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Dynamic event-triggered adaptive finite-time consensus control for multi-agent systems with time-varying actuator faults


  • Received: 04 November 2022 Revised: 16 December 2022 Accepted: 10 January 2023 Published: 21 February 2023
  • In this study, the adaptive finite-time leader-following consensus control for multi-agent systems (MASs) subjected to unknown time-varying actuator faults is reported based on dynamic event-triggering mechanism (DETM). Neural networks (NNs) are used to approximate unknown nonlinear functions. Command filter and compensating signal mechanism are introduced to alleviate the computational burden. Unlike the existing methods, by combining adaptive backstepping method with DETM, a novel finite time control strategy is presented, which can compensate the actuator efficiency successfully, reduce the update frequency of the controller and save resources. At the same time, under the proposed strategy, it is guaranteed that all followers can track the trajectory of the leader in the sense that consensus errors converge to a neighborhood of the origin in finite time, and all signals in the closed-loop system are bounded. Finally, the availability of the designed strategy is validated by two simulation results.

    Citation: Na Zhang, Jianwei Xia, Tianjiao Liu, Chengyuan Yan, Xiao Wang. Dynamic event-triggered adaptive finite-time consensus control for multi-agent systems with time-varying actuator faults[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 7761-7783. doi: 10.3934/mbe.2023335

    Related Papers:

  • In this study, the adaptive finite-time leader-following consensus control for multi-agent systems (MASs) subjected to unknown time-varying actuator faults is reported based on dynamic event-triggering mechanism (DETM). Neural networks (NNs) are used to approximate unknown nonlinear functions. Command filter and compensating signal mechanism are introduced to alleviate the computational burden. Unlike the existing methods, by combining adaptive backstepping method with DETM, a novel finite time control strategy is presented, which can compensate the actuator efficiency successfully, reduce the update frequency of the controller and save resources. At the same time, under the proposed strategy, it is guaranteed that all followers can track the trajectory of the leader in the sense that consensus errors converge to a neighborhood of the origin in finite time, and all signals in the closed-loop system are bounded. Finally, the availability of the designed strategy is validated by two simulation results.



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    [1] H. W. Zhang, F. L. Lewis, Z. H. Qu, Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs, IEEE Trans. Ind. Electron., 59 (2012), 3026–3041. https://doi.org/10.1109/TIE.2011.2160140 doi: 10.1109/TIE.2011.2160140
    [2] H. Cai, G. Q. Qu, Distributed tracking control of an interconnected leader-follower multiagent system, IEEE Trans. Autom. Control, 62 (2017), 3494–3501. https://doi.org/10.1109/TAC.2017.2660298 doi: 10.1109/TAC.2017.2660298
    [3] G. Wang, C. L. Wang, L. Li, Q. H. Du, Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks, Neurocomputing, 214 (2016), 269–279. https://doi.org/10.1016/j.neucom.2016.06.013 doi: 10.1016/j.neucom.2016.06.013
    [4] D. Zhang, Q. L. Han, X. Zhang, Network-based modeling and proportional-integral control for direct-drive-wheel systems in wireless network environments, IEEE Trans. Cybern., 50 (2020), 2462–2474. https://doi.org/10.1109/TCYB.2019.2924450 doi: 10.1109/TCYB.2019.2924450
