doi: 10.3934/dcdss.2020379

Event-triggered adaptive fault-tolerant control for multi-agent systems with unknown disturbances

1. 

School of Automation and Guangdong Provincial Key Laboratory of Intelligent, Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China

2. 

College of Engineering, Bohai University, Jinzhou 121013, China

* Corresponding author: Shubo Li

Received  January 2020 Revised  February 2020 Published  May 2020

This paper presents an event-triggered consensus control protocol for a class of multi-agent systems with actuator faults, sensor faults and unknown disturbances. The adaptive neural network compensation control method is introduced to solve the problem of sensor faults. The event-triggered mechanism is developed to reduce the communication burden. In the control design process, the radial basis function neural networks are used to approximate the unknown nonlinear functions, and a nonlinear disturbance observer is used to eliminate the effect of unknown external disturbances. Furthermore, based on the graph theory and Lyapunov stability theory, it is further shown that the consensus tracking errors are semi-globally uniformly ultimately bounded. Finally, the simulation example illustrates the effectiveness of the designed control protocol.

Citation: Hongru Ren, Shubo Li, Changxin Lu. Event-triggered adaptive fault-tolerant control for multi-agent systems with unknown disturbances. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020379
References:
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show all references

References:
[1]

A. BounemeurM. Chemachema and N. Essounbouli, Indirect adaptive fuzzy fault-tolerant tracking control for mimo nonlinear systems with actuator and sensor failures, ISA Transactions, 79 (2018), 45-61.  doi: 10.1016/j.isatra.2018.04.014.  Google Scholar

[2]

L. Cao, H. Li, G. Dong and R. Lu, Event-triggered control for multiagent systems with sensor faults and input saturation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 1–12. doi: 10.1109/TSMC.2019.2938216.  Google Scholar

[3]

Z. Chen, Q.-L. Han, Y. Yan and Z. Wu, How often should one update control and estimation: Review of networked triggering techniques, SCIENCE CHINA Information Sciences, 63 (2020), 150201. doi: 10.1007/s11432-019-2637-9.  Google Scholar

[4]

P. Du, H. Liang, S. Zhao and C. K. Ahn, Neural-based decentralized adaptive finite-time control for nonlinear large-scale systems with time-varying output constraints, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 1–12. doi: 10.1109/TSMC.2019.2918351.  Google Scholar

[5]

Z. HouL. Cheng and M. Tan, Decentralized robust adaptive control for the multiagent system consensus problem using neural networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39 (2009), 636-647.  doi: 10.1109/TSMCB.2008.2007810.  Google Scholar

[6]

J. HuY. WuT. Li and B. K. Ghosh, Consensus control of general linear multiagent systems with antagonistic interactions and communication noises, IEEE Transactions on Automatic Control, 64 (2018), 2122-2127.  doi: 10.1109/TAC.2018.2872197.  Google Scholar

[7]

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[9]

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[11]

J. LengH. ZhangD. YanQ. LiuX. Chen and D. Zhang, Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop, Journal of Ambient Intelligence and Humanized Computing, 10 (2019), 1155-1166.  doi: 10.1007/s12652-018-0881-5.  Google Scholar

[12]

X. LiD. W. Ho and J. Cao, Finite-time stability and settling-time estimation of nonlinear impulsive systems, Automatica, 99 (2019), 361-368.  doi: 10.1016/j.automatica.2018.10.024.  Google Scholar

[13]

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[14]

X. Li and J. Wu, Stability of nonlinear differential systems with state-dependent delayed impulses, Automatica, 64 (2016), 63-69.  doi: 10.1016/j.automatica.2015.10.002.  Google Scholar

[15]

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[16]

Y. Li and G. Yang, Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems with actuator faults and unknown dead zones, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (2016), 729-740.  doi: 10.1109/TSMC.2016.2521824.  Google Scholar

[17]

H. Liang, L. Zhang, Y. Sun and T. Huang, Containment control of semi-Markovian multiagent systems with switching topologies, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 1–11. doi: 10.1109/TSMC.2019.2946248.  Google Scholar

[18]

H. Liang, X. Guo, Y. Pan and T. Huang, Event-triggered fuzzy bipartite tracking control for network systems based on distributed reduced-order observers, IEEE Transactions on Fuzzy Systems, 2020, 1–1. doi: 10.1109/TFUZZ.2020.2982618.  Google Scholar

[19]

Q. LiuH. ZhangJ. Leng and X. Chen, Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system, International Journal of Production Research, 57 (2019), 3903-3919.  doi: 10.1080/00207543.2018.1471243.  Google Scholar

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Y. LiuX. LiuY. JingX. Chen and J. Qiu, Direct adaptive preassigned finite-time control with time-delay and quantized input using neural network, IEEE Transactions on Neural Networks and Learning Systems, 31 (2020), 1222-1231.  doi: 10.1109/TNNLS.2019.2919577.  Google Scholar

[21]

Q. Liu, J. Leng, D. Yan, D. Zhang, L. Wei, A. Yu, R. Zhao, H. Zhang and X. Chen, Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system, Journal of Manufacturing Systems, 2020, 1–13. doi: 10.1016/j.jmsy.2020.04.012.  Google Scholar

[22]

Y. LiuX. LiuY. Jing and Z. Zhang, A novel finite-time adaptive fuzzy tracking control scheme for nonstrict feedback systems, IEEE Transactions on Fuzzy Systems, 27 (2018), 646-658.  doi: 10.1109/TFUZZ.2018.2866264.  Google Scholar

[23]

