Event-triggered adaptive neural control for multiagent systems with deferred state constraints. (English) Zbl 1495.93053
Summary: This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints. A distributed event-triggered adaptive neural control approach is advanced. By virtue of a distributed sliding-mode estimator, the leader-following consensus control problem is converted into multiple simplified tracking control problems. Afterwards, a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time instant. Meanwhile, the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov function. In order to reduce the burden of communication, a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is excluded. Based on Lyapunov stability theorem, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded. Finally, a practical simulation example is given to verify the presented control scheme.
MSC:
| 93C65 | Discrete event control/observation systems |
| 93D50 | Consensus |
| 93A13 | Hierarchical systems |
| 93C40 | Adaptive control/observation systems |
| 93A16 | Multi-agent systems |
Keywords:
adaptive neural control; deferred time-varying state constraints; event-triggered mechanism; multiagent systems; sliding-mode estimatorReferences:
| [1] | Zhang, T. Y.; You, B.; Liu, G. P., Motion coordination for a class of multi-agents via networked predictive control, Journal of Systems Science and Complexity, 33, 3, 622-639 (2020) · Zbl 1447.93013 · doi:10.1007/s11424-020-8122-3 |
| [2] | Yang, B.; Zhou, Q.; Cao, L., Event-triggered control for multi-agent systems with prescribed performance and full state constraints, Acta Automatica Sinica, 45, 8, 1527-1535 (2019) · Zbl 1449.93183 |
| [3] | Liang H J, Zhang L C, Sun Y H, et al., Containment control of semi-Markovian multiagent systems with switching topologies, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, DOI: doi:10.1109/TSMC.2019.2946248. |
| [4] | Dong S L, Chen G R, Liu M Q, et al., Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance, SCIENCE CHINA Information Sciences, 2020, DOI: doi:10.1007/s11432-020-3122-6. |
| [5] | Wu Y, Liang H J, Zhang Y H, et al., Cooperative adaptive dynamic surface control for a class of high-order stochastic nonlinear multiagent systems, IEEE Transactions on Cybernetics, 2020, DOI: doi:10.1109/TCYB.2020.2986332. |
| [6] | Ren, H. R.; Karimi, H. R.; Lu, R. Q., Synchronization of network systems via aperiodic sampleddata control with constant delay and application to unmanned ground vehicles, IEEE Transactions on Industrial Electronics, 67, 6, 4980-4990 (2020) · doi:10.1109/TIE.2019.2928241 |
| [7] | Yang, L. Y.; Liu, S. J., Distributed stochastic source seeking for multiple vehicles over fixed topology, Journal of Systems Science and Complexity, 33, 3, 652-671 (2020) · Zbl 1447.93240 · doi:10.1007/s11424-020-8309-7 |
| [8] | Liang H J, Liu G L, Zhang H G, et al., Neural-network-based event-triggered adaptive control of nonaffine nonlinear multiagent systems with dynamic uncertainties, IEEE Transactions on Neural Networks and Learning Systems, 2020, DOI: doi:10.1109/TNNLS.2020.3003950. |
| [9] | Zhang, H. W.; Chen, J., Bipartite consensus of multi-agent systems over signed graphs: State feedback and output feedback control approaches, International Journal of Robust and Nonlinear Control, 27, 1, 3-14 (2017) · Zbl 1353.93011 · doi:10.1002/rnc.3552 |
| [10] | Xiao, W. B.; Cao, L.; Li, H. Y., Observer-based adaptive consensus control for nonlinear multiagent systems with time-delay, SCIENCE CHINA Information Sciences, 63, 132202 (2020) · doi:10.1007/s11432-019-2678-2 |
| [11] | Li, H. Y.; Wu, Y.; Chen, M., Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm, IEEE Transactions on Cybernetics, 51, 3, 1163-1174 (2021) · doi:10.1109/TCYB.2020.2982168 |
