Frequency Response Function-Based Learning Control: Analysis and Design for Finite-Time Convergence

• 成果类型: Article
• 署名作者: de Rozario, Robin; Oomen, Tom
• 署名单位: Eindhoven University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3159489
• 发表日期: 2023
• 页码: 1807-1814
• 关键词: Task analysis; CONVERGENCE; Finite impulse response filters; uncertainty; Parametric statistics; Design methodology; computational modeling; Frequency response; Iterative learning control; Linear systems; mechatronics; uncertain systems

摘要

Iterative learning control (ILC) enables substantial performance improvement by using past operational data in combination with approximate plant models. The aim of this article is to develop an ILC framework based on nonparametric frequency response function (FRF) models that requires very limited modeling effort. These FRF models describe the behavior of a system in periodic steady state, yet are employed for the control of arbitrary finite-length tasks. A detailed analysis and design framework is developed to construct noncausal learning filters directly from uncertain FRF models, that achieve ILC convergence for arbitrary tasks. The resulting framework provides a unification between ILC and iterative inversion-based control, where the latter is a learning method specifically developed for periodic tasks.