R. LuY. XuA. Xue and J. Zheng, Networked control with state reset and quantized measurements: Observer-based case, IEEE Transactions on Industrial Electronics, 60 (2012), 5206-5213.  doi: 10.1109/TIE.2012.2227910.  Google Scholar

[24]

R. LuW. YuJ. Lü and A. Xue, Synchronization on complex networks of networks, IEEE Transactions on Neural Networks and Learning Systems, 25 (2014), 2110-2118.  doi: 10.1109/TNNLS.2014.2305443.  Google Scholar

[25]

S. Luo and D. Ye, Adaptive double event-triggered control for linear multi-agent systems with actuator faults, IEEE Transactions on Circuits and Systems I: Regular Papers, 66 (2019), 4829-4839.  doi: 10.1109/TCSI.2019.2932084.  Google Scholar

[26]

Y. QianL. Liu and G. Feng, Output consensus of heterogeneous linear multi-agent systems with adaptive event-triggered control, IEEE Transactions on Automatic Control, 64 (2018), 2606-2613.  doi: 10.1109/TAC.2018.2868997.  Google Scholar

[27]

W. Ren, Distributed attitude alignment in spacecraft formation flying, Internat. J. Adapt. Control Signal Process., 21 (2007), 95-113.  doi: 10.1002/acs.916.  Google Scholar

[28]

R. O. Saber and R. M. Murray, Consensus protocols for networks of dynamic agents, 2003 American Control Conference, 2 (2003), 951-956.  doi: 10.1109/ACC.2003.1239709.  Google Scholar

[29]

R. SakthivelA. ParivallalB. KaviarasanH. Lee and Y. Lim, Finite-time consensus of Markov jumping multi-agent systems with time-varying actuator faults and input saturation, ISA Transactions, 83 (2018), 89-99.  doi: 10.1016/j.isatra.2018.08.016.  Google Scholar

[30]

R. SakthivelR. SakthivelB. KaviarasanH. Lee and Y. Lim, Finite-time leaderless consensus of uncertain multi-agent systems against time-varying actuator faults, Neurocomputing, 325 (2019), 159-171.  doi: 10.1016/j.neucom.2018.10.020.  Google Scholar

[31]

Y. SuQ. Wang and C. Sun, Self-triggered consensus control for linear multi-agent systems with input saturation, IEEE/CAA Journal of Automatica Sinica, 7 (2020), 150-157.  doi: 10.1109/JAS.2019.1911837.  Google Scholar

[32]

Y. SuB. ChenC. LinH. Wang and S. Zhou, Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach, Information Sciences, 369 (2016), 748-764.  doi: 10.1016/j.ins.2016.06.010.  Google Scholar

[33]

D. Sumpter and S. Pratt, A modelling framework for understanding social insect foraging, Behavioral Ecology and Sociobiology, 53 (2003), 131-144.  doi: 10.1007/s00265-002-0549-0.  Google Scholar

[34]

X. TanJ. Cao and X. Li, Consensus of leader-following multiagent systems: A distributed event-triggered impulsive control strategy, IEEE Transactions on Cybernetics, 49 (2018), 792-801.  doi: 10.1109/TCYB.2017.2786474.  Google Scholar

[35]

A. WangX. Liao and T. Dong, Fractional-order follower observer design for tracking consensus in second-order leader multi-agent systems: Periodic sampled-based event-triggered control, Journal of the Franklin Institute, 355 (2018), 4618-4628.  doi: 10.1016/j.jfranklin.2018.01.036.  Google Scholar

[36]

W. Wang, H. Liang, Y. Pan and T. Li, Prescribed performance adaptive fuzzy containment control for nonlinear multi-agent systems using disturbance observer, IEEE Transactions on Cybernetics, 2020, 1–13. doi: 10.1109/TCYB.2020.2969499.  Google Scholar

[37]

X. WangS. LiX. Yu and J. Yang, Distributed active anti-disturbance consensus for leader-follower higher-order multi-agent systems with mismatched disturbances, IEEE Transactions on Automatic Control, 62 (2017), 5795-5801.  doi: 10.1109/TAC.2016.2638966.  Google Scholar

[38]

H. Wu and H. Su, Observer-based consensus for positive multiagent systems with directed topology and nonlinear control input, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49 (2018), 1459-1469.  doi: 10.1109/TSMC.2018.2852704.  Google Scholar

[39]

Y. WuZ. WangS. Ding and H. Zhang, Leader–follower consensus of multi-agent systems in directed networks with actuator faults, Neurocomputing, 275 (2018), 1177-1185.  doi: 10.1016/j.neucom.2017.09.066.  Google Scholar

[40]

G. Xie, L. Sun, T. Wen, X. Hei and F. Qian, Adaptive transition probability matrix-based parallel IMM algorithm, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 1–10. doi: 10.1109/TSMC.2019.2922305.  Google Scholar

[41]

D. Yao, H. Li, R. Lu and Y. Shi, Distributed sliding mode tracking control of second-order nonlinear multi-agent systems: An event-triggered approach, IEEE Transactions on Cybernetics, 2020, 1-11. doi: 10.1109/TCYB.2019.2963087.  Google Scholar

[42]

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Figure 1.  Topology of communication graph
Figure 2.  Output trajectories of followers and the leader
Figure 3.  The trajectories of tracking errors
Figure 4.  The trajectories of event-triggered controllers
Figure 5.  The trajectories of errors between disturbances and disturbance observers
Figure 6.  The trajectories of errors between disturbances and disturbance observers
Figure 7.  Triggering instants and inter-event intervals
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