时滞非线性系统的采样迭代学习控制

Sampled-data iterative learning control for nonlinear system with time-delay

针对一类输入时滞非线性系统,提出了一种采样迭代学习控制算法,该算法不含跟踪误差的微分信号,给出了学习算法收敛的充分条件,当不存在初始误差、不确定扰动时,算法在采样点处能实现对期望输出信号的完全跟踪,否则,跟踪误差一致有界,仿真结果表明了该算法的有效性。

A sampled-data iterative learning controller is proposed for a class of nonlinear continuous-time systems with time-delay. The learning algorithm is constructed without any differentiation of the output error, and given a sufficient condition for convergence. Without initial error and disturbances, zero error between the plant output and the desired output can be shown at each sampling instant. If initial errors or disturbances exist, output error is uniform bounded. Simulation results demonstrate the effectiveness of the proposed algorithm.