Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

Xianggui Guo; Dongyu Zhang; Jianliang Wang; Choon Ki Ahn

doi:10.1109/JAS.2021.1004132

Volume 8 Issue 10

Oct. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(10): 1644-1656

X. G. Guo, D. Y. Zhang, J. L. Wang, and C. K. Ahn, "Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1644-1656, Oct. 2021. doi: 10.1109/JAS.2021.1004132

Citation:

X. G. Guo, D. Y. Zhang, J. L. Wang, and C. K. Ahn, "Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1644-1656, Oct. 2021. doi: 10.1109/JAS.2021.1004132

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Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

doi: 10.1109/JAS.2021.1004132

Xianggui Guo^{1, 2
,},
Dongyu Zhang^{1, 2
,},
Jianliang Wang^3
,,
Choon Ki Ahn^{4
,
,}

1.
School of Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
3.
Autonomous Intelligent Systems Department, Hangzhou Innovation Institute of Beihang University, Hangzhou 310051, China
4.
School of Electrical Engineering, Korea University, Seoul 1360701, Korea

Funds: This work was supported by the National Natural Science Foundation of China (61773056), the Scientific and Technological Innovation Foundation of Shunde Graduate School, University of Science and Technology Beijing (USTB) (BK19AE018), and the Fundamental Research Funds for the Central Universities of USTB (FRF-TP-20-09B, 230201606500061, FRF-DF-20-35, FRF-BD-19-002A). The work of J. L. Wang was supported by Zhejiang Natural Science Foundation (LD21F030001). The work of C. K. Ahn was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and Information and Communications Technology) (NRF-2020R1A2C1005449)

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Author Bio:
Xianggui Guo received the B.S. degree from College of Electrical Engineering, Northwest University for Nationalities, China, in 2005, and the M.S. degree from College of Electrical Engineering and Automation, Fuzhou University, China in 2008, the Ph.D. degree in control science and engineering from Northeastern University, China in 2012. From 2014 to 2016, he was a Postdoctoral Fellow at School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From July 2018 to August 2018, he was a Visiting Scholar with the Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada. Since 2019, he has been a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, China. His research interests include multi-agent systems, fuzzy systems, vehicular platoon control, and fault-tolerant Control

Dongyu Zhang received the B.S. degree from School of Instrument and Electronics, North University of China, Taiyuan, China in 2019. He is currently a master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, China. His research interest is multi-agent systems

Jianliang Wang (SM’00) received the B.E. degree in electrical engineering from Beijing Institute of Technology, China in 1982, the M.S. and Ph.D. degrees in electrical engineering from The Johns Hopkins University, USA in 1985 and 1988, respectively, specializing in nonlinear systems and control theory. From 1988 to 1990, he was a Lecturer with Beijing University of Aeronautics and Astronautics, China. From 1990 to 2019, he was an Associate Professor with the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. Since 2020, he has been a Professor with Hangzhou Innovation Institute of Beihang University, Hangzhou, China. His research interests include multi-agent systems, fault tolerant control, fault detection and identification, flight control systems, and vehicular platoon control

