Communication Structure Adaptive Control and Collaborative Optimization for Multi-Agent Systems
DOI:
https://doi.org/10.22399/ijcesen.4021Keywords:
Multi-agent systems, adaptive communication, collaborative optimization, fault-tolerant controlAbstract
Multi-agent systems(MAS) rely on adaptive communication architectures to coordinate, scale, and maintain robustness in dynamic settings where fixed topologies fail. By adjusting connections under uncertainty, agents preserve connectivity while reducing resource use, yet combining adaptation with collaborative optimization remains fragmented. Reviewing over fifty key works from 2003 to 2025, we present a unified taxonomy of communication paradigms (static vs. dynamic; directed vs. undirected; hierarchical vs. peer‑to‑peer; sparse vs. dense), adaptive‑control strategies (model‑free protocols, learning‑driven topology updates, fault‑resilient controls), and cooperative optimization methods. Advances in distributed consensus, event-triggered messaging, and quantized communication have dramatically lowered bandwidth requirements without sacrificing performance, and algebraic connectivity is shown critical for convergence rates. Despite progress, challenges persist in scalability, privacy/security in distributed coordination, and trust‑aware human–robot interaction. We propose future directions toward privacy-preserving protocols, enhanced communication security, and interpretable coordination frameworks.
References
[1] Jadbabaie, A., Lin, J., & Morse, A. S. (2003). Coordination of groups of mobile autonomous agents using nearest-neighbor rules. IEEE Transactions on Automatic Control, 48(6), 988-1001. https://doi.org/10.1109/TAC.2003.812781
[2] Chung, F. R. K. (1997). Spectral Graph Theory. American Mathematical Society. https://doi.org/10.1090/cbms/092
[3] Olfati-Saber, R., & Murray, R. M. (2004). Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 49(9), 1520-1533. https://doi.org/10.1109/TAC.2004.834113
[4] Ren, W., & Beard, R. W. (2005). Consensus seeking in multi-agent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5), 655-661. https://doi.org/10.1109/TAC.2005.846556
[5] Franceschetti, M., Khojasteh, M. J., & Win, M. Z. (2023). The many facets of information in networked estimation and control. Annual Review of Control, Robotics & Autonomous Systems, 6, 233-259. https://doi.org/10.1146/annurev-control-042820-010811
[6] Carli, R., Fagnani, F., Frasca, P., & Zampieri, S. (2014). Network clock synchronization based on the second-order linear consensus algorithm. IEEE Transactions on Automatic Control, 59(2), 409-422. https://doi.org/10.1109/TAC.2013.2783742
[7] Qin, J., Ma, Q., Shi, Y., & Wang, L. (2022). Micro-foundation of opinion dynamics via weighted-median mechanism. Physical Review Research, 4(2), 023213. https://doi.org/10.1103/PhysRevResearch.4.023213
[8] Zhang, X., et al. (2023). A survey of multi-agent deep reinforcement learning with communication. Autonomous Agents and Multi-Agent Systems, 37(4), 1-42. https://doi.org/10.1007/s10458-023-09633-6
[9] Maldonado-Andrade, D. J., Cruz, E., & Abad-Torres, J. (2024). Multi-agent systems: Components, framework and workflow. IEEE Access, 12, 1-28. https://doi.org/10.1109/ACCESS.2024.3409051
[10] Yu, B., Bullo, F., & Hendrickx, J. M. (2024). Fast and flexible multi-agent decision-making: A survey. Annual Review of Control, Robotics & Autonomous Systems, 7, 1-29. https://doi.org/10.1146/annurev-control-090523-100059
[11] Zhang, H., et al. (2024). Heterogeneous multi-robot cooperation with asynchronous MARL. IEEE Robotics and Automation Letters, 9(1), 159-166. https://doi.org/10.1109/LRA.2023.3328448
[12] Chen, Z., et al. (2022). Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs. Entropy, 26(2), 113. https://doi.org/10.3390/e26020113
[13] Saravanos, A. D., Li, Y., & Theodorou, E. (2023). Distributed Hierarchical Distribution Control for Very-Large-Scale Clustered Multi-Agent Systems. Proceedings of Robotics: Science and Systems, XIX, 110. https://doi.org/10.15607/RSS.2023.XIX.110
[14] Zhang, Y., et al. (2022). Distributed estimation of algebraic connectivity. IEEE Transactions on Cybernetics, 52(5), 3047-3056. https://doi.org/10.1109/TCYB.2020.3022653
[15] Fang, J., & Li, H. B. (2021). Distributed consensus with quantized data via sequence averaging. IEEE Transactions on Signal Processing, 69(2), 944-958. https://doi.org/10.1109/TSP.2020.3048270
[16] Ding, T., Zhu, S., Chen, C., Xu, J., & Guan, X. (2021). Differentially Private Distributed Optimization via State and Direction Perturbation in Multiagent Systems. IEEE Transactions on Automatic Control, 66(9), 4242-4249. https://doi.org/10.1109/TAC.2020.3048958
[17] Dinic, A., et al. (2021). Algebraic Connectivity Control in Distributed Networks by Using Multiple Communication Channels. Sensors, 21(15), 5014. https://doi.org/10.3390/s21155014
[18] Khalil, H. K. (2005). Non-linear Systems (3rd ed.), Chap. 14. Prentice-Hall.
