The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
Open accessAbstract
We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant systems. The scheme uses an implicit model description based on behavioral systems theory and past measured trajectories. In particular, it does not require any prior identification step, but only an initially measured input-output trajectory as well as an upper bound on the order of the unknown system. First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise. For bounded additive output measurement noise, we propose a robust modification of the scheme, including a slack variable with regularization in the cost. We prove that the application of this robust MPC scheme in a multistep fashion leads to practical exponential stability of the closed loop w.r.t. the noise level. The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme.
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Cites
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Lennart Ljung, A. (1987) · Cited by 17,690
Constrained model predictive control: Stability and optimality
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From model-based control to data-driven control: Survey, classification and perspective
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Nonlinear Model Predictive Control
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A note on persistency of excitation
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Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness
De Persis, C., & Tesi, P. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2019.2959924 · Cited by 983
Subspace Methods for System Identification
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Data-Enabled Predictive Control: In the Shallows of the DeePC
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Cited by
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Zhang, K., Chen, K., Li, Z., Chen, J., & Zheng, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3329484 · Cited by 24
Metadata
- Authors
- Berberich, J., Kohler, J., Muller, M. A., & Allgower, F.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2021-04
- Volume
- 66
- Issue
- 4
- Pages
- 1702-1717
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- 2334-3303
- OpenAlex
- W2952672134
- Cited by
- 803
- References
- 29
- FWCI
- 43.46
- Type
- journal article
- Language
- en
- Open access
- green
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