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Consensus of a new multi-agent system via multi-task, multi-control mechanism and multi-consensus strategy

Published: 17 July 2024 Publication History

Abstract

This paper studies the consensus problem of a new multi-agent system via multi-task, multi-control mechanism and multi-consensus strategy. Firstly, two multi-task algorithms are proposed, which can reasonably select some agent nodes with better performance to form the multi-agent system according to different tasks. Secondly, a multi-control protocol designed in this paper integrates continuous control mechanism, impulsive control mechanism and hybrid control mechanism, which has strong applicability and efficiency. Thirdly, a multi-consensus strategy is designed, and it can be converted into “average consensus strategy”, “leader-following consensus strategy” and “hybrid consensus strategy” through different parameter settings, which greatly broadens the application field of the multi-agent system. We construct a dynamic model for the proposed multi-agent system, analyze it by using matrix measure and Lyapunov stability theory, and obtain the theorems that enable it to achieve multiple consensus. Finally, the efficiency and practicability of our theories are verified through several simulation experiments. We further carry out some comparative analysis with some relevant papers, and obtain the superiority of our theories in multiple indicators.

References

[1]
Abdoos M., A cooperative multi-agent system for traffic signal control using game theory and reinforcement learning, IEEE Intell. Transp. Syst. Mag. 13 (4) (2021) 6–16.
[2]
Li Y.Z., Wu Y.Q., He S.H., Distributed consensus control for a group of autonomous marine vehicles with nonlinearity and external disturbances, Neurocomputing 443 (2021) 380–387.
[3]
Putra S.A., Trilaksono B.R., Riyansyah M., et al., Intelligent sensing in multiagent-based wireless sensor network for bridge condition monitoring system, IEEE Internet Things J. 6 (3) (2019) 5397–5410.
[4]
Putra S.A., Trilaksono B.R., Riyansyah M., et al., Multiagent architecture for bridge capacity measurement system using wireless sensor network and weight in motion, IEEE Trans. Instrum. Meas. 70 (2021) 1–14.
[5]
Ayanian N., Robuffo G.P., Fitch R., et al., Guest editorial: special issue on multi-robot and multi-agent systems, Auton. Robots 44 (2020) 297–298.
[6]
Moorthy S., Joo Y.H., Distributed leader-following formation control for multiple nonholonomic mobile robots via bioinspired neurodynamic approach, Neurocomputing 492 (2022) 308–321.
[7]
Blas H., Mendes A.S., Encinas F.G., et al., A multi-agent system for data fusion techniques applied to the internet of things enabling physical rehabilitation monitoring, Appl. Sci. 11 (1) (2020) 331.
[8]
Zheng Y.L., Liu Q.S., A review of distributed optimization: Problems, models and algorithms, Neurocomputing 483 (2022) 446–459.
[9]
Wang G., Wang C., Ding Z., et al., Distributed consensus of nonlinear multi-agent systems with mismatched uncertainties and unknown high-frequency gains, IEEE Trans. Circuits Syst. II 68 (3) (2021) 938–942.
[10]
Zhang P., Xue H., Gao S., et al., Distributed adaptive consensus tracking control for multi-agent system with communication constraints, IEEE Trans. Parallel Distrib. Syst. 32 (6) (2021) 1293–1306.
[11]
Wang T., Zhang H., Zhao Y., Consensus of multi-agent systems under binary-valued measurements and recursive projection algorithm, IEEE Trans. Automat. Control 65 (6) (2020) 2678–2685.
[12]
Wang Q., Dong X.W., Yu J.L., et al., Predefined finite-time output containment of nonlinear multi-agent systems with leaders of unknown inputs, IEEE Trans. Circuits Syst. I. Regul. Pap. 68 (8) (2021) 3436–3448.
[13]
Xie X., Wei T.D., Li X.D., Hybrid event-triggered approach for quasi-consensus of uncertain multi-agent systems with impulsive protocols, IEEE Trans. Circuits Syst. I. Regul. Pap. 69 (2) (2022) 872–883.
[14]
Zheng J., Zong X., Ge H., et al., Virtual leader-follower synchronization controller design for distributed parameter multi-agent systems with time-varying disturbances, Neurocomputing 450 (2021) 389–398.
[15]
Li Y., Tang C., Li K., et al., Consensus-based cooperative control for multi-platoon under the connected vehicles environment, IEEE Trans. Intell. Transp. Syst. 20 (6) (2019) 2220–2229.
[16]
Ryosuke K., Takahiro E., Fumitoshi M., Output-based dynamic event-triggered consensus control for linear multiagent systems, Automatica 133 (2021).
[17]