[19] Wen, G., & Chen, C. L. P. (2023). Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems. IEEE Transactions on Neural Networks and Learning Systems, 34(3), 1524-1536. https://doi.org/10.1109/TNNLS.2021.3105548
[20] Peng, Y., Yu, M., Wang, Z., & Sun, Y. (2024). Distributed secure observer-based adaptive consensus tracking control for uncertain non-linear MAS under DoS attacks. International Journal of Robust & Nonlinear Control, early view. https://doi.org/10.1002/rnc.7928
[21] Li, Y., Lu, G., & Li, K. (2024). Adaptive Fuzzy Secured Control for Multiagent Systems Under DoS Attacks and Intermittent Actuator Failures. IEEE Transactions on Fuzzy Systems, 32(5), 2567-2576. https://doi.org/10.1109/TFUZZ.2024.3354082
[22] Sedghi, L., Ijaz, Z., Noor-A-Rahim, M., Witheephanich, K., & Pesch, D. (2022). Machine Learning in Event-Triggered Control: Recent Advances and Open Issues. IEEE Access, 10, 74671-74690. https://doi.org/10.1109/ACCESS.2022.3191343
[23] Kipf, T. N., & Welling, M. (2018). Semi-supervised classification with graph convolutional networks. ICLR 2018 Proceedings. https://openreview.net/forum?id=SJU4ayYgl
[24] Mo, X., Huang, Z., Xing, Y., & Lv, C. (2022). Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network. IEEE Transactions on Intelligent Transportation Systems, 23(7), 9554-9567. https://doi.org/10.1109/TITS.2022.3146300
[25] Farhan, M., Shah, N., Wang, L., Muntean, G.-M., & Song, H. H. (2025). RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN. Expert Systems with Applications, 265, 125963. https://doi.org/10.1016/j.eswa.2024.125963
[26] Yadegar, M., & Meskin, N. (2021). Fault-tolerant control of nonlinear heterogeneous multi-agent systems. Automatica, 127, 109514. https://doi.org/10.1016/j.automatica.2021.109514
[27] Zhang, Z., & Dong, J. (2022). Fault-Tolerant Containment Control for IT2 Fuzzy Networked Multiagent Systems Against Denial-of-Service Attacks and Actuator Faults. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(4), 2213-2224. https://doi.org/10.1109/TSMC.2020.3048999
[28] Tang, R., Zhu, W., & Pu, H. (2023). Event-triggered distributed optimization of multi-agent systems with time delay. Mathematical Biosciences and Engineering, 20(12), 20712-20726. https://doi.org/10.3934/mbe.2023916
[29] Khatana, V., et al. (2020). Gradient-consensus optimisation in directed networks. American Control Conference 2020 Proceedings, 2754-2761. https://doi.org/10.23919/ACC45564.2020.9147544
[30] Meng, X., & Liu, Q. (2023). A consensus algorithm based on MAS with state noise and gradient disturbance for distributed convex optimization. Neurocomputing, 519, 148-157. https://doi.org/10.1016/j.neucom.2022.11.051
[31] Li, X., Kong, J., & Li, T. (2022). An improved consensus algorithm for MAS with directed topology and binary-valued communication. Neurocomputing, 468, 245-256. https://doi.org/10.1016/j.neucom.2021.11.043
[32] Survey on distributed Nash equilibrium seeking. (2022). Artificial Intelligence Review, 57(2), 1265-1309. https://doi.org/10.26599/AIR.2022.9150002
[33] Tan, S., Fang, Z., Wang, Y., & Lü, J. (2024). A Timestamp-Based Inertial Best-Response Dynamics for Distributed Nash Equilibrium Seeking in Weakly Acyclic Games. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 1330-1340. https://doi.org/10.1109/TNNLS.2022.3183250
[34] Chen, X., Hu, S., Yang, T., Xie, X., & Qiu, J. (2024). Event-Triggered Bipartite Consensus of Multiagent Systems With Input Saturation and DoS Attacks Over Weighted Directed Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(7), 4054-4065. https://doi.org/10.1109/TSMC.2022.3226148
