Contents I. Introduction
Iterative learning control (ILC), proposed in [1] for robotics application, is an effective strategy for precise tracking objectives. By imitating the essential principle of a human’s learning ability resulting in excellent performance for a given task through repetition, they successfully enabled industrial robotics with strong coupling nonlinearities to perform trajectory-tracking tasks rapidly and accurately, such as [2]. The basic process is as follows: For a robot performing a trajectory-tracking task on a finite time interval, the error information collected in previous operations is used to rectify the control input, resulting in gradual improvement of tracking performance as the iteration number increases. Clearly, the essential learning mechanism of this strategy is realized by its iterative characteristic, which is similar to other iterative-type controllers, such as [3] and [4], for various applications. Therefore, ILC is suitable for systems with the nature of repetitive property. The given control objective can be improved through iterative correction. Moreover, within a given time range, the nonlinear coupled dynamics with high uncertainty can be addressed by using a considerably simple algorithm while achieving a highly accurate tracking to the desired reference. After more than three decades of development, ILC has made significant advancements both in theory and engineering applications [5]–[8] and generated a large number of related research, such as distributed ILC, robust ILC, initial resetting condition, and adaptive ILC. For instance, in [9], an adaptive iterative learning reliable control (AILRC) strategy is developed to deal with actuator faults, control input saturation, and time-varying state delays simultaneously.
