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Optimal bipartite consensus control for heterogeneous unknown multi-agent systems via reinforcement learning

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  • Meng, Hao
  • Pang, Denghao
  • Cao, Jinde
  • Guo, Yechen
  • Niazi, Azmat Ullah Khan

Abstract

This study focuses on addressing optimal bipartite consensus control (OBCC) problems in heterogeneous multi-agent systems (MASs) without relying on the agents' dynamics. Motivated by the need for model-free and optimal consensus control in complex MASs, a novel distributed scheme utilizing reinforcement learning (RL) is proposed to overcome these challenges. The MAS network is randomly partitioned into sub-networks where agents collaborate within each subgroup to attain tracking control and ensure convergence of positions and speeds to a common value. However, agents from distinct subgroups compete to achieve diverse tracking objectives. Furthermore, the heterogeneous MASs considered have unknown first and second-order dynamics, adding to the complexity of the problem. To address the OBCC issue, the policy iteration (PI) algorithm is used to acquire solutions for discrete-time Hamilton-Jacobi-Bellman (HJB) equations while implementing a data-driven actor-critic neural network (ACNN) framework. Ultimately, the accuracy of our proposed approach is confirmed through the presentation of numerical simulations.

Suggested Citation

  • Meng, Hao & Pang, Denghao & Cao, Jinde & Guo, Yechen & Niazi, Azmat Ullah Khan, 2024. "Optimal bipartite consensus control for heterogeneous unknown multi-agent systems via reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 476(C).
    • Handle: RePEc:eee:apmaco:v:476:y:2024:i:c:s0096300324002492
    DOI: 10.1016/j.amc.2024.128785
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    References listed on IDEAS

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    1. Arockia Samy, Stephen & Anbalagan, Pratap, 2023. "Disturbance observer-based integral sliding-mode control design for leader-following consensus of multi-agent systems and its application to car-following model," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Peng, Zhinan & Hu, Jiangping & Shi, Kaibo & Luo, Rui & Huang, Rui & Ghosh, Bijoy Kumar & Huang, Jiuke, 2020. "A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    3. Parivallal, A. & Sakthivel, R. & Wang, Chao, 2022. "Output feedback control for bipartite consensus of nonlinear multi-agent systems with disturbances and switching topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Zhou, Min & Wang, JinRong & Shen, Dong, 2023. "Iterative learning control for continuous-time multi-agent differential inclusion systems with full learnability," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
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    2. Xiang, Aoxue & Zhao, Xinyuan & Ma, Ruicheng, 2026. "Finite-horizon optimal herdability control for hierarchical linear multi-agent systems with signed weighted graphs," Applied Mathematics and Computation, Elsevier, vol. 514(C).
    3. Wang, Guopeng & Cheng, Guozhu & Wang, Wenzhi & Xu, Liang, 2026. "Distributed event-based lag consensus formation control for connected and autonomous vehicles," Chaos, Solitons & Fractals, Elsevier, vol. 202(P1).

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