    [5] J. S. Huang, W. Wang, C. Y. Wen, J. Zhou, G. Q. Li, Distributed adaptive leader-follower and leaderless consensus control of a class of strict-feedback nonlinear systems: A unified approach, Automatica, 118 (2020). https://doi.org/10.1016/j.automatica.2020.109021 doi: 10.1016/j.automatica.2020.109021
    [6] N. Wang, G. H. Wen, Y. Wang, F. Zhang, A. Zemouche, Fuzzy adaptive cooperative consensus tracking of high-order nonlinear multiagent networks with guaranteed performances, IEEE Trans. Cybern., 52 (2022), 8838–8850. https://doi.org/10.1109/TCYB.2021.3051002 doi: 10.1109/TCYB.2021.3051002
    [7] J. A. Farrell, M. Polycarpou, M. Sharma, W. J. Dong, Command filtered backstepping, IEEE Trans. Autom. Control, 54 (2009), 1391–1395. https://doi.org/10.1109/TAC.2009.2015562 doi: 10.1109/TAC.2009.2015562
    [8] J. P. Yu, P. Shi, W. J. Dong, C. Lin, Adaptive fuzzy control of nonlinear systems with unknown dead zones based on command filtering, IEEE Trans. Fuzzy Syst., 26 (2018), 46–55. https://doi.org/10.1109/TFUZZ.2016.2634162 doi: 10.1109/TFUZZ.2016.2634162
    [9] Y. X. Lian, J. W. Xia, Ju H. Park, W. Sun, H. Shen, Disturbance observer-based adaptive neural network output feedback control for uncertain nonlinear systems, IEEE Trans. Neural Networks Learning Syst., 2022 (2022), forthcoming. https://doi.org/10.1109/TNNLS.2021.3140106 doi: 10.1109/TNNLS.2021.3140106
    [10] C. Xin, Y. X. Li, C. K. Ahn, Adaptive neural asymptotic tracking of uncertain non-strict feedback systems with full-state constraints via command filtered technique, IEEE Trans. Neural Networks Learning Syst., 2022 (2022), forthcoming. https://doi.org/10.1109/TNNLS.2022.3141091 doi: 10.1109/TNNLS.2022.3141091
    [11] R. H. Li, H. Q. Wu, J. D. Cao, Impulsive exponential synchronization of fractional-order complex dynamical networks with derivative couplings via feedback control based on discrete time state observations, Acta Math. Sci., 42 (2022), 46–55. https://doi.org/10.1007/s10473-022-0219-4 doi: 10.1007/s10473-022-0219-4
    [12] Z. Q. Zhang, H. Q. Wu, Cluster synchronization in finite/fixed time for semi-Markovian switching T-S fuzzy complex dynamical networks with discontinuous dynamic nodes, AIMS Math., 7 (2022), 11942–11971. https://doi.org/10.3934/math.2022666 doi: 10.3934/math.2022666
    [13] J. Bai, H. Q. W, J. D. Cao, Secure synchronization and identification for fractional complex networks with multiple weight couplings under DoS attacks, Comput. Appl. Math., 41 (2022). https://doi.org/10.1007/s40314-022-01895-2 doi: 10.1007/s40314-022-01895-2
    [14] X. N. Li, H. Q. Wu, J. D. Cao, Prescribed-time synchronization in networks of piecewise smooth systems via a nonlinear dynamic event-triggered control strategy, Math. Compu. Simul., 203 (2023), 647–668. https://doi.org/10.1016/j.matcom.2022.07.010 doi: 10.1016/j.matcom.2022.07.010
    [15] L. Zhao, J. P. Yu, C. Lin, Y. M. Ma, Adaptive neural consensus tracking for nonlinear multiagent systems using finite-time command filtered backstepping, IEEE Trans. Syst. Man Cybern. Syst., 48 (2018), 2003–2012. https://doi.org/10.1109/TSMC.2017.2743696 doi: 10.1109/TSMC.2017.2743696
    [16] J. W. Xia, J. Zhang, W. Sun, B. Y. Zhang, Z. Wang, Finite-time adaptive fuzzy control for nonlinear systems with full state constraints, IEEE Trans. Syst. Man Cybern. Syst., 49 (2019), 1541–1548. https://doi.org/10.1109/TSMC.2018.2854770 doi: 10.1109/TSMC.2018.2854770
