论文标题
迭代学习控制的基本跟踪性问题
Fundamental Trackability Problems for Iterative Learning Control
论文作者
论文摘要
通常，经典的迭代学习控制（ILC）方法着重于寻找重复系统的设计条件，以实现任何指定轨迹的完美跟踪，而他们忽略了ILC的基本问题：指定的轨迹是可跟踪的，还是同等的，是否存在对重复性系统的某些投入，以构成规定的特定型号的重复性系统？当前的论文有助于解决这个问题。不仅是针对ILC中任何指定轨迹正式引入的可跟踪性概念，而且还建立了一些相关的可跟踪性标准。此外，基于利用功能性库奇序列（FCS）的属性，为ILC开发了一种新的收敛分析方法，从而为ILC桥接了跟踪性与完美的跟踪任务之间的关系。给出了模拟示例，以验证ILC的提出的可跟踪性标准和FCS诱导的收敛分析方法的有效性。
Generally, the classic iterative learning control (ILC) methods focus on finding design conditions for repetitive systems to achieve the perfect tracking of any specified trajectory, whereas they ignore a fundamental problem of ILC: whether the specified trajectory is trackable, or equivalently, whether there exist some inputs for the repetitive systems under consideration to generate the specified trajectory? The current paper contributes to dealing with this problem. Not only is a concept of trackability introduced formally for any specified trajectory in ILC, but also some related trackability criteria are established. Further, the relation between the trackability and the perfect tracking tasks for ILC is bridged, based on which a new convergence analysis approach is developed for ILC by leveraging properties of a functional Cauchy sequence (FCS). Simulation examples are given to verify the effectiveness of the presented trackability criteria and FCS-induced convergence analysis method for ILC.
