loop system; neural networks; nonlinear discrete-time systems; semi-globally uniformly ultimately bounded system; singularity problem; tracking error; unknown systems; Adaptive control; Adaptive systems; Control systems; Feedback control; Neural networks; Nonlinear systems; Observers; Programmable control; State estimation; State feedback;
Conference_Titel :
Intelligent Control, 2001. (ISIC '01). Proceedings of the 2001 IEEE International Symposium on
Conference_Location :
Mexico City
Print_ISBN :
0-7803-6722-7
DOI :
10.1109/ISIC.2001.971491
