Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

doi:10.1007/s11424-021-0201-6

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints. A distributed eventtriggered 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.

Key words: Adaptive neural control, deferred time-varying state constraints, event-triggered mechanism, multiagent systems, sliding-mode estimator

[1] Zhang T Y, You B, and Liu G P, Motion coordination for a class of multi-agents via networked predictive control, Journal of Systems Science and Complexity, 2020, 33(3):622-639.
[2] Yang B, Zhou Q, Cao L, et al., Event-triggered control for multi-agent systems with prescribed performance and full state constraints, Acta Automatica Sinica, 2019, 45(8):1527-1535.
[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: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: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:10.1109/TCYB.2020.2986332.
[6] Ren H R, Karimi H R, Lu R Q, et al., Synchronization of network systems via aperiodic sampleddata control with constant delay and application to unmanned ground vehicles, IEEE Transactions on Industrial Electronics, 2020, 67(6):4980-4990.
[7] Yang L Y and Liu S J, Distributed stochastic source seeking for multiple vehicles over fixed topology, Journal of Systems Science and Complexity, 2020, 33(3):652-671.
[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:10.1109/TNNLS.2020.3003950.
[9] Zhang H W and 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, 2017, 27(1):3-14.
[10] Xiao W B, Cao L, Li H Y, et al., Observer-based adaptive consensus control for nonlinear multiagent systems with time-delay, SCIENCE CHINA Information Sciences, 2020, 63:132202.
[11] Li H Y, Wu Y, and Chen M, Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm, IEEE Transactions on Cybernetics, 2021, 51(3):1163-1174.
[12] Fan B, Guo S L, Peng J K, et al., A consensus-based algorithm for power sharing and voltage regulation in DC microgrids, IEEE Transactions on Industrial Informatics, 2020, 16(6):3987-3996.
[13] Li J L, Yang Q M, Fan B, et al., Robust state/output-feedback control of coaxial-rotor MAVs based on adaptive NN approach, IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(12):3547-3557.
[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:10.1109/TIE.2020.3040667.
[15] Zhou Q, Zhao S Y, Li H Y, et al., Adaptive neural network tracking control for robotic manipulators with dead zone, IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(12):3611-3620.
[16] Song Y D, Huang X C, and 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, 2016, 61(10):3227-3233.
[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: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:10.1109/TSMC.2020.3048722.
[19] Liu Q, Leng J W, Yan D X, et al., Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system, Journal of Manufacturing Systems, 2021, 58:52-64.
[20] Song Y D, Huang X C, and 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, 2017, 28(11):2614-2625.
[21] Chen C L P, Ren C E, and Du T, Fuzzy observed-based adaptive consensus tracking control for second-order multiagent systems with heterogeneous nonlinear dynamics, IEEE Transactions on Fuzzy Systems, 2016, 24(4):906-915.
[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: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:10.1109/TCYB.2020.3044883.
[24] Wang F, Chen B, Lin C, et al., Distributed adaptive neural control for stochastic nonlinear multiagent systems, IEEE Transactions on Cybernetics, 2016, 47(7):1795-1803.
[25] Liu Y J, Lu S M, Li D J, et al., Adaptive controller design-based ABLF for a class of nonlinear time-varying state constraint systems, IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2017, 47(7):1546-1553.
[26] Fan B, Yang Q M, Jagannathan S, et al., Output-constrained control of nonaffine multiagent systems with partially unknown control directions, IEEE Transactions on Automatic Control, 2019, 64(9):3936-3942.
[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:10.1007/s11432-020-3128-2.
[28] Ren H R, Lu R Q, Xiong J L, et al., Optimal filtered and smoothed estimators for discrete-time linear systems with multiple packet dropouts under Markovian communication constraints, IEEE Transactions on Cybernetics, 2020, 50(9):4169-4181.
[29] Wang J, Shi L R, and Guan X P, Semi-global leaderless consensus of linear multi-agent systems with actuator and communication constraints, Journal of Systems Science and Complexity, 2020, 33(4):882-902.
[30] Su Y X, Wang Q L, and Sun C Y, Self-triggered consensus control for linear multi-agent systems with input saturation, IEEE/CAA Journal of Automatica Sinica, 2020, 7(1):150-157.
[31] Shen D, Xu J X, Distributed learning consensus for heterogenous high-order nonlinear multi-agent systems with output constraints, Automatica, 2018, 97:64-72.
