CaltechAUTHORS
  A Caltech Library Service

Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control

Zhu, Edward L. and Stürz, Yvonne R. and Rosolia, Ugo and Borrelli, Francesco (2020) Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control. In: 2020 59th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 6198-6203. ISBN 9781728174471. https://resolver.caltech.edu/CaltechAUTHORS:20210121-152557974

[img] PDF - Accepted Version
See Usage Policy.

456Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20210121-152557974

Abstract

We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coupled state constraints. By performing the same task iteratively, data from previous task executions is used to construct and improve local time-varying safe sets and an approximate value function. These are used in a decoupled MPC problem as terminal sets and terminal cost functions. Our framework results in a decentralized controller, which requires no communication between agents over each iteration of task execution, and guarantees persistent feasibility, finite-time closed-loop convergence, and non-decreasing performance of the global system over task iterations. Numerical experiments of a multi-vehicle collision avoidance scenario demonstrate the effectiveness of the proposed scheme.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/cdc42340.2020.9303903DOIArticle
https://arxiv.org/abs/2004.01298arXivDiscussion Paper
ORCID:
AuthorORCID
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 partially funded by the grant ONR-N00014-18-1-2833.
Funders:
Funding AgencyGrant Number
Marie Curie Fellowship846421
Office of Naval Research (ONR)N00014-18-1-2833
Record Number:CaltechAUTHORS:20210121-152557974
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210121-152557974
Official Citation:E. L. Zhu, Y. R. Stürz, U. Rosolia and F. Borrelli, "Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control," 2020 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Korea (South), 2020, pp. 6198-6203, doi: 10.1109/CDC42340.2020.9303903
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107642
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Jan 2021 15:07
Last Modified:03 Feb 2021 22:29

Repository Staff Only: item control page