    [17] J. Wu, S. Qiu, M. Liu, H. Y. Li, Y. Liu, Finite-time velocity-free relative position coordinated control of spacecraft formation with dynamic event triggered transmission, Math. Biosci. Eng., 19 (2022), 6883–6906. https://doi.org/10.3934/mbe.2022324 doi: 10.3934/mbe.2022324
    [18] C. Wang, C. Zhang, D. He, J. L. Xiao, L. Y. Liu, Observer-based finite-time adaptive fuzzy backstepping control for MIMO coupled nonlinear systems, Math. Biosci. Eng., 19 (2022), 10637–10655. https://doi.org/10.3934/mbe.2022497 doi: 10.3934/mbe.2022497
    [19] Y. Cui, X. P. Liu, X. Deng, G. X. Wen, Command-filter-based adaptive finite-time consensus control for nonlinear strict-feedback multi-agent systems with dynamic leader, Inf. Sci., 565 (2021), 17–31. https://doi.org/10.1016/j.ins.2021.02.078 doi: 10.1016/j.ins.2021.02.078
    [20] L. Kong, W. He, W. Yang, Q. Li, O. Kaynak, Fuzzy approximation-based finite-time control for a robot with actuator saturation under time-varying constraints of work space, IEEE Trans. Cybern., 51 (2021), 4873–4884. https://doi.org/10.1109/TCYB.2020.2998837 doi: 10.1109/TCYB.2020.2998837
    [21] L. L. Zhang, W. W. Che, B. Chen, C. Lin, Adaptive fuzzy output-feedback consensus tracking control of nonlinear multiagent systems in prescribed performance, IEEE Trans. Cybern., 2022 (2022), forthcoming. https://doi.org/10.1109/TCYB.2022.3171239 doi: 10.1109/TCYB.2022.3171239
    [22] X. D. Li, D. W. C. Ho, J. D. Cao, Finite-time stability and settling-time estimation of nonlinear impulsive systems, Automatica, 99 (2019), 361–368. https://doi.org/10.1016/j.automatica.2018.10.024 doi: 10.1016/j.automatica.2018.10.024
    [23] D. Zhai, L. W. An, J. H. Li, Q. L. Zhang, Adaptive fuzzy fault-tolerant control with guaranteed tracking performance for nonlinear strict-feedback systems, Fuzzy Sets Syst., 302 (2016), 82–100. https://doi.org/10.1016/j.fss.2015.10.006 doi: 10.1016/j.fss.2015.10.006
    [24] Y. M. Li, S. C. Tong, Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults, IEEE Trans. Neural Networks Learn. Syst., 28 (2017), 2541–2554. https://doi.org/10.1109/TNNLS.2016.2598580 doi: 10.1109/TNNLS.2016.2598580
    [25] G. Y. Lai, C. Y. Wen, Z. Liu, Y. Zhang, C. L. P. Chen, S. L. Xie, Adaptive compensation for infinite number of actuator failures based on tuning function approach, Automatica, 87 (2018), 365–374. https://doi.org/10.1016/j.automatica.2017.07.014 doi: 10.1016/j.automatica.2017.07.014
    [26] Y. X. Li, Finite time command filtered adaptive fault tolerant control for a class of uncertain nonlinear systems, Automatica, 106 (2019), 117–123. https://doi.org/10.1016/j.automatica.2019.04.022 doi: 10.1016/j.automatica.2019.04.022
    [27] Z. M. Wu, Y. F. Wu, Y. Dong, Distributed adaptive neural consensus tracking control of MIMO stochastic nonlinear multiagent systems with actuator failures and unknown dead zones, Int. J. Adapt. Control Signal Process., 32 (2018), 1694–1714. https://doi.org/10.1002/acs.2940 doi: 10.1002/acs.2940
    [28] W. B. Xiao, H. R. Ren, Q. Zhou, H. Y. Li, R. Q. Lu, Distributed finite-time containment control for nonlinear multiagent systems with mismatched disturbances, IEEE Trans. Cybern., 52 (2022), 6939–6948. https://doi.org/10.1109/TCYB.2020.3042168 doi: 10.1109/TCYB.2020.3042168
    [29] W. Bai, P. X. Liu, H. Q. Wang, M. Chen, Adaptive finite-time control for nonlinear multi-agent high-order systems with actuator faults, Int. J. Syst. Sci., 53 (2022), 2437–2460. https://doi.org/10.1080/00207721.2022.2053891 doi: 10.1080/00207721.2022.2053891
    [30] D. Ye, X. G. Zhao, B. Cao, Distributed adaptive fault-tolerant consensus tracking of multi-agent systems against time-varying actuator faults, IET Control Theory Appl., 10 (2016), 554–563. https://doi.org/10.1049/iet-cta.2015.0790 doi: 10.1049/iet-cta.2015.0790
