Abstract:A data-driven control scheme based on dynamic event triggered strategy is proposed for bipartite consensus control of complex nonlinear MASs with unmeasurable external disturbance. The dynamic model of MASs with external disturbances is transformed into an equivalent data model using pseudo partial derivative, and radial basis function neural network is used to estimate the variation of unmeasured external disturbance. Based on the above data model, utilizing model free adaptive iterative learning algorithm combined with dynamic event triggered strategy, a dynamic event triggered based model-free iterative bipartite consensus control scheme is proposed. Then, the stability analysis of the proposed control scheme is given. The effectiveness of this control scheme is verified by simulation.
毛子祥, 侯忠生. 动态事件触发下带有扰动的MASs无模型迭代二分一致控制[J]. 复杂系统与复杂性科学, 2025, 22(1): 138-145.
MAO Zixiang, HOU Zhongsheng. Dynamic Event Triggered Based Model-free Iterative Bipartite Consensus Control for MASs with Disturbance[J]. Complex Systems and Complexity Science, 2025, 22(1): 138-145.
[1] AMIRKHANI A, BARSHOOI A H. Consensus in multi-agent systems: a review[J]. Artificial Intelligence Review, 2022, 55(5): 3897-3935. [2] OH K K, PARK M C, AHN H S. A survey of multi-agent formation control [J]. Automatica, 2015, 53: 424-440. [3] 杨盼盼, 刘明雍, 雷小康, 等. 群集系统分群行为建模与控制研究进展[J].控制与决策, 2016, 31(2): 193-206. YANG P P, LIU M Y, LEI X K, et al. Progress in modeling and control of fission behavior for flocking system[J]. Control and Decision, 2016, 31(2): 193-206. [4] ALTAFINI C. Consensus problems on networks with antagonistic interactions[J]. IEEE Transactions on Automatic Control, 2013, 58(4): 935-946. [5] CHIPADE V S, PANAGOU D. Multiagent planning and control for swarm herding in 2-D obstacle environments under bounded inputs[J]. IEEE Transactions on Robotics, 2021, 37(6):1956-1972. [6] FOURNARIS A P, LALOS A S, SERPANOS D. Generative adversarial networks in AI-enabled safety-critical systems: friend or foe? [J]. Computer, 2019, 52(9): 78-81. [7] XU D W, PENG P, WEI C C, et al. Road traffic network state prediction based on a generative adversarial network[J]. IET Intelligent Transport Systems, 2020, 14(10): 1286-1294. [8] 王潇, 纪志坚. 基于MAS的合作-竞争编队研究[J]. 复杂系统与复杂性科学, 2021, 18(1): 8-14. WANG X, JI Z J.Cooperative competitive formation based on MAS[J]. Complex Systems and Complexity Science, 2021, 18(1): 8-14. [9] QIN J H, FU W M, ZHENG W X, et al. On the bipartite consensus for generic linear multiagent systemswith input saturation[J]. IEEE Transactions on Cybernetics, 2017, 47(8): 1948-1958. [10] MENG Z Y, SHI G D, JOHANSSON K H, et al. Behaviors of networks with antagonistic interactions and switching topologies[J]. Automatica, 2016, 73: 110-116. [11] AHN H S, MOORE K L, CHEN Y Q. Trajectory keeping in satellite formation flying via robust periodic learning control [J]. International Journal of Robust and Nonlinear Control, 2010, 20(14): 1655-1666. [12] LÜ Y K, ZHANG H, HUANG C, et al. Distributed localization of multiagent systems with imperfect channels based on iterative learning [J]. IEEE Transactions on Industrial Electronics, 2022, 69(10): 10521-10529. [13] LI J S, LIU S Y, LI J M. Observer-based distributed adaptive iterative learning control for linear multi-agent systems[J]. International Journal of Systems Science, 2017, 48(14): 2948-2955. [14] JIN X. Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking[J]. Systems and Control Letters, 2016, 89: 16-23. [15] LIU K, JI Z. Dynamic event-triggered consensus of general linear multi-agent systems with adaptive strategy[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(8): 3440-3444. [16] YI X, LIU K, DIMAROGONAS D V, et al. Dynamic event-triggered and self-triggered control for multi-agent systems[J]. IEEE Transactions on Automatic Control, 2019, 64(8): 3300-3307. [17] LIN N, LING Q. Dynamic Periodic Event-triggered consensus protocols for Linear multiagent systems with network delay[J]. IEEE Systems Journal, 2023, 17(1): 1204-1215. [18] 王海, 刘根锋, 侯忠生. 高速列车数据驱动无模型自适应容错控制[J]. 控制与决策, 2022, 37(5): 1127-1136. WANG H, LIU G F, HOU Z S. Data-drivenmodel-free adaptive fault tolerant control for high-speed trains[J]. Control and Decision, 2022, 37(5): 1127-1136. [19] LIU S D, HOU Z S, ZHANG X, et al.Model-free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft[J]. IET Control Theory &Applications, 2020, 14(15): 2084-2096. [20] 王文佳, 侯忠生. 基于无模型自适应控制的自动泊车方案[J]. 控制与决策, 2022, 37(8): 2056-2066. WANG W J, HOU Z S.Model-free adaptive control based automatic parking scheme[J]. Control and Decision, 2022, 37(8): 2056-2066. [21] LIANG J Q, BU X H, CUI L Z, et al. Data-driven bipartite formation for a class of nonlinear MIMO multiagent systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(6): 3161-3173. [22] ZHAO H R, PENG L, YU H N, et al. Data driven distributed bipartite consensus tracking for nonlinear multiagent systems via iterative learning control [J]. IEEE Access, 2020, 8: 144718-144729. [23] REN Y, HOU Z S. Robustmodel-free adaptive iterative learning formation for unknown heterogeneous non-linear multi-agent systems[J]. IET Control Theory &Applications, 2020, 14(4): 654-663. [24] LIU T, HOU Z S.Model-free adaptive iterative learning containment control for unknown heterogeneous nonlinear MASs with disturbances[J]. Neurocomputing, 2023, 515: 121-132. [25] HUA C C, QIU Y F, GUAN X P. Event-triggered iterative learning containment control ofmodel-free multiagent Systems[J]. IEEE Transactions on Systems, Man, and Cybernetics-Systems, 2021, 51(12): 7719-7726.