Source data for: Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection

This article proposes an effective iterative learning control (ILC) approach based on successive projection scheme for repetitive systems with randomly varying trial lengths. A modified ILC problem is formulated to extend the classical ILC task description to incorporate a randomly varying trial length, while its design objective considers the mathematical expectation of its tracking error to evaluate the task performance. To solve this problem, this article employs the successive projection framework to give an iterative input signal update law by defining the corresponding convex sets based on the design requirements. This update law further yields an ILC algorithm, whose convergence properties are proved to be held under mild conditions. In addition, the input signal constraint can be embedded into the design without violating the convergence properties to obtain an alternative algorithm. The performance of the proposed algorithms is verified using a numerical model to show the effectiveness at occasions with and without input constraints.

本文针对试验长度随机变化的重复系统，提出了一种基于逐次投影（Successive Projection）策略的有效迭代学习控制（Iterative Learning Control，ILC）方法。本文构建了一类改进的ILC问题，将经典ILC任务描述扩展至可纳入试验长度随机变化的场景，同时以跟踪误差的数学期望作为任务性能的评价指标。为求解该问题，本文基于设计需求定义相应的凸集，采用逐次投影框架给出迭代输入信号更新律。该更新律进一步推导得到一种ILC算法，其收敛性在温和条件下可得到证明。此外，可将输入信号约束嵌入设计过程且不破坏收敛性，以此得到一种备选算法。本文通过数值模型对所提算法的性能进行验证，结果表明其在存在与不存在输入约束的场景下均具备有效性。