Distributed Constrained Optimal Coordination of Multiple Heterogeneous Systems Over a Directed Communication Network

This paper investigates the distributed optimal coordination problem and distributed constrained optimal coordination problem for multiple heterogeneous linear systems over a directed communication network. The objective is to develop distributed optimization algorithms for a set of heterogeneous linear agents over a directed communication network, which will drive the decision variables of the agents to the optimal state with optimal system performance. Firstly, a new distributed optimal coordination algorithm is developed, comprising an output regulation tracking controller and a surplus-based optimal policy generator. This is achieved by utilizing the output regulation technique and surplus-based consensus method. Secondly, a projected distributed optimal coordination algorithm is designed to address the distributed constrained optimal coordination problem, where the decision variables of the agents are constrained by local feasible set constraints and network resource constraints. In comparison to previous distributed optimal coordination studies, the agent dynamics and communication framework have been significantly expanded, and this represents the first investigation of the distributed constrained optimal coordination problem for heterogeneous linear systems over weight-unbalanced directed communication networks. Finally, the practical applications of multiple UAV systems and battery energy storage systems are presented, thereby demonstrating the efficacy of the proposed distributed optimal coordination and distributed constrained optimal coordination algorithms in a real-world context.

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Publication Details

Journal
IEEE Transactions on Automatic Control
Published
2025-04-14
DOI
https://doi.org/10.1109/tac.2025.3560512
Citations
9
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
13.38

Funders

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article

Distributed Constrained Optimal Coordination of Multiple Heterogeneous Systems Over a Directed Communication Network

Yongfang Liu, Chengxin Xian, Guanrong Chen, Guanghui Wen et al.
9 citations
IEEE Transactions on Automatic Control
Distributed Control Multi-Agent Systems
13.38
article

Distributed Constrained Optimal Coordination of Multiple Heterogeneous Systems Over a Directed Communication Network

Yongfang Liu, Chengxin Xian, Guanrong Chen, Guanghui Wen, Yu Zhao
article en
9 citations

Abstract

This paper investigates the distributed optimal coordination problem and distributed constrained optimal coordination problem for multiple heterogeneous linear systems over a directed communication network. The objective is to develop distributed optimization algorithms for a set of heterogeneous linear agents over a directed communication network, which will drive the decision variables of the agents to the optimal state with optimal system performance. Firstly, a new distributed optimal coordination algorithm is developed, comprising an output regulation tracking controller and a surplus-based optimal policy generator. This is achieved by utilizing the output regulation technique and surplus-based consensus method. Secondly, a projected distributed optimal coordination algorithm is designed to address the distributed constrained optimal coordination problem, where the decision variables of the agents are constrained by local feasible set constraints and network resource constraints. In comparison to previous distributed optimal coordination studies, the agent dynamics and communication framework have been significantly expanded, and this represents the first investigation of the distributed constrained optimal coordination problem for heterogeneous linear systems over weight-unbalanced directed communication networks. Finally, the practical applications of multiple UAV systems and battery energy storage systems are presented, thereby demonstrating the efficacy of the proposed distributed optimal coordination and distributed constrained optimal coordination algorithms in a real-world context.

IEEE Transactions on Automatic ControlVol. 70(10)
City University of Hong Kong (HK), Northwestern Polytechnical University (CN), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Distributed Control Multi-Agent Systems
13.38
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