Adaptive Neural Tracking Control of Pure-feedback Nonlinear Systems

摘要：

In this paper, an novel adaptive tracking control is developed for a class of completely non-af.ne purefeedback nonlinear systems using radial basis function neural networks (RBFNNs). Combining the dynamic surface control (DSC) technique and backstepping method, the explosion of complexity in the traditional backstepping design is avoided. Using mean value theorem and Young抯 inequality, only one learning parameter need to be tuned online in the whole controller design, and the computational burden is effectively alleviated. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.

关键词： Adaptive Control Neural Networks Dynamic Surface Control Pure-Feedback Nonlinear Systems

作者: Tianping Zhang Baicheng Zhu Xiaocheng Shi

作者单位: Department of Automation, College of Information Engineering, YangzhouUniversity, Yangzhou 225127, P Department of Automation, College of Information Engineering, Yangzhou University, Yangzhou 225127,

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2134-2139

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）