Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

Xin Gong; Michael V. Basin; Zhiguang Feng; Tingwen Huang; Yukang Cui

doi:10.1109/JAS.2023.123339

Volume 10 Issue 4

Apr. 2023

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 11.8, Top 4% (SCI Q1)

CiteScore: 17.6, Top 3% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(4): 969-984

X. Gong, M. V. Basin, Z. G. Feng, T. W. Huang, and Y. K. Cui, “Resilient time-varying formation-tracking of multi-UAV systems against composite attacks: A two-layered framework,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 969–984, Apr. 2023. doi: 10.1109/JAS.2023.123339

Citation:

X. Gong, M. V. Basin, Z. G. Feng, T. W. Huang, and Y. K. Cui, “Resilient time-varying formation-tracking of multi-UAV systems against composite attacks: A two-layered framework,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 969–984, Apr. 2023. doi: 10.1109/JAS.2023.123339

Citation:

X. Gong, M. V. Basin, Z. G. Feng, T. W. Huang, and Y. K. Cui, “Resilient time-varying formation-tracking of multi-UAV systems against composite attacks: A two-layered framework,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 969–984, Apr. 2023. doi: 10.1109/JAS.2023.123339

PDF( 3258 KB)

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

doi: 10.1109/JAS.2023.123339

1.
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, the School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, and the Department of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China
2.
School of Physical and Mathematical Sciences, the Autonomous University of Nuevo Leon, Naevo Leon 66455, Mexico, and also with the Ningbo Institute of Intelligent Equipment Technology, Ningbo 315299, China
3.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
4.
Texas A&M University at Qatar, Doha 23874, Qatar
5.
Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, the College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, and also with the Peng Cheng Laboratory, Shenzhen 518000, China

Funds: This work was supported in part by the National Natural Science Foundation of China (61903258), Guangdong Basic and Applied Basic Research Foundation (2022A1515010234), the Project of Department of Education of Guangdong Province (2022KTSCX105), and Qatar National Research Fund (NPRP12C-0814-190012)

More Information

Author Bio:
Xin Gong (Member, IEEE) received the B.Sc. degree in mechanical engineering from Wuhan University in 2015, the M.Sc. degree in mechanical engineering from Shanghai Jiao Tong University in 2018, and the Ph.D. degree in control engineering from The University of Hong Kong, China in 2022. In 2020, he served as a Research Associate with the College of Aeronautics and Engineering, Kent State University, USA. He is currently a Research Associate Professor at the School of Cyber Science and Engineering, Southeast University.His research interests include fast and resilient control of multi-agent systems, distributed optimization, game theory and their applications in UAV swarms

Michael V. Basin (Senior Member, IEEE) received the Ph.D. degree in physical and mathematical sciences with major in automatic control and system analysis from the Moscow Aviation University, Russia in 1992. He is currently Full Professor with the Autonomous University of Nuevo Leon, Rassia, and the Ningbo Institute of Industrial Equipment Technology. His research interests include optimal filtering and control problems, stochastic systems, time-delay systems, identification, sliding mode control and variable structure systems, applications to mechatronic and transportation systems. Starting from 1992, Prof. Basin published more than 400 research papers in international referred journals and conference proceedings. He is the author of the monograph “New Trends in Optimal Filtering and Control for Polynomial and Time-Delay Systems,” published by Springer. His works are cited more than 6500 times (h index = 44). He has supervised 15 doctoral and nine master’s theses. He was awarded a title of Highly Cited Researcher by Thomson Reuters, the publisher of Science Citation Index, in 2009 and listed in “100 000 Leading Scientists in the World” He has served as the Editor-in-Chief and serves as the Co-Editor-in-Chief of Journal of The Franklin Institute, the Senior Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of Automatica, IEEE Transactions on Systems, Man and Cybernetics: Systems, IET-Control Theory and Applications, International Journal of Systems Science, and Neural Networks. He has been honored as a Fellow of Prominent Talent (Qian Ren) Program of Zhejiang Province, China. He is a Regular Member of the Mexican Academy of Sciences

Zhiguang Feng (Member, IEEE) received the B.S. degree in automation from Qufu Normal University in 2006, the M.S. degree in control Science and Engineering from Harbin Institute of Technology in 2009, and the Ph.D. degree in the Department of Mechanical Engineering, The University of Hong Kong, China in 2013. He was a Research Associate in the Department of Mechanical Engineering, University of Hong Kong, China, from Oct. 2013 to Feb. 2014. From Mar. 2014 to Apr. 2015, he was a Visiting Fellow at University of Western Sydney, Australia. He was appointed with Victoria University in Australia as Postdoctoral Research Fellow from Oct. 2015 to Mar. 2017. Since 2017, he has been a full Professor at the College of Intelligent Systems Science and Engineering, Harbin Engineering University. In 2019, he was a Vice-Chancellor’s Postdoctoral Research Fellowship recipient at University of Wollongong, Australia. His research interests include singular systems, time-delay systems, robust control, dissipative control, and reachable set estimation