Liu P., Huang Z., Guo X., Event-triggered secure group consensus of second-order multi-agent systems under periodic DoS attacks, in: 2021 IEEE 10th Data Driven Control and Learning Systems Conference, (DDCLS), 2021,.
[18]
Ma T.D., Yu T.T., Huang J.S., et al., Adaptive odd impulsive consensus of multi-agent systems via comparison system method, Nonlinear Anal. Hybrid Syst. 35 (2020).
[19]
Chen T., Peng S.G., Zhang Z.H., Finite-time consensus of leader-following non-linear multi-agent systems via event-triggered impulsive control, IET Control Theory Appl. 15 (7) (2021) 926–936.
[20]
Hu X., Zhang Z.F., Li C.D., Consensus of multi-agent systems with dynamic join characteristics under impulsive control, Front. Inf. Technol. Electron. Eng. 022 (001) (2021) 120–133.
[21]
You L., Jiang X.W., Zhang X.H., et al., Distributed edge event-triggered control of nonlinear fuzzy multi-agent systems with saturation constraint hybrid impulsive protocols, IEEE Trans. Fuzzy Syst. 30 (10) (2022) 4142–4151.
[22]
Li C., Wu S., Feng G., et al., Stabilizing effects of impulses in discrete-time delayed neural networks, IEEE Trans. Neural Netw. 22 (2) (2011) 323–329.
[23]
He X., Yu J., Huang T., et al., Average quasi-consensus algorithm for distributed constrained optimization: Impulsive communication framework, IEEE Trans. Cybern. 50 (1) (2020) 351–360.
[24]
Wang Z., Wang D., Wang W., Distributed dynamic average consensus for nonlinear multi-agent systems in the presence of external disturbances over a directed graph, Inform. Sci. 479 (2019) 40–54.
[25]
Zheng M., Liu C., Liu F., Average-consensus tracking of sensor network via distributed coordination control of heterogeneous multi-agent systems, IEEE Control Syst. Lett. 3 (1) (2019) 132–137.
[26]
Zhao H.B., Meng X.Y., Wu S.T., Distributed edge-based event-triggered coordination control for multi-agent systems, Automatica (2021),.
[27]
Chen B., Yue J., Li W., et al., Average consensus control of multi-agent system under binary-valued observations with external disturbance and measurement noise, in: 2021 IEEE 4th International Conference on Electronics Technology, (ICET), 2021,.
[28]
Ruan X., Feng J., Xu C., et al., Observer-based dynamic event-triggered strategies for leader-following consensus of multi-agent systems with disturbances, IEEE Trans. Netw. Sci. Eng. 7 (4) (2020) 3148–3158.
[29]
Wang Y., Yuan Y., Liu J., Finite-time leader-following output consensus for multi-agent systems via extended state observer, Automatica 124 (2021).
[30]
Li G.L., Ren C.E., Chen C.L.P., Preview-based leader-following consensus control of distributed multi-agent systems, Inform. Sci. 559 (2021) 251–269.
[31]
Huang W.C., Tian B.B., Liu T.T., et al., Event-triggered leader-following consensus of multi-agent systems under semi-Markov switching topology with partially unknown rates, J. Franklin Inst. 359 (7) (2022) 3103–3125.
[32]
Desoer C., Vidyasagar M., Feedback Systems: Input–Output Properties, Academic Press, 1975,.
[33]
Wang C., Zsurzsan T.G., Zhang Z., Genetic algorithm assisted parametric design of splitting inductance in high frequency gan-based dual active bridge converter, IEEE Trans. Ind. Electron. 70 (1) (2021) 522–531.
[34]
Chui K.T., Driver stress recognition for smart transportation: Applying multiobjective genetic algorithm for improving fuzzy C-means clustering with reduced time and model complexity, Sustain. Comput.: Inform. Syst. 35 (2022).
[35]
Zhang Y.H., Wang Z.D., Zou L., et al., Neural-network-based secure state estimation under energy-constrained denial-of-service attacks: An encoding-decoding scheme, IEEE Trans. Netw. Sci. Eng. 10 (4) (2023) 2002–2015.
[36]
Guo J.Y., Wang Z.D., Zou L., et al., Finite-horizon H ∞ state estimation for discrete time-varying artificial neural networks: An accumulation-based event-triggered mechanism, IEEE Trans. Netw. Sci. Eng. 9 (6) (2022) 4184–4197.
[37]
Zhao Z.Y., Wang Z.D., Zou L., et al., Zonotopic distributed fusion for nonlinear networked systems with bit rate constraint, Inf. Fusion 90 (2023) 174–184.
[38]
Z.Y. Zhao, Z.D. Wang, L. Zou, et al., Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism, IEEE Trans. Neural Netw. Learn. Syst. https://doi.org/10.1109/TNNLS.2023.3325729.

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Published In

cover image Neurocomputing
Neurocomputing  Volume 584, Issue C
Jun 2024
240 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 17 July 2024

Author Tags

  1. Consensus
  2. Multi-agent system
  3. Multi-task
  4. Multi-control mechanism
  5. Multi-consensus strategy

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