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Robust data-driven predictive control using reachability analysis
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Jacobs Univ Bremen, Comp Sci & Elect Engn Dept, Bremen, Germany..ORCID iD: 0000-0003-2941-519x
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Univ Calif Berkeley, Model Predict Control Lab, Berkeley, CA USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-9940-5929
2022 (English)In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671, Vol. 68Article in journal (Refereed) Published
Abstract [en]

We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive con-trol, a controller utilizing data-driven reachable regions is proposed. The data-driven reachable regions are based on a matrix zonotope recursion and are computed based on only noisy input-output data of a trajectory of the system. We assume that measurement and process noise are contained in bounded sets. While we assume knowledge of these bounds, no knowledge about the statistical properties of the noise is assumed. In the noise-free case, we prove that the presented purely data-driven control scheme results in an equivalent closed-loop behavior to a nominal model predictive control scheme. In the case of measurement and process noise, our proposed scheme guarantees robust constraint satisfaction, which is essential in safety-critical applications. Numerical experiments show the effectiveness of the proposed data-driven controller in comparison to model-based control schemes.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 68
Keywords [en]
Predictive control, Reachability analysis, Data -driven methods, Zonotope
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-323221DOI: 10.1016/j.ejcon.2022.100666ISI: 000901439100022Scopus ID: 2-s2.0-85134325950OAI: oai:DiVA.org:kth-323221DiVA, id: diva2:1730729
Note

QC 20230125

Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2023-01-25Bibliographically approved

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Alanwar, AmrStuerz, YvonneJohansson, Karl H.

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