Adaptive neural network control for a class of nonaffine discrete-time systems

摘要：

The tracking control problem for a class of nonaffine uncertain discrete-time nonlinear systems is addressed. Firstly, a linear output feedback dynamic compensator is proposed to stabilize the linear portion of the tracking error system, and a discrete single-hidden-layer (SHL) neural network (NN) controller is introduced to cancel the inversion error. NN learn though the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. Secondly, a linear observer is proposed for the tracking error dynamics. Thirdly, by the Lyapunov stability theory, we show that the output tracking error converges to a neighborhood of the origin, whose size can be adjusted by control parameters. Finally, simulation illustrates the effectiveness of the proposed control method.

关键词： Discrete-time systems Nonaffine systems Fix point theorem Output feedback Adaptive control Neural networks

作者: Jiemei ZHAO Lijun ZHANG Xue QI Heming JIA

作者单位: College of Automation, Harbin Engineering University, Harbin 150001, China College of Automation, Harbin Engineering University, Harbin 150001, China School of Marine, Northwe School of Science, Anhui Science and Technology University, Bengbu 233100, China College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, Chin

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 700-705

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）