Adaptive Control of Servo Systems with Uncertainties Using Self-Recurrent Wavelet Neural Networks

摘要：

In order to solve the control problem for servo system with model uncertainties, a robust control method is proposed. The proposed controller is a combination of an adaptive backstepping technique and a self-recurrent wavelet neural network (SRWNN). The SRWNN is used to observe the model lumped uncertainties of servo systems. Moreover, a robust controller is designed to compensate the lumped uncertainties including approximation error, high-order terms in Taylor series, external disturbances, LuGre friction and model parameters. From the Lyapunov stability analysis, the weights adaptation laws are derived, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results are utilized to validate the good position tracking performance and robust against uncertainties.

关键词： Adaptive backstepping self-recurrent wavelet neural network servo systems LuGre friction

作者: Jinzhu Zhou Baoyan Duan Jin Huang

作者单位: Research Institute on Mechatronics Xidian University,Xian,710071, China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）