一类非线性相似组合大系统的迭代学习控制

Iterative Learning Control for a Class of Nonlinear Similar Composite Systems

利用状态反馈部分线性化技术研究了一类非线性相似组合大系统的迭代学习控制问题。与现有结果不同的是，它不直接研究系统本身，而是构造一个适当的反馈，然后对闭环系统给出其迭代学习收敛的充分条件，且每次迭代的初始状态均可通过理想信号直接确定。研究表明相似结构能简化系统的分析与设计。

A class of nonlinear composite large-scale systems with similar structure is analyzed by using partially linearization technique. Unlike the existing results, a kind of feedback law is given to construct a closed-loop systems at first. Then, a sufficient condition guarateeing the convergence of iterative learning control for the systems is shown, and what is more, the iterative initial states are got by the desired output directly. It's shown that similar structure may be used to simplify systems analyses and design.