Online Decentralized Decision Making With Inequality Constraints: An ADMM approach
Abstract
We discuss an online decentralized decision making problem where the agents are coupled with affine inequality constraints. Alternating Direction Method of Multipliers (ADMM) is used as the computation engine and we discuss the convergence of the algorithm in an online setting. To be specific, when decisions have to be made sequentially with a fixed time step, there might not be enough time for the ADMM to converge before the scenario changes and the decision needs to be updated. In this case, a suboptimal solution is employed and we analyze the optimality gap given the convergence condition. Moreover, in many cases, the decision making problem changes gradually over time. We propose a warm-start scheme to accelerate the convergence of ADMM and analyze the benefit of the warm-start. The proposed method is demonstrated in a decentralized multiagent control barrier function problem with simulation.
Additional Information
© 2020 IEEE. Manuscript received September 14, 2020; revised November 18, 2020; accepted December 4, 2020. Date of publication December 15, 2020; date of current version April 26, 2021. Recommended by Senior Editor L. Menini. The Caltech component of this research was funded by Ford Motor Company.Attached Files
Submitted - 2011.10023.pdf
Files
Name | Size | Download all |
---|---|---|
md5:7339b095a3168c279e81f3477879b422
|
412.9 kB | Preview Download |
Additional details
- Eprint ID
- 107465
- DOI
- 10.1109/lcsys.2020.3044873
- Resolver ID
- CaltechAUTHORS:20210113-163505184
- Ford Motor Company
- Created
-
2021-01-14Created from EPrint's datestamp field
- Updated
-
2021-08-25Created from EPrint's last_modified field