    [31] F. Wang, X. Y. Zhang, Adaptive finite time control of nonlinear systems under time-varying actuator failures, IEEE Trans. Syst. Man Cybern. Syst., 49 (2019), 1845–1852. https://doi.org/10.1109/TSMC.2018.2868329 doi: 10.1109/TSMC.2018.2868329
    [32] Y. H. Jing, G. H. Yang, Adaptive fuzzy output feedback fault-tolerant compensation for uncertain nonlinear systems with infinite number of time-varying actuator failures and full-state constraints, IEEE Trans. Cybern., 51 (2021), 568–578. https://doi.org/10.1109/TCYB.2019.2904768 doi: 10.1109/TCYB.2019.2904768
    [33] Y. F. Li, S. X. Ding, C. C. Hua, G. P. Liu, Distributed adaptive leader-following consensus for nonlinear multiagent systems with actuator failures under directed switching graphs, IEEE Trans. Cybern., 53 (2023), 211–221. https://doi.org/10.1109/TCYB.2021.3091392 doi: 10.1109/TCYB.2021.3091392
    [34] C. L. Wang, C. Y. Wen, L. Guo, Adaptive consensus control for nonlinear multiagent systems with unknown control directions and time-varying actuator faults, IEEE Trans. Auto. Control, 66 (2021), 4222–4229. https://doi.org/10.1109/TAC.2020.3034209 doi: 10.1109/TAC.2020.3034209
    [35] W. Wu, Y. M. Li, S. C. Tong, Neural network output-feedback consensus fault-tolerant control for nonlinear multiagent systems with intermittent actuator faults, IEEE Trans. Neural Networks Learn. Syst., 2021 (2021), forthcoming. https://doi.org/10.1109/TNNLS.2021.3117364 doi: 10.1109/TNNLS.2021.3117364
    [36] Y. H. Yin, F. Y. Wang, Z. X. Liu, Z. Q. Chen, Finite-time leader-following consensus of multiagent systems with actuator faults and input saturation, IEEE Trans. Syst. Man Cybern. Syst., 52 (2022), 3314–3325. https://doi.org/10.1109/TSMC.2021.3064361 doi: 10.1109/TSMC.2021.3064361
    [37] K. X. Lu, Z. Liu, Y. N. Wang, C. L. P. Chen, Resilient adaptive neural control for uncertain nonlinear systems with infinite number of time-varying actuator failures, IEEE Trans. Cybern., 52 (2022), 4356–4369. https://doi.org/10.1109/TCYB.2020.3026321 doi: 10.1109/TCYB.2020.3026321
    [38] J. W. Xia, Y. X. Lian, S. F. Su, H. Shen, G. L. Chen, Observer-based event-triggered adaptive fuzzy control for unmeasured stochastic nonlinear systems with unknown control directions, IEEE Trans. Cybern., 52 (2022), 10655–10666. https://doi.org/10.1109/TCYB.2021.3069853 doi: 10.1109/TCYB.2021.3069853
    [39] L. Cao, H. Y. Li, Q. Zhou, Adaptive intelligent control for nonlinear strict-feedback systems with virtual control coefficients and uncertain disturbances based on event-triggered mechanism, IEEE Trans. Cybern., 48 (2018), 3390–3402. https://doi.org/10.1109/TCYB.2018.2865174 doi: 10.1109/TCYB.2018.2865174
    [40] H. J. Liang, G. L. Liu, H. G. Zhang, T. W. Huang, Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties, IEEE Trans. Neural Networks Learn. Syst., 32 (2021), 2239–2250. https://doi.org/10.1109/TNNLS.2020.3003950 doi: 10.1109/TNNLS.2020.3003950
    [41] J. W. Xia, B. M. Li, S. F. Su, W. Sun, H. Shen, Finite-time command filtered event-triggered adaptive fuzzy tracking control for stochastic nonlinear systems, IEEE Trans. Fuzzy Syst., 29 (2021), 1815–1825. https://doi.org/10.1109/TFUZZ.2020.2985638 doi: 10.1109/TFUZZ.2020.2985638
    [42] C. E. Ren, Q. X. Fu, J. A. Zhang, J. S. Zhao, Adaptive event-triggered control for nonlinear multi-agent systems with unknown control directions and actuator failures, Nonlinear Dyn., 105 (2021), 1657–1672. https://doi.org/10.1007/s11071-021-06684-w doi: 10.1007/s11071-021-06684-w
