Abstract
This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks. Meanwhile, the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error. Second, a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies. The scaled formation error system is modeled as a time-varying delay system. Then, several sufficient stability criteria are derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). A co-design algorithm based on the sparrow search algorithm (SSA) is presented to design the control gains and triggering parameters jointly. Finally, numerical simulations of multiple unmanned aerial vehicles (UAVs) are presented to validate the designed control method.
摘要
本文考虑固定和切换拓扑下的多智能体系统缩放编队控制问题. 首先, 提出一种改进的基于采样的包含动态辅助变量的弹性动态事件触发机制. 在该机制中, 引入一个服从伯努利分布的随机变量来表达通信网络的空闲和繁忙情况. 同时, 将绝对值运算引入触发条件, 以有效减小编队误差. 然后, 基于所提机制, 在固定和切换拓扑下设计一个缩放编队控制协议. 缩放编队误差系统被建模为一个时变时滞系统. 通过构建适当的Lyapunov–Krasovskii泛函, 导出编队误差系统稳定的充分条件. 提出一种基于麻雀搜索算法的联合设计算法, 用于联合设计控制增益和触发参数. 最后, 通过多无人机仿真实验平台, 对所设计控制方法的有效性进行了数值验证.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Cao YC, Yu WW, Ren W, et al., 2013. An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans Ind Inform, 9(1):427–438. https://doi.org/10.1109/TII.2012.2219061
Chen WB, Chen YY, Zhang Y, 2022. Finite-time coordinated path-following control of leader-following multiagent systems. Front Inform Technol Electron Eng, 23(10):1511–1521. https://doi.org/10.1631/FITEE.2100476
Dong XW, Zhou Y, Ren Z, et al., 2017. Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Trans Ind Electron, 64(6):5014–5024. https://doi.org/10.1109/TIE.2016.2593656
Du SL, Liu T, Ho DWC, 2020. Dynamic event-triggered control for leader-following consensus of multiagent systems. IEEE Trans Syst Man Cybern Syst, 50(9):3243–3251. https://doi.org/10.1109/TSMC.2018.2866853
Ge XH, Han QL, Zhang XM, 2018. Achieving cluster formation of multi-agent systems under aperiodic sampling and communication delays. IEEE Trans Ind Electron, 65(4):3417–3426. https://doi.org/10.1109/TIE.2017.2752148
Ge XH, Han QL, Zhong MY, et al., 2019. Distributed Krein space-based attack detection over sensor networks under deception attacks. Automatica, 109:108557. https://doi.org/10.1016/j.automatica.2019.108557
Ge XH, Xiao SY, Han QL, et al., 2022. Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks. IEEE/CAA J Autom Sin, 9(1):31–46. https://doi.org/10.1109/JAS.2021.1004060
Ge XH, Han QL, Wu Q, et al., 2023. Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks. IEEE/CAA J Autom Sin, 10(5):1234–1251. https://doi.org/10.1109/JAS.2022.105845
Guan YP, Ge XH, 2018. Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks. IEEE Trans Signal Inform Process Netw, 4(1):48–59. https://doi.org/10.1109/TSIPN.2017.2749959
Guo XG, Liu PM, Wang JL, et al., 2021. Event-triggered adaptive fault-tolerant pinning control for cluster consensus of heterogeneous nonlinear multi-agent systems under aperiodic DoS attacks. IEEE Trans Netw Sci Eng, 8(2):1941–1956. https://doi.org/10.1109/TNSE.2021.3077766
He WL, Mo ZK, 2022. Secure event-triggered consensus control of linear multiagent systems subject to sequential scaling attacks. IEEE Trans Cybern, 52(10):10314–10327. https://doi.org/10.1109/TCYB.2021.3070356
He WL, Xu B, Han QL, et al., 2020. Adaptive consensus control of linear multiagent systems with dynamic event-triggered strategies. IEEE Trans Cybern, 50(7):2996–3008. https://doi.org/10.1109/TCYB.2019.2920093
