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迭代学习控制(ILC)利用系统的重复性不断改进控制性能。本文讨论一类具有扰动的非线性、时变系统高阶迭代学习控制算法及其迭代学习收敛的充分条件,并与D型迭代学习算法相比,讨论典型PD高阶ILC算法的收敛速度。仿真结果证实高阶ILC算法具有更快的收敛速度,并且当系统满足收敛条件、不确定项及输出扰动项有界时迭代学习收敛。
Iterative Learning Control (ILC) takes advantage of system repeatability to continuously improve control performance. In this paper, we discuss the sufficient conditions for a class of high order iterative learning control algorithms with perturbations and their iterative learning convergence. Compared with the D-type iterative learning algorithm, the convergence speed of typical PD high-order ILC algorithms is discussed. The simulation results show that the higher-order ILC algorithm has faster convergence rate, and converges iteratively when the system satisfies the convergence condition, the uncertainties and the output perturbation term bounded.