Adaptive Neuro Sliding Mode Control of Nonlinear System

摘要：

Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic and nonlinear system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system. Theoretical analysis and simulation results indicate that the control approach can be applied to the systems either with or without series nonlinearity and/or dead zone in the input.

作者: Xu Zi-bin Min Jian-qing Ruan Jian

作者单位: The MOE Key Laboratory of Mechanical Manufacture and Automation Zhejiang University of Technology, H The MOE Key Laboratory of Mechanical Manufacture and Automation Zhejiang University of Technology, H Zhejiang Shuren University, Hangzhou, China, 310015

会议类型: 国际会议

会议名称: International Conference on Intelligent Computation Technology and Automation(2008 智能计算技术与自动化国际会议 ICICTA 2008)

会议地点: 长沙

会议语种:英文

页码: 284-288

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）