Choon Ki Ahn (M’06-SM’12) received the B.S. and M.S. degrees from the School of Electrical Engineering, Korea University, Seoul, Korea, in 2000 and 2002, respectively, the Ph.D. degree from the School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea, in 2006. He is currently a Crimson Professor of Excellence with the College of engineering and a Full Professor with the School of electrical engineering, Korea University, Seoul, Korea. His current research interests include control, estimation, fuzzy systems, neural networks, and nonlinear dynamics. He was the recipient of the Early Career Research Award and Excellent Research Achievement Award of Korea University in 2015 and 2016, respectively. He was awarded the Medal for “Top 100 Engineers” 2010 by IBC, Cambridge, UK. In 2016, he was ranked #1 in Electrical/Electronic Engineering and #2 in entire areas of engineering among Korean young professors based on paper quality. In 2017, he received the Presidential Young Scientist Award from the President of South Korea. In 2019–2021, he received the Research Excellence Award from Korea University (Top 3% Professor of Korea University in Research). He is a member of the IEEE Technical Committee on Cyber-Physical Systems; IEEE Systems, Man, and Cybernetics Society Technical Committee on Intelligent Learning in Control Systems; and IEEE SMCS Technical Committee on Soft Computing. He has been on the editorial board of leading international journals, including the IEEE Systems, Man, and Cybernetics Magazine; IEEE Transactions on Neural Networks and Learning Systems; IEEE Transactions on Fuzzy Systems; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions on Automation Science and Engineering; IEEE Transactions on Intelligent Transportation Systems; IEEE Transactions on Circuits and Systems I: Regular Papers; IEEE Systems Journal; Nonlinear Dynamics; Aerospace Science and Technology; Artificial Intelligence Review; and other flagship journals. He was the recipient of the Highly Cited Researcher Award in Engineering by Clarivate Analytics (formerly, Thomson Reuters)
Corresponding author: Choon Ki Ahn, e-mail: hironaka@korea.ac.kr
Received Date: 2021-03-12
Revised Date: 2021-04-07
Accepted Date: 2021-05-07

Available Online: 2021-05-24

Abstract

Abstract

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks over an undirected graph. A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements. Meanwhile, this control scheme can also provide more reasonable control signals when DoS attacks occur. To save network resources, an adaptive memory event-triggered mechanism (AMETM) is also proposed and Zeno behavior is excluded. It is worth mentioning that the AMETM’s updates do not require global information. Then, the observer and controller gains are obtained by using the linear matrix inequality (LMI) technique. Finally, simulation examples show the effectiveness of the proposed control scheme.

