By using the approximation capability of neural networks and the backstepping method, an adaptive neural network control scheme is proposed for a class of stochastic strictfeedback nonlinear systems with dead-zone model. Compared with the existing literature, the proposed approach eliminates the condition that the controller and adaptation law should be related to the nodes of neural network. By Lyapunov method, it is shown that all signals in the closed loop system are bounded in probability, and the error signals are semi-globally uniformly ultimately bounded in mean square or the sense of four-moment. imulation results are given to illustrate the effectiveness of the proposed control scheme.
ZHU Baicheng ,ZHANG Tianping ,WANG Fei.
ADAPTIVE TRACKING CONTROL FOR A CLASS OF STOCHASTIC SYSTEMS WITH DEAD-ZONE MODEL. Journal of Systems Science and Mathematical Sciences, 2012, 32(11): 1331-1342 https://doi.org/10.12341/jssms11990