This paper is concerned with the amplitude boundedness problem of adaptive iterative learning control (AILC) for robot manipulators operating with iteration-dependent periods. By introducing virtual memory slots for storing historical data, a practical AILC method is proposed to achieve the segment-wise learning. This method requires less memory storage for historical information of previous iterations, especially in comparison with that of the conventional AILC methods using point-wise learning strategies. It is shown that not only the energy boundedness but also the amplitude boundedness of estimates and inputs of practical AILC can be guaranteed. Moreover, the practical AILC method can achieve the perfect tracking objective regardless of iteration-dependent periods when the robot manipulators have a persistent full learning property. In addition, a solution to the visual manipulator platform is provided and deployed based on Coppeliasim and Matlab, which helps to show the amplitude boundedness of learning results and the perfect tracking performances of the proposed practical AILC method for robot manipulators.

本文关注的是迭代依赖周期操作的机器人操纵器的自适应迭代学习控制（AILC）的幅度有界问题。通过引入虚拟内存槽来存储历史数据，提出了一种实用的AILC方法来实现分段学习。该方法需要较少的内存存储来存储先前迭代的历史信息，特别是与使用逐点学习策略的传统 AILC 方法相比。结果表明，实际AILC的估计和输入不仅能保证能量有界，而且能保证幅值有界。此外，当机器人机械臂具有持续的完全学习特性时，实用的AILC方法可以实现完美的跟踪目标，而不管迭代依赖周期如何。此外，还提供并部署了基于Coppeliasim和Matlab的视觉机械臂平台解决方案，有助于展示学习结果的幅度有界性以及所提出的机器人机械臂实用AILC方法的完美跟踪性能。