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References

[1]	J. J. Cui, Y. W. Liu, and A. Nallanathan, “Multi-agent reinforcement learning-based resource allocation for UAV networks,” IEEE Trans. Wireless Commun., vol. 19, no. 2, pp. 729–743, Feb. 2020. doi: 10.1109/TWC.2019.2935201
[2]	D. Chowdhury and H. K. Khalil, “Practical synchronization in networks of nonlinear heterogeneous agents with application to power systems,” IEEE Trans. Autom. Control, vol. 66, no. 1, pp. 184–198, Jan. 2021. doi: 10.1109/TAC.2020.2981084
[3]	Y. Kikuya, S. M. Dibaji, and H. Ishii, “Fault-tolerant clock synchronization over unreliable channels in wireless sensor networks,” IEEE Trans. Control Netw. Syst., vol. 5, no. 4, pp. 1551–1562, Dec. 2018. doi: 10.1109/TCNS.2017.2732169
[4]	M. B. Khalkhali, A. Vahedian, and H. S. Yazdi, “Multi-target state estimation using interactive kalman filter for multi-vehicle tracking,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1131–1144, Mar. 2020. doi: 10.1109/TITS.2019.2902664
[5]	L. W. An and G. H. Yang, “Decentralized adaptive fuzzy secure control for nonlinear uncertain interconnected systems against intermittent DoS attacks,” IEEE Trans. Cybern., vol. 49, no. 3, pp. 827–838, Mar. 2019. doi: 10.1109/TCYB.2017.2787740
[6]	C. Deng and C. Y. Wen, “Distributed resilient observer-based faulttolerant control for heterogeneous multiagent systems under actuator faults and DoS attacks,” IEEE Trans. Control Netw. Syst., vol. 7, no. 3, pp. 1308–1318, Sep. 2020. doi: 10.1109/TCNS.2020.2972601
[7]	Y. M. Wu and X. X. He, “Secure consensus control for multiagent systems with attacks and communication delays,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 136–142, Jan. 2017. doi: 10.1109/JAS.2016.7510010
[8]	D. Zhang, G. Feng, Y. Shi, and D. Srinivasan, “Physical safety and cyber security analysis of multi-agent systems: a survey of recent advances,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 319–333, Feb. 2021. doi: 10.1109/JAS.2021.1003820
[9]	W. L. He, Z. K. Mo, Q. -L. Han, and F. Qian, “Secure impulsive synchronization in Lipschitz-type multi-agent systems subject to deception attacks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1326–1334, Sep. 2020.
[10]	X. Huang and J. X. Dong, “Reliable leader-to-follower formation control of multiagent systems under communication quantization and attacks,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 50, no. 1, pp. 89–99, Jan. 2020. doi: 10.1109/TSMC.2019.2894946
[11]	G. Franze, F. Tedesco, and D. Famularo, “Resilience against replay attacks: A distributed model predictive control scheme for networked multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 628–640, Mar. 2021. doi: 10.1109/JAS.2020.1003542
[12]	C. J. Zhou, B. W. Hu, Y. Shi, Y. C. Tian, X. Li, and Y. Zhao, “A unified architectural approach for cyberattack-resilient industrial control systems,” Proc. IEEE, vol. 109, no. 4, pp. 517–541, Apr. 2021. doi: 10.1109/JPROC.2020.3034595
[13]	Y. Wan, G. H. Wen, X. H. Yu, and T. W. Huang, “Distributed consensus tracking of networked agent systems under Denial-of-Service attacks,” IEEE Trans. Syst., Man, Cybern., Syst., to be published, Jan. 2020.
[14]	E. Mousavinejad, F. W. Yang, Q. L. Han, X. H. Ge, and L. Vlacic, “Distributed cyber attacks detection and recovery mechanism for vehicle platooning,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 9, pp. 3821–3834, Sep. 2020. doi: 10.1109/TITS.2019.2934481
[15]	C. De Persis and P. Tesi, “Input-to-state stabilizing control under denialof-service,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 2930–2944, Nov. 2015. doi: 10.1109/TAC.2015.2416924
[16]	T. Y. Zhang and D. Ye, “Distributed event-triggered control for multiagent systems under intermittently random denial-of-service attacks,” Inf. Sci., vol. 542, pp. 380–390, Jan. 2021. doi: 10.1016/j.ins.2020.06.070
[17]	Z. Feng and G. Q. Hu, “Secure cooperative event-triggered control of linear multiagent systems under DoS attacks,” IEEE Trans. Control Syst. Technol., vol. 28, no. 3, pp. 741–752, May 2020. doi: 10.1109/TCST.2019.2892032
