Abstract
This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost; a coordinator, which assigns the tasks to the agents; and the cloud, which stores data to facilitate the agents' learning. The tasks consist in traveling repeatedly between a set of target states while satisfying input and state constraints. In turn, the state constraints may change in time for each of the possible tasks. To deal with it, different modes of operation, which establish different restrictions, are defined. The agents' inputs are found by solving local model predictive control (MPC) problems where the terminal set and cost are defined from previous trajectories. The data collected by each agent is uploaded to the cloud and made accessible to all their peers. Likewise, similarity between tasks is exploited to accelerate the learning process. The applicability of the proposed approach is illustrated by simulation results.
Original language | English |
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Title of host publication | 2022 IEEE 61st Conference on Decision and Control, CDC 2022 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 5291-5296 |
Number of pages | 6 |
ISBN (Electronic) | 9781665467612 |
DOIs | |
Publication status | Published - 10 Jan 2023 |
Externally published | Yes |
Event | 61st IEEE Conference on Decision and Control, CDC 2022 - The Marriott Cancún Collection, Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 Conference number: 61 https://cdc2022.ieeecss.org/ |
Conference
Conference | 61st IEEE Conference on Decision and Control, CDC 2022 |
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Abbreviated title | CDC 2022 |
Country/Territory | Mexico |
City | Cancun |
Period | 6/12/22 → 9/12/22 |
Internet address |
Funding
P. Chanfreut, J. M. Maestre and E. F. Camacho are with the Department of Systems and Automation Engineering, University of Seville, Spain (emails: {pchanfreut,pepemaestre,efcamacho}@us.es) F. Borrelli is with the Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94701 USA (e-mail: [email protected]) This work is supported by the Spanish Training Program for Academic Staff under Grant FPU17/02653, by the Manuel Gayán Buiza Award, by the European Research Council Advanced Grant OCONTSOLAR under Grant SI-1838/24/2018, and by the Spanish MCIN/AEI/10.13039/501100011033 Project C3PO-R2D2 under Grant PID2020-119476RB-I00. Also, we would like to thank Dr. Filiberto Fele for his feedback regarding the article.