Cooperative Tuning of Multi-Agent Optimal Control Systems

Zehui Lu*, Wanxin Jin, Shaoshuai Mou, Brian D.O. Anderson

*Corresponding author for this work

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

    5 Citations (Scopus)

    Abstract

    This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective functions, or controllers in a coordinated way to minimize the sum of their loss functions. Different from classical techniques for tuning parameters in a controller, we allow tunable parameters appearing in both the system dynamics and the objective functions of each agent. A framework is developed to allow all agents to reach a consensus on the tunable parameter, which minimizes team loss. The key idea of the proposed algorithm rests on the integration of consensus-based distributed optimization for a multi-agent system and a gradient generator capturing the optimal performance as a function of the parameter in the feedback loop tuning the parameter for each agent. Both theoretical results and simulations for a synchronous multi-agent rendezvous problem are provided to validate the proposed method for cooperative tuning of multi-agent optimal control.

    Original languageEnglish
    Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages571-576
    Number of pages6
    ISBN (Electronic)9781665467612
    DOIs
    Publication statusPublished - 2022
    Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
    Duration: 6 Dec 20229 Dec 2022

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    Volume2022-December
    ISSN (Print)0743-1546
    ISSN (Electronic)2576-2370

    Conference

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

    Fingerprint

    Dive into the research topics of 'Cooperative Tuning of Multi-Agent Optimal Control Systems'. Together they form a unique fingerprint.

    Cite this