反馈辅助PD型迭代学习控制:初值问题及修正策略

讨论迭代初态与期望初态存在固定偏移情形下 的迭代学习控制问题, 提出带有反馈辅助项的PD型迭代学习控制算法, 可实现系统输出对期望轨迹的渐近跟踪. 为了进一步实现输出轨迹在预定有限区间上对期望轨迹的完全跟踪, 提出分别带有初始修正作用和终态吸引的学习算法. 文中给出所提出的学习算法的极限轨迹, 并对学习算法进行收敛性分析, 推导出收敛性充分条件, 可用于学习增益的确定. 通过数值结果, 验证所提学习算法的有效性.

Feedback-aided PD-type Iterative Learning Control：Initial Condition Problem and Rectifying Strategies

This paper addresses the problem of iterative learning control for systems in the presence of a fixed initial shift. A feedback-aided PD-type learning algorithm is proposed, and the convergence analysis indicates that such a learning algorithm can ensure that the tracking error achieves asymptotic convergence with respect to time, as the iteration approaches infinity. Furthermore, the initial rectifying and terminal converging strategies are adopted respectively to form learning algorithms for eliminating the effect of the fixed initial shift. It is shown that the system output converges to the desired trajectory over a pre-specified time interval no matter what value the fixed initial shift takes. Numerical results are presented to demonstrate the effectiveness of the proposed learning algorithms.