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Prescribed-time distributed resource allocation algorithm for heterogeneous linear multi-agent networks with unbalanced directed communication

Author

Listed:
  • Lian, Yuxiao
  • Zhang, Baoyong
  • Yuan, Deming
  • Yao, Yao
  • Song, Bo

Abstract

In this paper, a prescribed-time distributed algorithm is proposed to solve the resource allocation problem among the heterogeneous linear multi-agent systems over unbalanced directed networks. First, an estimator with prescribed-time convergence performance is designed to cope with the asymmetry of the unbalanced network topology. Then, a novel prescribed-time convergence result that features an adjustable convergence rate is developed. Based on this result, it is shown that the algorithm developed in this paper ensures the agents' outputs accurately reach the optimal solution within a prescribed-time and they are maintained at the optimum thereafter. Furthermore, a parameter selection rule is formulated to reflect the low conservatism of the algorithm. This indicates that the parameters affecting the convergence speed of the algorithm are not necessary to rely on the global information. Finally, the performance of the proposed algorithm is illustrated through simulations.

Suggested Citation

  • Lian, Yuxiao & Zhang, Baoyong & Yuan, Deming & Yao, Yao & Song, Bo, 2025. "Prescribed-time distributed resource allocation algorithm for heterogeneous linear multi-agent networks with unbalanced directed communication," Applied Mathematics and Computation, Elsevier, vol. 503(C).
    • Handle: RePEc:eee:apmaco:v:503:y:2025:i:c:s0096300325002243
    DOI: 10.1016/j.amc.2025.129498
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    References listed on IDEAS

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    1. Xiong, Menghui & Zhang, Baoyong & Yuan, Deming & Zhang, Yijun & Chen, Jun, 2023. "Event-triggered distributed online convex optimization with delayed bandit feedback," Applied Mathematics and Computation, Elsevier, vol. 445(C).
    2. Liu, Chunxia & Lu, Kaihong & Chen, Xiaojie & Szolnoki, Attila, 2023. "Game-theoretical approach for task allocation problems with constraints," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    3. Golmisheh, Fatemeh Mahdavi & Shamaghdari, Saeed, 2024. "Heterogeneous optimal formation control of nonlinear multi-agent systems with unknown dynamics by safe reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 460(C).
    4. Zhao, Huanyu & Park, Ju H. & Zhang, Yulin, 2014. "Couple-group consensus for second-order multi-agent systems with fixed and stochastic switching topologies," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 595-605.
    5. Cheng, Ling & Zhang, Sirui & Wang, Yingchun, 2024. "Distributed optimal capacity allocation of integrated energy system via modified ADMM," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    Full references (including those not matched with items on IDEAS)

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