论文标题
通过参考适应来克服迭代学习控制系统中的输出约束
Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation
论文作者
论文摘要
迭代学习控制（ILC）方案可以保证诸如渐近稳定性和单调误差收敛之类的属性，但通常不能确保遵守输出约束。本文的主题是设计适应ILC（RAILC）方案的设计，扩展了现有的ILC系统，并能够符合输出约束。潜在的想法是通过使用对输出进展的保守估计来扩展每个试验的参考。属性作为阈值高于阈值的单调收敛性，对输出约束的尊重得到了正式证明。数值模拟和实验结果增强了我们的理论结果。
Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output's progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results.