[18]	Y. Xu, M. Fang, Z. G. Wu, Y. J. Pan, M. Chadli, and T. W. Huang, “Input-based event-triggering consensus of multiagent systems under denial-of-service attacks,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 50, no. 4, pp. 1455–1464, Apr. 2020. doi: 10.1109/TSMC.2018.2875250
[19]	Y. Yang, Y. F. Li, D. Yue, Y. C. Tian, and X. H. Ding, “Distributed secure consensus control with event-triggering for multiagent systems under DoS attacks,” IEEE Trans. Cybern., vol. 51, no. 6, pp. 2916–2928, Jun. 2021. doi: 10.1109/TCYB.2020.2979342
[20]	L. Zhao and G. H. Yang, “Adaptive fault-tolerant control for nonlinear multi-agent systems with DoS attacks,” Inf. Sci., vol. 526, pp. 39–53, Jul. 2020. doi: 10.1016/j.ins.2020.03.083
[21]	J. L. Liu, T. T. Yin, D. Yue, H. R. Karimi, and J. D. Cao, “Event-based secure leader-following consensus control for multiagent systems with multiple cyber attacks,” IEEE Trans. Cybern., vol. 51, no. 1, pp. 162–173, Jan. 2021. doi: 10.1109/TCYB.2020.2970556
[22]	X. G. Guo, X. Fan, J. L. Wang, and J. H. Park, “Event-triggered switching-type fault detection and isolation for fuzzy control systems under DoS attacks,” IEEE Trans. Fuzzy Syst., to be published, Sep. 2020.
[23]	W. C. Zou, C. K. Ahn, and Z. R. Xiang, “Fuzzy-approximation-based distributed fault-tolerant consensus for heterogeneous switched nonlinear multiagent systems,” IEEE Trans. Fuzzy Syst., to be published, Jul. 2020.
[24]	X. G. Guo, J. L. Wang, and F. Liao, “Adaptive quantised H_∞ observer-based output feedback control for non-linear systems with input and output quantisation,” IET Control Theory Appl., vol. 11, no. 2, pp. 263–272, Jan. 2017. doi: 10.1049/iet-cta.2016.0988
[25]	S. L. Hu, D. Yue, Q. L. Han, X. P. Xie, X. L. Chen, and C. X. Dou, “Observer-based event-triggered control for networked linear systems subject to denial-of-service attacks,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 1952–1964, May 2020. doi: 10.1109/TCYB.2019.2903817
[26]	Z. Y. Wu, H. D. Mo, J. L. Xiong, and M. Xie, “Adaptive event-triggered observer-based output feedback $ {\cal{L}}_{\infty }$ load frequency control for networked power systems” IEEE Trans. Ind. Inf., vol. 16, no. 6, pp. 3952–3962, Jun. 2020. doi: 10.1109/TII.2019.2942637
[27]	Y. Wu, H. Liang, Y. Zhang, and C. K. Ahn, “Cooperative adaptive dynamic surface control for a class of high-order stochastic nonlinear multi-agent systems,” IEEE Trans. Cybern. to be published, May 2020.
[28]	X. G. Guo, J. L. Wang, F. Liao, and R. S. H. Teo, “Distributed adaptive sliding mode control strategy for vehicle-following systems with nonlinear acceleration uncertainties,” IEEE Trans. Veh. Technol., vol. 66, no. 2, pp. 981–991, Feb. 2017. doi: 10.1109/TVT.2016.2556938
[29]	I. Furtat, E. Fridman, and A. Fradkov, “Disturbance compensation with finite spectrum assignment for plants with input delay,” IEEE Trans. Autom. Control, vol. 63, no. 1, pp. 298–305, Jan. 2018. doi: 10.1109/TAC.2017.2732279
[30]	Y. Yuan, Z. D. Wang, and L. Guo, “Event-triggered strategy design for discrete-time nonlinear quadratic games with disturbance compensations: the noncooperative case,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 48, no. 11, pp. 1885–1896, Nov. 2018. doi: 10.1109/TSMC.2017.2704278
[31]	X. G. Guo, X. Fan, and C. K. Ahn, “Adaptive event-triggered fault detection for interval type-2 T-S fuzzy systems with sensor saturation,” IEEE Trans. Fuzzy Syst., to be published, May 2020.
[32]	W. Y. Xu, D. W. C. Ho, J. Zhong, and B. Chen, “Event/Self-triggered control for leader-following consensus over unreliable network with DoS attacks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 10, pp. 3137–3149, Oct. 2019. doi: 10.1109/TNNLS.2018.2890119
[33]	D. Ye, M. M. Chen, and H. J. Yang, “Distributed adaptive eventtriggered fault-tolerant consensus of multiagent systems with general linear dynamics,” IEEE Trans. Cybern., vol. 49, no. 3, pp. 757–767, Mar. 2019. doi: 10.1109/TCYB.2017.2782731
