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Distributed Learning Model Predictive Control for Linear Systems

Stürz, Yvonne R. and Zhu, Edward L. and Rosolia, Ugo and Johansson, Karl H. and Borrelli, Francesco (2020) Distributed Learning Model Predictive Control for Linear Systems. In: 2020 59th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 4366-4373. ISBN 9781728174471.

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This paper presents a distributed learning model predictive control (DLMPC) scheme for distributed linear time invariant systems with coupled dynamics and state constraints. The proposed solution method is based on an online distributed optimization scheme with nearest-neighbor communication. If the control task is iterative and data from previous feasible iterations are available, local data are exploited by the subsystems in order to construct the local terminal set and terminal cost, which guarantee recursive feasibility and asymptotic stability, as well as performance improvement over iterations. In case a first feasible trajectory is difficult to obtain, or the task is non-iterative, we further propose an algorithm that efficiently explores the state-space and generates the data required for the construction of the terminal cost and terminal constraint in the MPC problem in a safe and distributed way. In contrast to other distributed MPC schemes which use structured positive invariant sets, the proposed approach involves a control invariant set as the terminal set, on which we do not impose any distributed structure. The proposed iterative scheme converges to the global optimal solution of the underlying infinite horizon optimal control problem under mild conditions. Numerical experiments demonstrate the effectiveness of the proposed DLMPC scheme.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Stürz, Yvonne R.0000-0001-5729-8491
Rosolia, Ugo0000-0002-1682-0551
Borrelli, Francesco0000-0001-8919-6430
Additional Information:© 2020 IEEE. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 846421. This work was also sponsored by the Office of Naval Research. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of Naval Research or the US government.
Funding AgencyGrant Number
Marie Curie Fellowship846421
Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechAUTHORS:20210121-152557828
Persistent URL:
Official Citation:Y. R. Stürz, E. L. Zhu, U. Rosolia, K. H. Johansson and F. Borrelli, "Distributed Learning Model Predictive Control for Linear Systems," 2020 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Korea (South), 2020, pp. 4366-4373, doi: 10.1109/CDC42340.2020.9303820
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107641
Deposited By: George Porter
Deposited On:21 Jan 2021 23:38
Last Modified:16 Nov 2021 19:04

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