数据丢失对迭代学习控制的影响分析

针对一类线性系统,分析数据丢失对迭代学习控制算法的影响.首先基于lifting方法给出跟踪误差渐近收敛和单调收敛的条件,并分析收敛速度与数据丢失率的关系,结果表明收敛速度随着数据丢失程度的增加而变慢.其次,为抑制迭代变化扰动的影响,给出一种存在数据丢失时的鲁棒迭代学习控制器设计方法,并将控制器设计问题转化为求取线性矩阵不等式的可行解.仿真示例验证了理论分析的结果以及鲁棒迭代学习控制算法的有效性.

Effect analysis of data dropout on iterative learning control

In this paper, the effect analysis of data dropout on iterative learning control (ILC) for linear discrete-time systems is considered. Using the lifting technique to ILC, the conditions of tracking error for both asymptotic stability and monotonic convergence are first given, and the relationship between convergence speed and data dropout rate is also presented. It is shown that the convergent speed gets slower as dropout rate increases. To attenuate iteration-varying disturbances for ILC system with data dropout, a robust iterative learning controller design is proposed. The controller can be derived in terms of linear matrix inequalities (LMIs) that can be solved by using existing numerical techniques. Some examples are also given to validate the theoretical results and the effectiveness of the proposed robust ILC scheme.