| [12] | Fan, B.; Guo, S. L.; Peng, J. K., A consensus-based algorithm for power sharing and voltage regulation in DC microgrids, IEEE Transactions on Industrial Informatics, 16, 6, 3987-3996 (2020) · doi:10.1109/TII.2019.2941268 |
| [13] | Li, J. L.; Yang, Q. M.; Fan, B., Robust state/output-feedback control of coaxial-rotor MAVs based on adaptive NN approach, IEEE Transactions on Neural Networks and Learning Systems, 30, 12, 3547-3557 (2019) · doi:10.1109/TNNLS.2019.2911649 |
| [14] | Zhang M H, Jing X J, and Wang G, Bioinspired nonlinear dynamics-based adaptive neural network control for vehicle suspension systems with uncertain/unknown dynamics and input delay, IEEE Transactions on Industrial Electronics, 2020, DOI: doi:10.1109/TIE.2020.3040667. |
| [15] | Zhou, Q.; Zhao, S. Y.; Li, H. Y., Adaptive neural network tracking control for robotic manipulators with dead zone, IEEE Transactions on Neural Networks and Learning Systems, 30, 12, 3611-3620 (2019) · doi:10.1109/TNNLS.2018.2869375 |
| [16] | Song, Y. D.; Huang, X. C.; Wen, C. Y., Tracking control for a class of unknown nonsquare MIMO nonaffine systems: A deep-rooted information based robust adaptive approach, IEEE Transactions on Automatic Control, 61, 10, 3227-3233 (2016) · doi:10.1109/TAC.2015.2508741 |
| [17] | Pan Y N, Du P H, Xue H, et al., Singularity-free fixed-time fuzzy control for robotic systems with user-defined performance, IEEE Transactions on Fuzzy Systems, 2020, DOI: doi:10.1109/TFUZZ.2020.2999746. |
| [18] | Zhang M H and Jing X J, Adaptive neural network tracking control for double-pendulum tower crane systems with nonideal inputs, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, DOI: doi:10.1109/TSMC.2020.3048722. |
| [19] | Liu, Q.; Leng, J. W.; Yan, D. X., Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system, Journal of Manufacturing Systems, 58, 52-64 (2021) · doi:10.1016/j.jmsy.2020.04.012 |
| [20] | Song, Y. D.; Huang, X. C.; Jia, Z. J., Dealing with the issues crucially related to the functionality and reliability of NN-associated control for nonlinear uncertain systems, IEEE Transactions on Neural Networks and Learning Systems, 28, 11, 2614-2625 (2017) · doi:10.1109/TNNLS.2016.2598616 |
| [21] | Chen, C. L P.; Ren, C. E.; Du, T., Fuzzy observed-based adaptive consensus tracking control for second-order multiagent systems with heterogeneous nonlinear dynamics, IEEE Transactions on Fuzzy Systems, 24, 4, 906-915 (2016) · doi:10.1109/TFUZZ.2015.2486817 |
| [22] | Lin G H, Li H Y, Ma H, et al., Human-in-the-loop consensus control for nonlinear multiagent systems with actuator faults, IEEE/CAA Journal of Automatica Sinica, 2020, DOI: doi:10.1109/JAS.2020.1003596. |
| [23] | Liu Y, Yao D Y, Li H Y, et al., Distributed cooperative compound tracking control for a platoon of vehicles with adaptive NN, IEEE Transactions on Cybernetics, 2020, DOI: doi:10.1109/TCYB.2020.3044883. |
| [24] | Wang, F.; Chen, B.; Lin, C., Distributed adaptive neural control for stochastic nonlinear multiagent systems, IEEE Transactions on Cybernetics, 47, 7, 1795-1803 (2016) · Zbl 1354.37051 · doi:10.1109/TCYB.2016.2623898 |
| [25] | Liu, Y. J.; Lu, S. M.; Li, D. J., Adaptive controller design-based ABLF for a class of nonlinear time-varying state constraint systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 7, 1546-1553 (2017) · doi:10.1109/TSMC.2016.2633007 |
| [26] | Fan, B.; Yang, Q. M.; Jagannathan, S., Output-constrained control of nonaffine multiagent systems with partially unknown control directions, IEEE Transactions on Automatic Control, 64, 9, 3936-3942 (2019) · Zbl 1482.93015 · doi:10.1109/TAC.2019.2892391 |
| [27] | Cao L, Ren H R, Meng W, et al., Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints, SCIENCE CHINA Information Sciences, 2020, DOI: doi:10.1007/s11432-020-3128-2. |