He WL, Xu WY, Ge XH, et al., 2022. Secure control of multiagent systems against malicious attacks: a brief survey. IEEE Trans Ind Inform, 18(6):3595–3608. https://doi.org/10.1109/TII.2021.3126644
Hu JP, Wu YZ, Li T, et al., 2019. Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Trans Autom Contr, 64(5):2122–2127. https://doi.org/10.1109/TAC.2018.2872197
Huang J, Wen GG, Peng ZX, et al., 2020. Cluster-delay consensus for second-order nonlinear multi-agent systems. J Syst Sci Compl, 33(2):333–344. https://doi.org/10.1007/s11424-020-8174-4
Ju YM, Ding DR, He X, et al., 2022. Consensus control of multi-agent systems using fault-estimation-in-the-loop: dynamic event-triggered case. IEEE/CAA J Autom Sin, 9(8):1440–1451. https://doi.org/10.1109/JAS.2021.1004386
Lin ZY, Wang LL, Han ZM, et al., 2014. Distributed formation control of multi-agent systems using complex Laplacian. IEEE Trans Autom Contr, 59(7):1765–1777. https://doi.org/10.1109/TAC.2014.2309031
Luo SP, Ye D, 2022. Cluster consensus control of linear multiagent systems under directed topology with general partition. IEEE Trans Autom Contr, 67(4):1929–1936. https://doi.org/10.1109/TAC.2021.3069398
Ma L, Wang YL, Han QL, 2021. H∞ cluster formation control of networked multiagent systems with stochastic sampling. IEEE Trans Cybern, 51(12):5761–5772. https://doi.org/10.1109/TCYB.2019.2959201
Mahony R, Kumar V, Corke P, 2012. Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot Autom Mag, 19(3):20–32. https://doi.org/10.1109/MRA.2012.2206474
Meng DY, Jia YM, 2016. Scaled consensus problems on switching networks. IEEE Trans Autom Contr, 61(6):1664–1669. https://doi.org/10.1109/TAC.2015.2479119
Ning BD, Han QL, Zuo ZY, et al., 2023. Fixed-time and prescribed-time consensus control of multiagent systems and its applications: a survey of recent trends and methodologies. IEEE Trans Ind Inform, 19(2):1121–1135. https://doi.org/10.1109/TII.2022.3201589
Park P, Ko JW, Jeong C, 2011. Reciprocally convex approach to stability of systems with time-varying delays. Automatica, 47(1):235–238. https://doi.org/10.1016/j.automatica.2010.10.014
Ren W, Atkins E, 2005. Second-order consensus protocols in multiple vehicle systems with local interactions. Proc AIAA Guidance, Navigation, and Control Conf and Exhibit. https://doi.org/10.2514/6.2005-6238
Roy S, 2015. Scaled consensus. Automatica, 51:259–262. https://doi.org/10.1016/j.automatica.2014.10.073
Shan YH, Hu JF, Chan KW, et al., 2021. A unified model predictive voltage and current control for microgrids with distributed fuzzy cooperative secondary control. IEEE Trans Ind Inform, 17(12):8024–8034. https://doi.org/10.1109/TII.2021.3063282
Shen B, Wang ZD, Wang D, et al., 2020. Distributed state-saturated recursive filtering over sensor networks under round-robin protocol. IEEE Trans Cybern, 50(8):3605–3615. https://doi.org/10.1109/TCYB.2019.2932460
Su YF, Cai H, Huang J, 2022. The cooperative output regulation by the distributed observer approach. Int J Netw Dynam Intell, 1(1):20–35. https://doi.org/10.53941/ijndi0101003
Wang H, Yu WW, Wen GH, et al., 2019. Fixed-time consensus of nonlinear multi-agent systems with general directed topologies. IEEE Trans Circ Syst II Expr Briefs, 66(9):1587–1591. https://doi.org/10.1109/TCSII.2018.2886298
Wang JH, Bao F, Han L, et al., 2020. Discrete sliding mode control for time-varying formation tracking of multi-UAV system with a dynamic leader. Proc Chinese Automation Congress, p.4950–4955. https://doi.org/10.1109/CAC51589.2020.9327623
