This paper addresses the consensus problem of Markov jump multi-agent systems under dynamic event-triggered communication. In order to utilize the limited network resources reasonably and improve the efficiency of data transmission, a dynamic event-triggered method is adopted. Considering the challenge of obtaining system mode information, a hidden Markov model is introduced. On this basis, because of the limitation of mode information acquisition, the case with partially unknown probabilities both exist in the transition probability matrix and the observation probability matrix is discussed, which makes the conclusion realistic. Moreover, a sampled-data consensus protocol is proposed, and based on the Lyapunov stability theory, several sufficient conditions are derived to ensure the consensus of the system under specified performance. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.
CaoYWenCSongY (2021a) A unified event-triggered control approach for uncertain pure-feedback systems with or without state constraints. IEEE Transactions on Cybernetics51(3): 1262–1271.
2.
CaoZNiuYSongJ (2020) Finite-time sliding-mode control of Markovian jump cyber-physical systems against randomly occurring injection attacks. IEEE Transactions on Automatic Control65(3): 1264–1271.
3.
CaoZNiuYLamJ, et al. (2021b) A hybrid sliding mode control scheme of Markovian jump systems via transition rates optimal design. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(12): 7752–7763.
4.
ChenMYanHZhangH, et al. (2021) Dynamic event-triggered asynchronous control for nonlinear multiagent systems based on T–S fuzzy models. IEEE Transactions on Fuzzy Systems29(9): 2580–2592.
5.
ChengJParkJHWuZG (2021) A hidden Markov model based control for periodic systems subject to singular perturbations. Systems & Control Letters157: 105059.
6.
ChengJWuYWuZ, et al. (2022a) Fuzzy filter design for affine systems with sensor faults: a dynamic event-triggered approach. Journal of Systems Science and Complexity35: 1761–1784.
7.
ChengJWuYYanH, et al. (2022b) Protocol-based filtering for fuzzy Markov affine systems with switching chain. Automatica141: 110321.
8.
do Valle CostaOLFragosoMDTodorovMG (2015) A detector-based approach for the H2 control of Markov jump linear systems with partial information. IEEE Transactions on Automatic Control60(5): 1219–1234.
9.
DongSRenWWuZG, et al. (2020) H∞ output consensus for Markov jump multiagent systems with uncertainties. IEEE Transactions on Cybernetics50(5): 2264–2273.
10.
FanMCWangM (2019) Second-order consensus for a class of uncertain multi-agent systems subject to input saturation. Transactions of the Institute of Measurement and Control41(7): 1957–1964.
11.
GaoJLiJPanH, et al. (2020) Consensus via event-triggered strategy of nonlinear multi-agent systems with Markovian switching topologies. ISA Transactions104: 122–129.
12.
GuoGDingLHanQL (2014) A distributed event-triggered transmission strategy for sampled-data consensus of multi-agent systems. Automatica50(5): 1489–1496.
13.
HeemelsWHDonkersMTeelAR (2013) Periodic event-triggered control for linear systems. IEEE Transactions on Automatic Control58(4): 847–861.
14.
HuYLamJLiangJ (2013) Consensus control of multi-agent systems with missing data in actuators and Markovian communication failure. International Journal of Systems Science44(10): 1867–1878.
15.
HuangWLinCHuB, et al. (2022) Robust distributed adaptive consensus for linear multiagent systems with uncertain topologies. Transactions of the Institute of Measurement and Control44(9): 1723–1731.
16.
HuoSZhangY (2020) H∞ consensus of Markovian jump multi-agent systems under multi-channel transmission via output feedback control strategy. ISA Transactions99: 28–36.
17.
HuoSGeLLiF (2021) Robust H∞ consensus for Markov jump multiagent systems under mode-dependent observer and quantizer. IEEE Systems Journal15(2): 2443–2450.
18.
HuoSZhangYLewisFL, et al. (2022) Observer-based resilient consensus control for heterogeneous multi-agent systems against cyber-attacks. IEEE Transactions on Control of Network Systems. Epub ahead of print 2September. DOI: 10.1109/TCNS.2022. 3203905.
19.
LiFSongSZhaoJ, et al. (2019) Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities. Applied Mathematics and Computation360: 1–13.
