基于小波逼近的非线性系统鲁棒迭代学习控制

针对存在扰动的未知非线性系统,利用小波逼近将系统参数化,结合变结构控制技术, 提出了一种鲁棒迭代学习控制算法.该算法采用迭代学习的方式修正小波逼近的系数,利用具 有死区的滑模变结构技术保证算法的鲁棒收敛性.收敛性分析表明,每次迭代学习都将减小所 得到的逼近系数与最佳系数的差异.因此,期望轨迹变化后,该算法针对以前轨迹的学习结果仍 然可以起作用,部分克服了传统迭代学习控制的学习结果仅对某一特定轨迹有效的缺点.

Robust Iterative Learning Control Using Wavelet Approximation for Nonlinear Plants

A robust iterative learning control (ILC) algorithm based on wavelet approximation and variable structure control is presented for a class of unknown nonlinear uncertain systems with exogenous disturbance. The algorithm parameterizes the plant nonlin-earities by using wavelet approximation whose coefficients are iteratively adjusted by learning. The sliding variable structure control strategy with dead zone is applied to ensure the robust convergence. The convergence analysis shows that errors between the actual and the optimal wavelet coefficients are monotonically decreasing with iterations. So this scheme can partially overcome the limitation of conventional ILC scheme that the result for a given trajectory has no use for other trajectories.