Adaptive neural control of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays

作者：Gao Huating; Zhang Tianping^*; Xia Xiaonan

来源：Journal of the Franklin Institute, 2014, 351(6): 3182-3199.

DOI：10.1016/j.jfranklin.2014.02.013

In this paper, a novel adaptive control scheme is Investigated based on the backstepping design for a class of stochastic nonlinear systems with untnodeled dynamics and time-varying state delays. The radial basis function neural networks are used to approximate the unknown nonlinear functions obtained by using Ito differential formula and Young's inequality. The unknown time-varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov-Krasovskii functions and introducing available dynamic signal. It is proved that all signals in the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded (SGUUB) in mean square or the sense of four-moment. Simulation results illustrate the effectiveness of the proposed design.

• 出版日期2014-6
• 单位扬州大学