Data-driven designs of observers and controllers via solving model matching problems

• Published in: Automatica (Oxford) Vol. 156; p. 111196
• Main Authors: Chen, Hongtian, Luo, Hao, Huang, Biao, Jiang, Bin, Kaynak, Okyay
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Closed-loop systems; Data-driven designs; Observers and controllers; Online learning and optimization; Online learning and optimization; Observers and controllers; Closed-loop systems; Data-driven designs
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2023.111196

This paper targets the development of a data-driven framework for designing observers and controllers of close-loop systems with consideration of disturbances. Under this framework, both the design of the observer and optimization of the controller can be achieved by only using data. After parameterizing the feedback control systems subject to disturbances, the first step is to identify data-driven stable kernel and image representations (SKR and SIR) with the aid of time-series data. Then, SKR constructs a data-driven realization of a diagnostic observer. Through disturbance elimination and reference tracking, online optimization using SIR provides a data-driven solution to the model matching problem (MMP) whilst the controller is re-configured and optimized in real time. An example is used to demonstrate the effectiveness of the proposed data-driven designs.