[34]	B. Cheng and Z. K. Li, “Fully distributed event-triggered protocols for linear multiagent networks,” IEEE Trans. Autom. Control, vol. 64, no. 4, pp. 1655–1662, Apr. 2019. doi: 10.1109/TAC.2018.2857723
[35]	S. L. Hu, D. Yue, X. P. Xie, X. L. Chen, and X. X. Yin, “Resilient event-triggered controller synthesis of networked control systems under periodic DoS jamming attacks,” IEEE Trans. Cybern., vol. 49, no. 12, pp. 4271–4281, Dec. 2019. doi: 10.1109/TCYB.2018.2861834
[36]	E. G. Tian, K. Y. Wang, X. Zhao, S. B. Shen, and J. L. Liu, “An improved memory-event-triggered control for networked control systems,” J. Franklin Inst., vol. 356, no. 13, pp. 7210–7223, Sep. 2019. doi: 10.1016/j.jfranklin.2019.06.041
[37]	K. Y. Wang, E. G. Tian, S. B. Shen, L. N. Wei, and J. L. Zhang, “Input-output finite-time stability for networked control systems with memory event-triggered scheme,” J. Franklin Inst., vol. 356, no. 15, pp. 8507–8520, Oct. 2019. doi: 10.1016/j.jfranklin.2019.08.020
[38]	C. L. Liu, L. Shan, Y. Y. Chen, and Y. Zhang, “Average-consensus filter of first-order multi-agent systems with disturbances,” IEEE Trans. Circuits Syst.,Ⅱ,Exp. Briefs, vol. 65, no. 11, pp. 1763–1767, Nov. 2018. doi: 10.1109/TCSII.2017.2762723
[39]	X. D. Wang, Z. Y. Fei, T. Wang, and L. Yang, “Dynamic event-triggered actuator fault estimation and accommodation for dynamical systems,” Inf. Sci., vol. 525, pp. 119–133, Jul. 2020. doi: 10.1016/j.ins.2020.03.016
[40]	B. H. Wang, W. S. Chen, J. C. Wang, B. Zhang, Z. Q. Zhang, and X. G. Qiu, “Accurate cooperative control for multiple leaders multiagent uncertain systems: a two-layer node-to-node communication framework,” IEEE Trans. Ind. Inf., vol. 14, no. 6, pp. 2395–2405, Jun. 2018. doi: 10.1109/TII.2017.2776316
[41]	Y. B. Gao, J. X. Liu, G. H. Sun, M. Liu, and L. G. Wu, “Fault deviation estimation and integral sliding mode control design for Lipschitz nonlinear systems,” Syst. Control Lett., vol. 123, pp. 8–15, Jan. 2019. doi: 10.1016/j.sysconle.2018.08.006
[42]	A. ur Rehman, M. Rehan, N. Iqbal, and C. K. Ahn, “LPV scheme for robust adaptive output feedback consensus of lipschitz multiagents using lipschitz nonlinear protocol,” IEEE Trans. Syst., Man, Cybern., Syst., to be published, Jan. 2020.
[43]	Y. Xu, M. Fang, P. Shi, and Z. G. Wu, “Event-based secure consensus of mutiagent systems against DoS attacks,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3468–3476, Aug. 2020. doi: 10.1109/TCYB.2019.2918402
[44]	R. N. Yang and W. X. Zheng, “Output-based event-triggered predictive control for networked control systems,” IEEE Trans. Ind. Electron., vol. 67, no. 12, pp. 10631–10640, Dec. 2020. doi: 10.1109/TIE.2019.2958303

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Taking into account the effects of DoS attacks and external disturbance, a state observer is designed to remove the assumption that the states of the agent are measurable. Meanwhile, by embedding a buffer in the state observer, the state of the previous moment can be stored; thus, the observer accuracy can be improved in the presence of DoS attacks, which can provide a more reasonable control signal.
A distribute adaptive memory event-triggered mechanism is designed, which does not need global information when updating, thus reducing the communication burden. The existence of the buffer allows us to dynamically adjust the number of trigger points to improve system performance and reduce the occurrence of error triggered events caused by sudden changes due to erroneous measurements.
Compensation mechanism for observer and controller: Different from the previous results that using disturbance observer based on state feedback control technique to resist disturbance, this method can effectively reduce the difficulty caused by unmeasurable state and improve observer performance. In addition, the consensus performance of the system can also be improved.

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

doi: 10.1109/JAS.2021.1004132

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