    [43] J. B. Qiu, M. Ma, H. Wang, Event-triggered adaptive fuzzy fault-tolerant control for stochastic nonlinear systems via command filtering, IEEE Trans. Syst. Man Cybern. Syst., 52 (2022), 1145–1155. https://doi.org/10.1109/TSMC.2020.3013744 doi: 10.1109/TSMC.2020.3013744
    [44] X. L. Wang, J. W. Xia, J. H. Park, X. P. Xie, G. L. Chen, Intelligent control of performance constrained switched nonlinear systems with random noises and its application: an event-driven approach, IEEE Trans. Circuits Syst. I Regular Papers, 69 (2022), 3736–3747. https://doi.org/10.1109/TCSI.2022.3175748 doi: 10.1109/TCSI.2022.3175748
    [45] C. Y. Wang, Z. Y. Ma, S. C. Tong, Adaptive fuzzy output-feedback event-triggered control for fractional-order nonlinear system, Math. Biosci. Eng., 19 (2022), 12334–12352. https://doi.org/10.3934/mbe.2022575 doi: 10.3934/mbe.2022575
    [46] P. Cheng, S. P. He, V. Stojanovic, X. L. Luan, F. Liu, Fuzzy fault detection for Markov jump systems with partly accessible hidden information: an event-triggered approach, IEEE Trans. Cybern., 52 (2022), 7352–7361. https://doi.org/10.1109/TCYB.2021.3050209 doi: 10.1109/TCYB.2021.3050209
    [47] J. Song, Y. K. Wang, Y. G. Niu, H. K. Lam, S. P. He, H. J. Liu, Periodic event-triggered terminal sliding mode speed control for networked PMSM system: A GA-optimized extended state observer approach, IEEE-ASME Trans. Mechatron., 27 (2022), 4153–4164. https://doi.org/10.1109/TMECH.2022.3148541 doi: 10.1109/TMECH.2022.3148541
    [48] S. X. Luo, F. Q. Deng, On event-triggered control of nonlinear stochastic systems, IEEE Trans. Autom. Control, 65 (2020), 369–375. https://doi.org/10.1109/TAC.2019.2916285 doi: 10.1109/TAC.2019.2916285
    [49] X. D. Li, D. X. Peng, J. D. Cao, Lyapunov stability for impulsive systems via event-triggered impulsive control, IEEE Trans. Autom. Control, 65 (2020), 4908–4913. https://doi.org/10.1109/TAC.2020.2964558 doi: 10.1109/TAC.2020.2964558
    [50] X. D. Li, X. Y. Yang, J. D. Cao, Event-triggered impulsive control for nonlinear delay systems, Automatica, 117 (2020). https://doi.org/10.1016/j.automatica.2020.108981 doi: 10.1016/j.automatica.2020.108981
    [51] F. Shu, J. Y. Zhai, Dynamic event-triggered output feedback control for a class of nonlinear systems with time-varying delays, Inf. Sci., 569 (2021), 205–216. https://doi.org/10.1016/j.ins.2021.04.020 doi: 10.1016/j.ins.2021.04.020
    [52] X. H. Ge, Q. L. Han, L. Ding, Y. L. Wang, X. M. Zhang, Dynamic event-triggered distributed coordination control and its applications: a survey of trends and techniques, IEEE Trans. Syst. Man Cybern. Syst., 50 (2020), 3112–3125. https://doi.org/10.1109/TSMC.2020.3010825 doi: 10.1109/TSMC.2020.3010825
    [53] L. J. Wang, C. L. P. Chen, Reduced-order observer-based dynamic event-triggered adaptive NN control for stochastic nonlinear systems subject to unknown input saturation, IEEE Trans. Neural Networks Learn. Syst., 32 (2021), 1678–1690. https://doi.org/10.1109/TNNLS.2020.2986281 doi: 10.1109/TNNLS.2020.2986281
    [54] M. Li, S. Li, C. K. Ahn, Z. R. Xiang, Adaptive fuzzy event-triggered command-filtered control for nonlinear time-delay systems, IEEE Trans. Fuzzy Syst., 30 (2022), 1025–1035. https://doi.org/10.1109/TFUZZ.2021.3052095 doi: 10.1109/TFUZZ.2021.3052095
    [55] A. H. Hu, J. H. Park, M. F. Hu, Consensus of nonlinear multiagent systems with intermittent dynamic event-triggered protocols, Nonlinear Dyn., 104 (2021), 1299–1313. https://doi.org/10.1007/s11071-021-06321-6 doi: 10.1007/s11071-021-06321-6
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