论文部分内容阅读
针对不确定时滞系统讨论带有初始修正的PD型迭代学习控制算法，给出这类系统的输出极限轨迹，以及迭代输出收敛于该极限轨迹的较弱的充分条件，其中将初始条件放宽为某任意可达初始状态函数的可重复性．仿真结果表明这种算法中的初始修正项可以有效地抑制初始偏移的影响。
A PD-type iterative learning control algorithm with initial correction is discussed for an uncertainly delayed system. The output limit trajectory of such systems and the weaker sufficient conditions for the iterative output to converge to the limit trajectory are given. The initial conditions are relaxed to Repeatability of an arbitrary reachable initial state function. Simulation results show that the initial correction term in this algorithm can effectively suppress the influence of initial offset.