Adaptive neural tracking control for a class of stochastic nonlinear systems

作者：Wang, Huan-qing; Chen, Bing^*; Lin, Chong

来源：International Journal of Robust and Nonlinear Control, 2014, 24(7): 1262-1280.

DOI：10.1002/rnc.2943

• stochastic nonlinear systems
• the neural networks control
• adaptive tracking control
• backstepping design

This paper investigates the problem of adaptive neural control design for a class of single-input single-output strict-feedback stochastic nonlinear systems whose output is an known linear function. The radial basis function neural networks are used to approximate the nonlinearities, and adaptive backstepping technique is employed to construct controllers. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. The salient property of the proposed scheme is that only one adaptive parameter is needed to be tuned online. So, the computational burden is considerably alleviated. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed approach.

• 出版日期2014-5-10
• 单位青岛大学; 渤海大学