Combining Prior Knowledge and Data for Robust Controller Design

• 成果类型: Article
• 署名作者: Berberich, Julian; Scherer, Carsten W.; Allgower, Frank
• 署名单位: University of Stuttgart; University of Stuttgart
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3209342
• 发表日期: 2023
• 页码: 4618-4633
• 关键词: data-driven control; identification for control; Linear matrix inequalities (LMIs); Linear systems; Robust control

摘要

We present a framework for systematically combining data of an unknown linear time-invariant system with prior knowledge on the system matrices or on the uncertainty for robust controller design. Our approach leads to linear matrix inequality (LMI)-based feasibility criteria that guarantee stability and performance robustly for all closed-loop systems consistent with the prior knowledge and the available data. The design procedures rely on a combination of multipliers inferred via prior knowledge and learnt from measured data, where for the latter, a novel and unifying disturbance description is employed. While large parts of the article focus on linear systems and input-state measurements, we also provide extensions to robust output-feedback design based on noisy input-output data and against nonlinear uncertainties. We illustrate through numerical examples that our approach provides a flexible framework for simultaneously leveraging prior knowledge and data, thereby reducing conservatism and improving performance significantly if compared to black-box approaches to data-driven control.