Wavelet Asymptotic Tracking Control for Uncertain Nonlinear Systems
A robust adaptive wavelet neural network control of uncertain nonlinear system is proposed to make the tracking error asymptotically converges to zero. Wavelet neural networks are used to approach the unknown functions. All the parameters of wavelet neural networks are tuned online. Robust terms are used to compensate the approximate errors. As different from usual robust terms, time-varying parameters are introduced in robust terms to guarantee the closed-system tracing error converges to zero. The parameters’ update laws of the robust terms are designed by Lyapunov function. The systematic design procedure for the controller is addressed by using the backstepping technique. It is proved that the tracking error asymptotically converges to zero. The proposed method is validated by simulation.
QIAO Ji-Hong WANG Hong-Yan CHEN Yan
College of Computer and Information Engineering, Beijing Technology and Business University, Beijing Department of Information Engineering, Academy of Armored Force Engineering, Beijing 100072, P.R.Chi
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）