基于初次控制信号提取的迭代学习控制方法

在同一迭代学习控制(Iterative learning control, ILC)系统中,选取一个合适的初次迭代控制信号相对于从零开始学习达到目标跟踪精度的迭代次数更少.本文针对线性系统研究从历次轨迹跟踪控制信息中通过期望轨迹匹配提取初次迭代控制信号的方法.首先提出了一种轨迹基元优化匹配算法,在满足一定相似度的情况下,通过轨迹分割、平移与旋转变换,在轨迹基元库中寻找与当前期望轨迹叠合的轨迹基元组合轨迹;进而,依据线性叠加原理和轨迹叠合的平移矢量与旋转变换矩阵,获取与期望轨迹叠合的轨迹基元控制信号;在此基础上,通过轨迹基元控制信号串联组合和时间尺度变换,提取出当前期望轨迹的初次迭代控制信号.对于初次迭代控制信号在拼接处由边界条件差异引起的干扰,给出了一种H∞反馈辅助ILC方法.最后,在XYZ三轴运动平台实现所提算法,实验结果表明本文所提方法的有效性.

Iterative Learning Control Based on Extracting Initial Iterative Control Signals

In the same iterative learning control (ILC) system, compared with an iterative process that starts from blind, selecting a proper initial control signal has fewer iterations to achieve the target tracking accuracy. This paper studies the method of extracting the initial iterative control signal from all previous trajectory tracking control information through matching the desired trajectory for linear systems. First, a trajectory primitive optimal matching algorithm is proposed. In the case of a given similarity index, some trajectory primitive combinations superposed on the current desired trajectory are found from the trajectory library through the trajectory segmentation, translation and rotation transformation. Further, according to the linear superposition principle, translation vectors and rotation matrices on superposing trajectory, the control signals of the trajectory primitives superimposed on the desired trajectory are acquired. By assembling the trajectory primitive control signals in series and transforming their time scales, an initial iteration control signal of the current desired trajectory is extracted. For the interference caused by the difference of the boundary condition of the initial iteration control signals at the splicing, an ${H_\infty }$ feedback assistant ILC method is presented. Finally, the proposed algorithm is implemented on the $XYZ$ triaxial motion platform, the experiment results show that the proposed method is effective.