Adaptive neural tracking control for a class of stochastic nonlinear systems with unknown dead-zone

作者：Wang Huanqing; Chen Bing; Lin Chong

来源：International Journal of Innovative Computing Information and Control, 2013, 9(8): 3257-3269.

• Adaptive neural control
• Adaptive neural controllers
• Closed-loop signals
• Controller designs
• Dead-zone
• Radical basis function neural networks
• Semi-globally uniformly ultimately bounded
• Stochastic nonlinear systems

This paper is concerned with the problem of adaptive neural tracking control for a class of strict-feedback stochastic nonlinear systems with unknown dead zone. In the controller design, radical basis RBF) neural networks are used to model the packaged unknown nonlinearities, and then an adaptive neural controller is systematically derived without requiring any information on the boundedness of dead-zone parameters (slopes and break-points). It is shown that the proposed controller guarantees that all the closed-loop signals are semi-globally uniformly ultimately bounded in probability and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

• 出版日期2013