论文标题
关于基于优化的约束迭代学习控制的鲁棒性
On Robustness in Optimization-Based Constrained Iterative Learning Control
论文作者
论文摘要
迭代学习控制（ILC）是重复任务的控制策略，其中利用了以前的运行信息以提高未来的绩效。基于优化的ILC（OB-ILC）是受约束ILC的强大设计框架，其中该过程中的测量值集成到优化算法中，以提供针对噪声和建模误差的鲁棒性。本文提出了一个基于前向拆分算法的约束线性过程的强大ILC控制器。它证明了如何利用结构化的不确定性信息来确保限制满意度，并通过结合单调操作员理论和稳健控制的概念来在迭代域中提供严格的稳定性分析。精确运动阶段的数值模拟支持理论结果。
Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based ILC (OB-ILC) is a powerful design framework for constrained ILC where measurements from the process are integrated into an optimization algorithm to provide robustness against noise and modelling error. This paper proposes a robust ILC controller for constrained linear processes based on the forward-backward splitting algorithm. It demonstrates how structured uncertainty information can be leveraged to ensure constraint satisfaction and provides a rigorous stability analysis in the iteration domain by combining concepts from monotone operator theory and robust control. Numerical simulations of a precision motion stage support the theoretical results.
