分域设计迭代学习控制器抑制非重复性扰动

为了抑制迭代方向上已知重复样式的非重复性输出扰动,提出了迭代学习控制(Iterative Learning Control,ILC)的分域算法。时间域内设计传统PID型迭代学习控制器,并且优化其参数;迭代域内利用内模原理抑制非重复性输出扰动,跟踪期望轨迹;利用加权思想将两者相结合,得到迭代学习控制器的分域设计算法。相对于已有算法,建立了针对一般扰动的设计框架,并且合理配置了算法的参数,使收敛速度及精度有所提高。仿真结果说明了该算法的有效性。

A Separative Domain Design to Harness the Nonrepetitiveness in Iterative Learning Control

A separative domain design for iterative learning control(ILC)is proposed to harness the non-repetitive output disturbance with known repeating patterns.The traditional PID type ILC algorithm is designed in time-axis and the parameters are optimized.Internal model principle is used for harnessing the disturbance and tracking the desired trajectory in iterative-axis.The new iterative learning control algorithm is obtained by using the weighted method.A new framework for common disturbance is established to design the iterative learning controller and the parameters are optimized properly for much wider applicable field.The algorithm has higher convergence speed and convergence precision than previous algorithms.Simulation results show the effectiveness of the proposed method.