Heterogeneous optimal formation control of nonlinear multi-agent systems with unknown dynamics by safe reinforcement learning

Fatemeh Mahdavi Golmisheh and Saeed Shamaghdari

Applied Mathematics and Computation, 2024, vol. 460, issue C

Abstract: This article presents the problem of distributed training with a decentralized execution policy as a safe, optimal formation control for a heterogeneous nonlinear multi-agent system. The control objective is to guarantee safety while achieving optimal performance. This objective is achieved by introducing novel distributed optimization problems with cost and local control barrier functions (CBFs). Designing an optimal formation controller is defined as optimal performance and modeled by a cost function. A local CBF trains a safe controller to ensure multi-agent systems operate within the safe regions. Instead of optimizing constrained optimization problems, this method generates safe, optimal controllers from unconstrained optimization problems by utilizing local CBFs. As a result, the presented approach has a lower computational cost than constrained optimization problems. It is proven that the proposed controller's optimality and stability are not affected by adding the local CBF to the cost function. A safe, optimal policy is iteratively derived using a new off-policy multi-agent reinforcement learning (MARL) algorithm that does not need knowledge of the agents' dynamics. Finally, the effectiveness of the proposed algorithm is evaluated through simulation of the collision-free problem of the multi-quadrotor formation control.

Keywords: control barrier function; formation control; heterogeneous multi-agent system; nonlinear system; safe reinforcement learning (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S009630032300471X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:460:y:2024:i:c:s009630032300471x

DOI: 10.1016/j.amc.2023.128302

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().