Backstepping-Based Neural Adaptive Control for Saturated Nonlinear Systems

摘要：

　　In this paper,neural adaptive backstepping control is investigated for a class of nonlinear systems with a saturated control input.To deal with the tracking problem in the face of input saturation,effective auxiliary systems are constructed,which generate signals preventing the stability of the closed-loop system,and the learning capabilities of adaptation laws from being destroyed.Radial basis function neural networks(RBF NNs)are used in the online learning of unknown dynamics.The semi-global bounded stability of the closed-loop system under the proposed control law is guaranteed by utilizing Lyapunov stability theory,and the system output tracks the desired curve with only small error.Simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Neural Adaptive Control Backstepping Control Nonlinear System

作者: Shigen Gao Bin Ning Hairong Dong Yao Chen

作者单位: State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,P State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,P School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,P.R.Chin

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 3345-3349

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）