[32] Shen D and Xu J X, Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints, International Journal of Adaptive Control and Signal Processing, 2017, 31(12):1779-1807.
[33] Meng W C, Yang Q M, Si J, et al., Consensus control of nonlinear multiagent systems with time-varying state constraints, IEEE Transactions on Cybernetics, 2016, 47(8):2110-2120.
[34] Yang B, Xiao W B, Yin H, et al., Adaptive neural control for multiagent systems with asymmetric time-varying state constraints and input saturation, International Journal of Robust and Nonlinear Control, 2020, 30(12):4764-4778.
[35] Nowzari C, Garcia E, and Cortès J, Event-triggered communication and control of networked systems for multi-agent consensus, Automatica, 2019, 105:1-27.
[36] Ma H, Li H Y, Lu R Q, et al., Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances, SCIENCE CHINA Information Sciences, 2020, 63:150212.
[37] Zhao Q T, Sun J, and Bai Y Q, Dynamic event-triggered control for nonlinear systems:A small-gain approach, Journal of Systems Science and Complexity, 2020, 33(4):930-943.
[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: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:10.1109/TFUZZ.2019.2961642.
[40] Yao D Y, Li H Y, Lu R Q, et al., Distributed sliding mode tracking control of second-order nonlinear multi-agent systems:An event-triggered approach, IEEE Transactions on Cybernetics, 2020, 50(9):3892-3902.
[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:10.1109/TCYB.2019.2895044.
[42] Xu Y and Wu Z G, Distributed adaptive event-triggered fault-tolerant synchronization for multiagent systems, IEEE Transactions on Industrial Electronics, 2021, 68(2):1537-1547.
[43] Xu Y, Fang M, Pan Y J, et al., Event-triggered output synchronization for nonhomogeneous agent systems with periodic denial-of-service attacks, International Journal of Robust and Nonlinear Control, 2021, 31:1851-1865.
[44] Bai W W, Li T S, and Tong S C, NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems, IEEE Transactions on Cybernetics, 2020, 50(11):4573-4584.
[45] Yang C G, Ge S Z, Xiang C, et al., Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach, IEEE Transactions on Neural Networks, 2008, 19(11):1873-1886.
[46] Song Y D and Zhou S Y, Tracking control of uncertain nonlinear systems with deferred asymmetric time-varying full state constraints, Automatica, 2018, 98:314-322.
[47] Cao Y C, Ren W, and Meng Z Y, Decentralized finite-time sliding mode estimators and their applications in decentralized finite-time formation tracking, Systems and Control Letters, 2010, 59(9):522-529.
[48] Chen B, Liu X P, Liu K F, et al., Direct adaptive fuzzy control of nonlinear strict-feedback systems, Automatica, 2009, 45(6):1530-1535.
[49] Xing L T, Wen C Y, Liu Z T, et al., Event-triggered output feedback control for a class of uncertain nonlinear systems, IEEE Transactions on Automatic Control, 2019, 64(1):290-297.
[50] Wang W and Tong S C, Adaptive fuzzy bounded control for consensus of multiple strict-feedback nonlinear systems, IEEE Transactions on Cybernetics, 2018, 48(2):522-531.
[51] Dong G W, Li H Y, Ma H, et al., Finite-time consensus tracking neural network FTC of multiagent systems, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2):653-662.
[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:10.1109/TFUZZ.2020.2992632.
[53] Li Q B, Guo J, Sun C Y, et al., Finite-time synchronization for a class of dynamical complex networks with nonidentical nodes and uncertain disturbance, Journal of Systems Science and Complexity, 2019, 32(3):818-834.
[54] Song Y D, Wang Y J, Holloway J, et al., Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time, Automatica, 2017, 83(1):243-251.
[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:10.1109/JAS.2020.1003605.
[56] Song Y D, Wang Y J, and Wen C Y, Adaptive fault-tolerant PI tracking control with guaranteed transient and steady-state performance, IEEE Transactions on Automatic Control, 2017, 62(1):481-487.
[57] Huang Y B, He Y, An J Q, et al., Polynomial-type Lyapunov-Krasovskii functional and JacobiBessel inequality:Further results on stability analysis of time-delay systems, IEEE Transactions on Automatic Control, 2020, DOI:10.1109/TAC.2020.3013930.
[58] Long F, Zhang C K, Jiang L, et al., Stability analysis of systems with time-varying delay via improved Lyapunov-Krasovskii functionals, IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2021, 51(4):2457-2466.
[59] Zhang C K, Long F, He Y, et al., A relaxed quadratic function negative-determination lemma and its application to time-delay systems, Automatica, 2020, 113:108764.

[1]	WANG Zhenhua , YOU Keyou , XU Juanjuan, ZHANG Huanshui. CONSENSUS DESIGN FOR CONTINUOUS-TIME MULTI-AGENT SYSTEMS WITH COMMUNICATION DELAY [J]. Journal of Systems Science and Complexity, 2014, 27(4): 701-711.

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