[35] Yao, X., Sun, H., Zhao, Z., & Liu, Y. (2024). Event-Triggered Bipartite Consensus Tracking and Vibration Control of Flexible Timoshenko Manipulators Under Time-Varying Actuator Faults. IEEE/CAA Journal of Automatica Sinica, 11(5), 1190-1201. https://doi.org/10.1109/JAS.2024.124266
[36] Pei, X., Li, K., & Li, Y. (2024). Neuro-adaptive Event-triggered Optimal Control for Power Battery Systems With State Constraints. International Journal of Control, Automation and Systems, 22, 581-592. https://doi.org/10.1007/s12555-022-1127-z
[37] Antsaklis, P., et al. (2008). Event-triggered control for networked systems. Hybrid Systems: Computation and Control 2008 Proceedings, 1-14. https://doi.org/10.1145/1349206.1349232
[38] Yang, F., & Matni, N. Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control. IEEE Conference on Decision and Control (CDC), 2396-2401 (2021). https://doi.org/10.1109/CDC45484.2021.9683779
[39] Liu, Y., & Li, L. Adaptive Leader-Follower Consensus Control of Multiple Flexible Manipulators With Actuator Failures and Parameter Uncertainties. IEEE/CAA Journal of Automatica Sinica, 10(4), 1020-1031 (2023). https://doi.org/10.1109/JAS.2023.123093
[40] Malli, I., Bechlioulis, C. P., & Kyriakopoulos, K. J. Robust Distributed Estimation of the Algebraic Connectivity for Networked Multi-robot Systems. IEEE International Conference on Robotics and Automation (ICRA), 9155-9160 (2021). https://doi.org/10.1109/ICRA48506.2021.9561201
[41] Stojanovic, V. Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming. Mathematical Modelling and Control, 3(3), 181-191 (2023). https://doi.org/10.3934/mmc.2023016
[42] Poveda, J. I., et al. Fixed-Time Nash Equilibrium Seeking in Time-Varying Networks. IEEE Transactions on Automatic Control, 68(4), 1954-1969 (2023). https://doi.org/10.1109/TAC.2022.3176275
[43] Zhang, Z., Yan, W., & Li, H. Distributed Optimal Control for Linear Multiagent Systems on General Digraphs. IEEE Transactions on Automatic Control, 66(1), 322-328 (2021). https://doi.org/10.1109/TAC.2020.2974424
[44] Wang, J., Zhang, J., Tavoosi, J., Shirkhani, M. Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots. Complexity, 2024, Article ID 1330458 (2024). https://doi.org/10.1155/2024/1330458
[53] Ma, C., Li, A., Du, Y., et al. (2024). Efficient and scalable reinforcement learning for large-scale network control. Nature Machine Intelligence, 6, 1006-1020. https://doi.org/10.1038/s42256-024-00879-7
[54] Ye, F., Cao, X., Chow, M.-Y., & Cai, L. (2024). Privacy-Preserving Average Consensus: Fundamental Analysis and a Generic Framework Design. IEEE Transactions on Information Theory, 70(4), 2870-2885. https://doi.org/10.1109/TIT.2024.3370311
[55] Zahedi, Z., Verma, M., Sreedharan, S., & Kambhampati, S. (2023). Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction. Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 281-289. https://doi.org/10.1145/3568162.3578628
[56] Ning, B., Han, Q. L., & Zuo, Z. (2023). Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies. IEEE Transactions on Industrial Informatics, 19(2), 1121-1135. https://doi.org/10.1109/TII.2022.3201589
[57] Xie, G., Xu, H., Li, Y., Hu, X., & Wang, C. D. (2022). Fast distributed consensus seeking in large-scale and high-density multi-agent systems with connectivity maintenance. Information Sciences, 589, 497-515. https://doi.org/10.1016/j.ins.2022.06.079
[58] Loizaga, E., Bastida, L., Sillaurren, S., Moya, A., & Toledo, N. (2024). Modelling and measuring trust in human-robot collaboration. Applied Sciences, 14(5), 1919. https://doi.org/10.3390/app14051919
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