20.
LuanXMinYDingZ, et al. (2017) Stochastic given-time H∞ consensus over Markov jump networks with disturbance constraint. Transactions of the Institute of Measurement and Control39(8): 1253–1261.
21.
MinYLuanXDingZ, et al. (2018) Given-time consensus for stochastic Markov jump networks by dynamic output feedback. Transactions of the Institute of Measurement and Control40(10): 3160–3168.
SakthivelRSakthivelRKaviarasanB, et al. (2018) Leader-following exponential consensus of input saturated stochastic multi-agent systems with Markov jump parameters. Neurocomputing287: 84–92.
24.
ShenHHuXWangJCaoJ, et al. (2021) Non-fragile H∞ synchronization for Markov jump singularly perturbed coupled neural networks subject to double-layer switching regulation. IEEE Transactions on Neural Networks and Learning Systems. Epub ahead of print 6September. DOI: 10.1109/TNNLS.2021.3107607.
25.
ShenHXingMYanH, et al. (2022) Observer-based I2–I∞ control for singularly perturbed semi-Markov jump systems with improved weighted TOD protocol. Science China Information Sciences65(9): 199204.
26.
StadtmannFCostaOL (2017) H2-control of continuous-time hidden Markov jump linear systems. IEEE Transactions on Automatic Control62(8): 4031–4037.
27.
WangJWuJCaoJ, et al. (2022a) Nonfragile output feedback tracking control for Markov jump fuzzy systems based on integral reinforcement learning scheme. IEEE Transactions on Cybernetics. Epub ahead of print 4October. DOI: 10.1109/TCYB.2022. 3203795.
28.
WangJYangCXiaJ, et al. (2022b) Observer-based sliding mode control for networked fuzzy singularly perturbed systems under weighted try-once-discard protocol. IEEE Transactions on Fuzzy Systems30(6): 1889–1899.
29.
WangXLiJXingJ, et al. (2018) A novel finite-time average consensus protocol for multi-agent systems with switching topology. Transactions of the Institute of Measurement and Control40(2): 606–614.
30.
WuZGShiPShuZ, et al. (2017) Passivity-based asynchronous control for Markov jump systems. IEEE Transactions on Automatic Control62(4): 2020–2025.
31.
XieDYuanDLuJ, et al. (2013) Consensus control of second-order leader–follower multi-agent systems with event-triggered strategy. Transactions of the Institute of Measurement and Control35(4): 426–436.
32.
YanZSangCFangM, et al. (2018) Energy-to-peak consensus for multi-agent systems with stochastic disturbances and Markovian switching topologies. Transactions of the Institute of Measurement and Control40(16): 4358–4368.
33.
YangZZhengSLiangB, et al. (2021) Event-triggered finite-time consensus for stochastic multi-agent systems. Transactions of the Institute of Measurement and Control43(4): 782–791.
34.
YaoDZhangBLiP, et al. (2019) Event-triggered sliding mode control of discrete-time Markov jump systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems49(10): 2016–2025.
35.
YiJWWangYWXiaoJW (2014) Consensus in Markovian jump second-order multi-agent systems with random communication delay. IET Control Theory & Applications8(16): 1666–1675.
36.
ZhaoHFeiS (2018) Consensus for discrete-time heterogeneous networked systems consisting of third-order and first-order agents under fixed and switching topologies. Transactions of the Institute of Measurement and Control40(9): 2748–2755.
37.
ZhaoHMengXWuS (2021) Distributed edge-based event-triggered coordination control for multi-agent systems. Automatica132: 109797.
38.
ZhaoHParkJHZhangY, et al. (2014) Distributed output feedback consensus of discrete-time multi-agent systems. Neuralcomputing138: 86–91.
39.
ZhuKWangZChenY, et al. (2021) Neural-network-based set-membership fault estimation for 2-D systems under encoding-decoding mechanism. IEEE Transactions on Neural Networks and Learning Systems. Epub ahead of print 12August. DOI: 10.1109/TNNLS.2021.3102127.
40.
ZhuWPuHWangD, et al. (2017) Event-based consensus of second-order multi-agent systems with discrete time. Automatica79: 78–83.
