基于影响函数的迭代控制器2-D分析与设计

2-D Analysis and Design of Iterative Controller Based on Influence Function

从增益和收敛性分析两个角度讨论了迭代学习控制算法出现的问题,并在此基础上提出了具有影响函数的新型迭代学习控制算法.该算法通过影响函数获得了以前时刻控制信息对当前控制量的影响,并构成非线性控制器.在线性离散时变系统下进行了2-D理论的收敛性分析,结果表明,其收敛条件与普通线性控制算法并无不同;但是由于采用了更多的以往有效信息,所提算法收敛速度更快,控制性能更好.最后通过两个示例结果对该算法进行了验证.

The problems of gain and convergence analysis in iterative learning control （ILC） are discussed, and a new ILC algorithm with influence function is presented. This algorithm achieves the influence of the previous controlling information on the current controlling via influence function, which forms a nonlinear controller. Convergence analysis is made in discrete linear time-varying （LTV） system with the two-dimension theory, and the results show that the convergence condition of the presented algorithm is the same as that of the traditional ILC algorithms. However, since more efficient previous information is used, the new ILC algorithm has a faster convergence rate and a better controlling performance. The results of two examples are given to validate the effectiveness of the proposed algorithm.