Collaborative learning model predictive control for repetitive tasks

P. Chanfreut, J.M. Maestre, E.F. Camacho, F. Borrelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers
Pages5291-5296
Number of pages6
ISBN (Electronic)9781665467612
DOIs
Publication statusPublished - 10 Jan 2023
Externally publishedYes
Event61st IEEE Conference on Decision and Control, CDC 2022 - The Marriott Cancún Collection, Cancun, Mexico
Duration: 6 Dec 20229 Dec 2022
Conference number: 61
https://cdc2022.ieeecss.org/

Conference

Conference61st IEEE Conference on Decision and Control, CDC 2022
Abbreviated titleCDC 2022
Country/TerritoryMexico
CityCancun
Period6/12/229/12/22
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.

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