Data-driven cooperative output regulation of multi-agent systems under distributed denial of service attacks
Tóm tắt
This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.
Tài liệu tham khảo
Su Y F, Huang J. Cooperative output regulation of linear multi-agent systems. IEEE Trans Automat Contr, 2012, 57: 1062–1066
Liu W, Huang J. Cooperative global robust output regulation for a class of nonlinear multi-agent systems with switching network. IEEE Trans Automat Contr, 2015, 60: 1963–1968
Li X, Soh Y C, Xie L, et al. Cooperative output regulation of heterogeneous linear multi-agent networks via performance allocation. IEEE Trans Automat Contr, 2019, 64: 683–696
Zhang D, Deng C, Feng G. Resilient cooperative output regulation for nonlinear multi-agent systems under DoS attacks. IEEE Trans Automat Contr, 2022. doi: https://doi.org/10.1109/TAC.2022.3184388
Yan Y, Chen Z. Cooperative output regulation of linear discrete-time time-delay multi-agent systems by adaptive distributed observers. Neurocomputing, 2019, 331: 33–39
Ren W, Beard R. Distributed Consensus in Multi-vehicle Cooperative Control. London: Springer-Verlag 2008
Yu Z, Huang D, Jiang H, et al. Distributed consensus for multiagent systems via directed spanning tree based adaptive control. SIAM J Control Optim, 2018, 56: 2189–2217
Gao W, Mynuddin M, Wunsch D C, et al. Reinforcement learning-based cooperative optimal output regulation via distributed adaptive internal model. IEEE Trans Neural Netw Learn Syst, 2022, 33: 5229–5240
Cai H, Lewis F L, Hu G, et al. The adaptive distributed observer approach to the cooperative output regulation of linear multi-agent systems. Automatica, 2017, 75: 299–305
Jiang Z P, Bian T, Gao W. Learning-based control: a tutorial and some recent results. FNT Syst Control, 2020, 8: 176–284
Murray J J, Cox C J, Lendaris G G, et al. Adaptive dynamic programming. IEEE Trans Syst Man Cybern C, 2002, 32: 140–153
Kiumarsi B, Vamvoudakis K G, Modares H, et al. Optimal and autonomous control using reinforcement learning: a survey. IEEE Trans Neural Netw Learn Syst, 2018, 29: 2042–2062
Liu D, Xue S, Zhao B, et al. Adaptive dynamic programming for control: a survey and recent advances. IEEE Trans Syst Man Cybern Syst, 2021, 51: 142–160
Gao W, Jiang Z P. Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview. Control Theor Technol, 2022, 20: 1–19
Li J, Modares H, Chai T, et al. Off-policy reinforcement learning for synchronization in multiagent graphical games. IEEE Trans Neural Netw Learn Syst, 2017, 28: 2434–2445
Gao W, Jiang Z P, Lewis F L, et al. Leader-to-formation stability of multiagent systems: an adaptive optimal control approach. IEEE Trans Automat Contr, 2018, 63: 3581–3587
Ye Z, Zhang D, Wu Z G, et al. A3C-based intelligent event-triggering control of networked nonlinear unmanned marine vehicles subject to hybrid attacks. IEEE Trans Intell Transp Syst, 2022, 23: 12921–12934
Deng C, Zhang D, Feng G. Resilient practical cooperative output regulation for MASs with unknown switching exosystem dynamics under DoS attacks. Automatica, 2022, 139: 110172
Specht S M, Lee R B. Distributed denial of service: taxonomies of attacks, tools and countermeasure. In: Proceedings of the International Conference on Parallel and Distributed Computing Systems, Tokyo, 2004. 543–550
Zargar S T, Joshi J, Tipper D. A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun Surv Tutorials, 2013, 15: 2046–2069
Xu W, Hu G, Ho D W C, et al. Distributed secure cooperative control under denial-of-service attacks from multiple adversaries. IEEE Trans Cybern, 2020, 50: 3458–3467
Deng C, Wen C. MAS-based distributed resilient control for a class of cyber-physical systems with communication delays under DoS attacks. IEEE Trans Cybern, 2021, 51: 2347–2358
Chen J, Zhang H, Yin G. Distributed dynamic event-triggered secure model predictive control of vehicle platoon against DoS attacks. IEEE Trans Veh Technol, 2023, 72: 2863–2877
Wang X, Hong Y, Huang J, et al. A distributed control approach to a robust output regulation problem for multi-agent linear systems. IEEE Trans Automat Contr, 2010, 55: 2891–2895
Hu W, Liu L. Cooperative output regulation of heterogeneous linear multi-agent systems by event-triggered control. IEEE Trans Cybern, 2017, 47: 105–116
Huang J. Nonlinear Output Regulation: Theory and Applications. Philadelphia: SIAM, 2004
de Persis C, Tesi P. Input-to-state stabilizing control under denial-of-service. IEEE Trans Automat Contr, 2015, 60: 2930–2944
Bian T, Jiang Z P. Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design. Automatica, 2016, 71: 348–360
Al-Tamimi A, Lewis F L, Abu-Khalaf M. Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof. IEEE Trans Syst Man Cybern B, 2008, 38: 943–949
Bian T, Jiang Z P. Reinforcement learning and adaptive optimal control for continuous-time nonlinear systems: a value iteration approach. IEEE Trans Neural Netw Learn Syst, 2022, 33: 2781–2790
Jiang Y, Jiang Z P. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. Automatica, 2012, 48: 2699–2704
Sontag E D. Input to state stability: basic concepts and results. In: Nonlinear and Optimal Control Theory. Berlin: Springer-Verlag, 2007. 163–220
Khalil H K. Nonlinear Systems. 3rd ed. Upper Saddle River: Prentice Hall PTR, 2002