Gaussian process based distributed model predictive control for multi-agent systems using sequential convex programming and ADMM

Viet Anh Le, Truong X. Nghiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper develops a distributed algorithm for data-driven Distributed Model Predictive Control (DMPC) for multi-agent control systems, where the agents' dynamics are modeled by Gaussian Processes (GPs). A multi-agent control system with a coordinator is considered, in which computation and data must be distributed among the agents and the coordinator. We employ the linearized Gaussian Process (linGP) concept, proposed in our previous works, to sequentially approximate the stochastic latent processes of the GP models in a Sequential Convex Programming (SCP) framework, leading to a convex linGP-DMPC subproblem, which is solved cooperatively by the agents with the ADMM algorithm. The resulting distributed algorithm, called linGP-SCP-ADMM, can solve nonconvex GP-DMPC for multi-agent systems effectively since the data and computation are distributed among the agents. The effectiveness and advantages of the proposed algorithm are evaluated by simulation in a formation control example.

Original languageEnglish (US)
Title of host publicationCCTA 2020 - 4th IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-36
Number of pages6
ISBN (Electronic)9781728171401
DOIs
StatePublished - Aug 2020
Event4th IEEE Conference on Control Technology and Applications, CCTA 2020 - Virtual, Montreal, Canada
Duration: Aug 24 2020Aug 26 2020

Publication series

NameCCTA 2020 - 4th IEEE Conference on Control Technology and Applications

Conference

Conference4th IEEE Conference on Control Technology and Applications, CCTA 2020
Country/TerritoryCanada
CityVirtual, Montreal
Period8/24/208/26/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Instrumentation

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