Details
| Original language | English |
|---|---|
| Title of host publication | 2020 59th IEEE Conference on Decision and Control, CDC 2020 |
| Pages | 1260-1267 |
| Number of pages | 8 |
| ISBN (electronic) | 9781728174471 |
| Publication status | Published - 2020 |
| Event | 2020 59th IEEE Conference on Decision and Control (CDC) - Jeju, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Volume | 2020-December |
| ISSN (Print) | 0743-1546 |
| ISSN (electronic) | 2576-2370 |
Abstract
Keywords
- eess.SY, cs.SY, math.OC
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Modelling and Simulation
Cite this
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2020 59th IEEE Conference on Decision and Control, CDC 2020. 2020. p. 1260-1267 9303965 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Robust Constraint Satisfaction in Data-Driven MPC
AU - Berberich, Julian
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding Information: This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Julian Berberich, and the International Research Training Group “Soft Tissue Robotics” (GRK 2198/1).
PY - 2020
Y1 - 2020
N2 - We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.
AB - We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.
KW - eess.SY
KW - cs.SY
KW - math.OC
UR - http://www.scopus.com/inward/record.url?scp=85099876899&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9303965
DO - 10.1109/CDC42340.2020.9303965
M3 - Conference contribution
SN - 978-1-7281-7446-4
SN - 978-1-7281-7448-8
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1260
EP - 1267
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
T2 - 2020 59th IEEE Conference on Decision and Control (CDC)
Y2 - 14 December 2020 through 18 December 2020
ER -