| [28] | Ren, H. R.; Lu, R. Q.; Xiong, J. L., Optimal filtered and smoothed estimators for discrete-time linear systems with multiple packet dropouts under Markovian communication constraints, IEEE Transactions on Cybernetics, 50, 9, 4169-4181 (2020) · doi:10.1109/TCYB.2019.2924485 |
| [29] | Wang, J.; Shi, L. R.; Guan, X. P., Semi-global leaderless consensus of linear multi-agent systems with actuator and communication constraints, Journal of Systems Science and Complexity, 33, 4, 882-902 (2020) · Zbl 1455.93185 · doi:10.1007/s11424-020-8366-y |
| [30] | Su, Y. X.; Wang, Q. L.; Sun, C. Y., Self-triggered consensus control for linear multi-agent systems with input saturation, IEEE/CAA Journal of Automatica Sinica, 7, 1, 150-157 (2020) · doi:10.1109/JAS.2019.1911837 |
| [31] | Shen, D.; Xu, J. X., Distributed learning consensus for heterogenous high-order nonlinear multi-agent systems with output constraints, Automatica, 97, 64-72 (2018) · Zbl 1406.93031 · doi:10.1016/j.automatica.2018.07.030 |
| [32] | Shen, D.; Xu, J. X., Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints, International Journal of Adaptive Control and Signal Processing, 31, 12, 1779-1807 (2017) · Zbl 1383.93004 · doi:10.1002/acs.2799 |
| [33] | Meng, W. C.; Yang, Q. M.; Si, J., Consensus control of nonlinear multiagent systems with time-varying state constraints, IEEE Transactions on Cybernetics, 47, 8, 2110-2120 (2016) · doi:10.1109/TCYB.2016.2629268 |
| [34] | Yang, B.; Xiao, W. B.; Yin, H., Adaptive neural control for multiagent systems with asymmetric time-varying state constraints and input saturation, International Journal of Robust and Nonlinear Control, 30, 12, 4764-4778 (2020) · Zbl 1466.93092 · doi:10.1002/rnc.5004 |
| [35] | Nowzari, C.; Garcia, E.; Cortés, J., Event-triggered communication and control of networked systems for multi-agent consensus, Automatica, 105, 1-27 (2019) · Zbl 1429.93339 · doi:10.1016/j.automatica.2019.03.009 |
| [36] | Ma, H.; Li, H. Y.; Lu, R. Q., Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances, SCIENCE CHINA Information Sciences, 63, 150212 (2020) · doi:10.1007/s11432-019-2680-1 |
| [37] | Zhao, Q. T.; Sun, J.; Bai, Y. Q., Dynamic event-triggered control for nonlinear systems: A small-gain approach, Journal of Systems Science and Complexity, 33, 4, 930-943 (2020) · Zbl 1455.93121 · doi:10.1007/s11424-020-9210-0 |
| [38] | Liang H J, Guo X Y, Pan Y N, et al., Event-triggered fuzzy bipartite tracking control for network systems based on distributed reduced-order observers, IEEE Transactions on Fuzzy Systems, 2020, DOI: doi:10.1109/TFUZZ.2020.2982618. |
| [39] | Zhou Q, Wang W, Ma H, et al., Event-triggered fuzzy adaptive containment control for nonlinear multi-agent systems with unknown Bouc-Wen hysteresis input, IEEE Transactions on Fuzzy Systems, 2019, DOI: doi:10.1109/TFUZZ.2019.2961642. |
| [40] | Yao, D. Y.; Li, H. Y.; Lu, R. Q., Distributed sliding mode tracking control of second-order nonlinear multi-agent systems: An event-triggered approach, IEEE Transactions on Cybernetics, 50, 9, 3892-3902 (2020) · doi:10.1109/TCYB.2019.2963087 |
| [41] | Yang Q L, Sun J, and Chen J, Output consensus for heterogeneous linear multiagent systems with a predictive event-triggered mechanism, IEEE Transactions on Cybernetics, 2019, DOI: doi:10.1109/TCYB.2019.2895044. |
| [42] | Xu, Y.; Wu, Z. G., Distributed adaptive event-triggered fault-tolerant synchronization for multiagent systems, IEEE Transactions on Industrial Electronics, 68, 2, 1537-1547 (2021) · doi:10.1109/TIE.2020.2967739 |
| [43] | Xu, Y.; Fang, M.; Pan, Y. J., Event-triggered output synchronization for nonhomogeneous agent systems with periodic denial-of-service attacks, International Journal of Robust and Nonlinear Control, 31, 1851-1865 (2021) · Zbl 1526.93165 · doi:10.1002/rnc.5223 |
