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对于非严格重复线性时变连续系统,初始迭代条件和参考轨迹在一定带宽范围内都是迭代变化的.提出一种非严格的迭代学习方法来控制跟踪整流.通过该方法所获得的控制器,能保证闭环系统的所有信号是全局有界的,能够使超出初始时间间隔的输出跟踪误差收敛到一个小的残差集内,该残差集大小取决于输入矩阵的估测误差.尤其是当输入矩阵已知的情况下,能够让超出的初始时间间隔输出跟踪误差趋近于零.
For non-strictly repeating linear time-varying continuous systems, the initial iteration conditions and the reference trajectories change iteratively over a certain bandwidth, and a non-strict iterative learning method is proposed to control the tracking rectification. The controller obtained by this method, It can be guaranteed that all the signals of the closed-loop system are globally bounded and the output tracking errors beyond the initial time interval can be converged into a small residual set whose size depends on the estimation error of the input matrix, especially when With an input matrix known, the output tracking error can be brought closer to zero for the out-of-range initial interval.