Wang XL, Sun Y, Ding DR, 2022. Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. Int J Netw Dynam Intell, 1(1):85–98. https://doi.org/10.53941/ijndi0101008
Xie ML, Ding DR, Ge XH, et al., 2023. Distributed platooning control of automated vehicles subject to replay attacks based on proportional integral observers. IEEE/CAA J Autom Sin, early access. https://doi.org/10.1109/JAS.2022.105941
Xu B, He WL, 2018. Event-triggered cluster consensus of leader-following linear multi-agent systems. J Artif Intell Soft Comput Res, 8(4):293–302. https://doi.org/10.1515/jaiscr-2018-0019
Xue JK, Shen B, 2020. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Contr Eng, 8(1):22–34. https://doi.org/10.1080/21642583.2019.1708830
Ye MJ, Han QL, Ding L, et al., 2023. Distributed Nash equilibrium seeking in games with partial decision information: a survey. Proc IEEE, 111(2):140–157. https://doi.org/10.1109/JPROC.2023.3234687
Ye N, Zhang YB, Borror CM, 2004. Robustness of the Markov-chain model for cyber-attack detection. IEEE Trans Rel, 53(1):116–123. https://doi.org/10.1109/TR.2004.823851
Yu JJ, Zhang KJ, Fei SM, 2009. Further results on mean square exponential stability of uncertain stochastic delayed neural networks. Commun Nonl Sci Numer Simul, 14(4):1582–1589. https://doi.org/10.1016/j.cnsns.2008.04.009
Yu JL, Dong XW, Li QD, et al., 2022. Adaptive practical optimal time-varying formation tracking control for disturbed high-order multi-agent systems. IEEE Trans Circ Syst I Reg Papers, 69(6):2567–2578. https://doi.org/10.1109/TCSI.2022.3151464
Zhang D, Xu ZH, Karimi HR, et al., 2018. Distributed H∞ output-feedback control for consensus of heterogeneous linear multiagent systems with aperiodic sampled-data communications. IEEE Trans Ind Electron, 65(5):4145–4155. https://doi.org/10.1109/TIE.2017.2772196
Zhang LZ, Li YY, Lou JG, et al., 2022. Bipartite asynchronous impulsive tracking consensus for multi-agent systems. Front Inform Technol Electron Eng, 23(10):1522–1532. https://doi.org/10.1631/FITEE.2100122
Zhang XM, Han QL, Ge XH, et al., 2023. Sampled-data control systems with non-uniform sampling: a survey of methods and trends. Ann Rev Contr, 55:70–91. https://doi.org/10.1016/j.arcontrol.2023.03.004
Zhu GL, Liu KX, Gu HB, et al., 2022. Observer-based event-triggered formation control of multi-agent systems with switching directed topologies. IEEE Trans Circ Syst I Reg Papers, 69(3):1323–1332. https://doi.org/10.1109/TCSI.2021.3134816
Author information
Authors and Affiliations
Contributions
Yanping YANG designed the research. Siyu MA drafted the paper. Yanping YANG and Dawei LI helped organize the paper. Dawei LI and Jinghui SUO revised and finalized the paper.
Corresponding author
Ethics declarations
All the authors declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (Nos. 62103097 and 61803081), the Shanghai Rising-Star Program (No. 21QA1400100), and the Natural Science Foundation of Shanghai, China (No. 20ZR1400800)
Rights and permissions
About this article
Cite this article
Yang, Y., Ma, S., Li, D. et al. Modified dynamic event-triggered scaled formation control for multi-agent systems via a sparrow search algorithm based co-design algorithm. Front Inform Technol Electron Eng 25, 197–213 (2024). https://doi.org/10.1631/FITEE.2300615
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2300615