| [44] | Bai, W. W.; Li, T. S.; Tong, S. C., NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems, IEEE Transactions on Cybernetics, 50, 11, 4573-4584 (2020) · doi:10.1109/TCYB.2020.2963849 |
| [45] | Yang, C. G.; Ge, S. Z.; Xiang, C., Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach, IEEE Transactions on Neural Networks, 19, 11, 1873-1886 (2008) · doi:10.1109/TNN.2008.2003290 |
| [46] | Song, Y. D.; Zhou, S. Y., Tracking control of uncertain nonlinear systems with deferred asymmetric time-varying full state constraints, Automatica, 98, 314-322 (2018) · Zbl 1406.93172 · doi:10.1016/j.automatica.2018.09.032 |
| [47] | Cao, Y. C.; Ren, W.; Meng, Z. Y., Decentralized finite-time sliding mode estimators and their applications in decentralized finite-time formation tracking, Systems and Control Letters, 59, 9, 522-529 (2010) · Zbl 1207.93103 · doi:10.1016/j.sysconle.2010.06.002 |
| [48] | Chen, B.; Liu, X. P.; Liu, K. F., Direct adaptive fuzzy control of nonlinear strict-feedback systems, Automatica, 45, 6, 1530-1535 (2009) · Zbl 1166.93341 · doi:10.1016/j.automatica.2009.02.025 |
| [49] | Xing, L. T.; Wen, C. Y.; Liu, Z. T., Event-triggered output feedback control for a class of uncertain nonlinear systems, IEEE Transactions on Automatic Control, 64, 1, 290-297 (2019) · Zbl 1423.93341 · doi:10.1109/TAC.2018.2823386 |
| [50] | Wang, W.; Tong, S. C., Adaptive fuzzy bounded control for consensus of multiple strict-feedback nonlinear systems, IEEE Transactions on Cybernetics, 48, 2, 522-531 (2018) · doi:10.1109/TCYB.2016.2645763 |
| [51] | Dong, G. W.; Li, H. Y.; Ma, H., Finite-time consensus tracking neural network FTC of multiagent systems, IEEE Transactions on Neural Networks and Learning Systems, 32, 2, 653-662 (2021) · doi:10.1109/TNNLS.2020.2978898 |
| [52] | Du P H, Pan Y N, Li H Y, et al., Nonsingular finite-time event-triggered fuzzy control for large-scale nonlinear systems, IEEE Transactions on Fuzzy Systems, 2020, DOI: doi:10.1109/TFUZZ.2020.2992632. |
| [53] | Li, Q. B.; Guo, J.; Sun, C. Y., Finite-time synchronization for a class of dynamical complex networks with nonidentical nodes and uncertain disturbance, Journal of Systems Science and Complexity, 32, 3, 818-834 (2019) · Zbl 1414.93163 · doi:10.1007/s11424-018-8141-5 |
| [54] | Song, Y. D.; Wang, Y. J.; Holloway, J., Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time, Automatica, 83, 1, 243-251 (2017) · Zbl 1373.93136 · doi:10.1016/j.automatica.2017.06.008 |
| [55] | Dong G W, Cao L, Yao D Y, et al., Adaptive attitude control for multi-MUAV systems with output dead-zone and actuator fault, IEEE/CAA Journal of Automatica Sinica, 2020, DOI: doi:10.1109/JAS.2020.1003605. |
| [56] | Song, Y. D.; Wang, Y. J.; Wen, C. Y., Adaptive fault-tolerant PI tracking control with guaranteed transient and steady-state performance, IEEE Transactions on Automatic Control, 62, 1, 481-487 (2017) · Zbl 1359.93233 · doi:10.1109/TAC.2016.2554362 |
| [57] | Huang Y B, He Y, An J Q, et al., Polynomial-type Lyapunov-Krasovskii functional and Jacobi-Bessel inequality: Further results on stability analysis of time-delay systems, IEEE Transactions on Automatic Control, 2020, DOI: doi:10.1109/TAC.2020.3013930. · Zbl 1467.93243 |
| [58] | Long, F.; Zhang, C. K.; Jiang, L., Stability analysis of systems with time-varying delay via improved Lyapunov-Krasovskii functionals, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 4, 2457-2466 (2021) · doi:10.1109/TSMC.2019.2914367 |
| [59] | Zhang, C. K.; Long, F.; He, Y., A relaxed quadratic function negative-determination lemma and its application to time-delay systems, Automatica, 113, 108764 (2020) · Zbl 1440.93144 · doi:10.1016/j.automatica.2019.108764 |
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