离散系统单调收敛高阶迭代学习控制

研究了一类离散线性时不变系统高阶迭代学习控制在相应范数意义下的单调收敛条件,给出了对给定目标函数迭代学习控制参数的最优解,并讨论了其收敛速度。常见的离散P型、D型及PD型ILC算法均可看作是所讨论算法的特例。仿真结果表明采用给出的最优设计具有更好的迭代学习单调收敛性能。

HIGH-ORDER ITERATIVE LEARNING CONTROL FOR DISCRETE-TIME SYSTEM BASED ON MONOTONIC CONVERGENCE

Monotonic convergence condition of high-order iterative learning control (ILC) in a suitable norm topology is discussed for a class of discrete-time linear time-invariant system. Optimal parameters design of the ILC scheme is presented and corresponding convergence rate is analyzed. The commonly used algorithms of discrete P, D, PD iterative learning control can be considered examples of the proposed updating law. Simulation results show that better monotonic convergence performance is achieved by using the optimal design.