Tingwen Huang (Fellow, IEEE) received the B.S. degree in mathematics from Southwest Normal University (now Southwest University) in 1990, the M.S. degree in applied mathematics from Sichuan University 1993, and the Ph.D. degree in applied mathematics from College Station Texas A&M University, USA in 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then, he joined Texas A&M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, then he was promoted to Professor in 2013. His research interests include computational intelligence, smart grid, dynamical systems, optimization and control

Yukang Cui (Member, IEEE) received the B.Eng. degree in automation from the Harbin Institute of Technology in July 2012 and the Ph.D. degree in mechanical engineering from the University of Hong Kong, China in March 2017. During 2017, he was a Research Associate at the Department of Mechanical Engineering, the University of Hong Kong, China. Since 2018, he has been with the College of Mechatronics and Control Engineering, Shenzhen University, where he is currently an Associate Professor. His current research interests include singular time-delay systems, nonlinear control systems, networked control systems, multi-agent systems and their application in quadrotor formation
Corresponding author: Yukang Cui, e-mail: cuiyukang@gmail.com
Received Date: 2022-10-12
Revised Date: 2022-11-11
Accepted Date: 2022-12-03

Available Online: 2023-02-21

Abstract

Abstract

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks, including denial-of-services (DoS) attacks, false-data injection attacks, camouflage attacks, and actuation attacks (AAs). Inspired by the concept of digital twin, a new two-layered protocol equipped with a safe and private twin layer (TL) is proposed, which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer. First, a topology-repairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme, which saves communication resources considerably. The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated. Second, a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed. Moreover, this novel adaptive controller can achieve uniformly ultimately bounded convergence, whose error bound can be given explicitly. The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.
- Composite attacks,
- multi-UAV systems,
- resilient control,
- time-varying formation-tracking

FullText(HTML)

References(40)

References

[1]	I. Bayezit and B. Fidan, “Distributed cohesive motion control of flight vehicle formations,” IEEE Trans. Industrial Electronics, vol. 60, no. 12, pp. 5763–5772, 2012.
[2]	X. Dong, B. Yu, Z. Shi, and Y. Zhong, “Time-varying formation control for unmanned aerial vehicles: Theories and applications,” IEEE Trans. Control Systems Technology, vol. 23, no. 1, pp. 340–348, 2015. doi: 10.1109/TCST.2014.2314460
[3]	X. Dong, Y. Zhou, Z. Ren, and Y. Zhong, “Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying,” IEEE Trans. Industrial Electronics, vol. 64, no. 6, pp. 5014–5024, 2016.
[4]	X. Dong, Y. Zhou, Z. Ren, and Y. Zhong, “Time-varying formation control for unmanned aerial vehicles with switching interaction topologies,” Control Engineering Practice, vol. 46, pp. 26–36, 2016. doi: 10.1016/j.conengprac.2015.10.001
[5]	X. Dong, Y. Li, C. Lu, G. Hu, Q. Li, and Z. Ren, “Time-varying formation tracking for UAV swarm systems with switching directed topologies,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3674–3685, 2018.
[6]	L. Huang, M. Zhou, K. Hao, and E. Hou, “A survey of multi-robot regular and adversarial patrolling,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 894–903, 2019. doi: 10.1109/JAS.2019.1911537
[7]	H. Liu, Q. Chen, N. Pan, Y. Sun, Y. An, and D. Pan, “UAV stocktaking task-planning for industrial warehouses based on improved hybrid differential evolution algorithm,” IEEE Trans. Industrial Informatics, vol. 18, no. 1, pp. 582–591, 2021.
[8]	Z. Feng, G. Wen, and G. Hu, “Distributed secure coordinated control for multiagent systems under strategic attacks,” IEEE Trans. Cybernetics, vol. 47, no. 5, pp. 1273–1284, 2017. doi: 10.1109/TCYB.2016.2544062
[9]	D. Zhang, L. Liu, and G. Feng, “Consensus of heterogeneous linear multiagent systems subject to aperiodic sampled-data and DoS attack,” IEEE Trans. Cybernetics, vol. 49, no. 4, pp. 1501–1511, 2019. doi: 10.1109/TCYB.2018.2806387
[10]	Z. Feng and G. Hu, “Distributed secure average consensus for linear multi-agent systems under DoS attacks,” in Proc. American Control Conf., 2017, pp. 2261–2266.
[11]	Z. Feng and G. Hu, “Distributed secure leader-following consensus of multi-agent systems under DoS attacks and directed topology,” in Proc. IEEE Int. Conf. Information and Automation, 2017, pp. 73–79.
[12]	X. Guo, D. Zhang, J. 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, 2021. doi: 10.1109/JAS.2021.1004132
[13]	Y. Yang, Y. Li, D. Yue, Y.-C. Tian, and X. Ding, “Distributed secure consensus control with event-triggering for multiagent systems under DoS attacks,” IEEE Trans. Cybernetics, vol. 51, no. 6, pp. 2916–2928, 2020.
[14]	R. Langner, “StuxNet: Dissecting a cyberwarfare weapon,” IEEE Security Privacy, vol. 9, no. 3, pp. 49–51, 2011. doi: 10.1109/MSP.2011.67
[15]	Y. Zhao, Z. Chen, C. Zhou, Y.-C. Tian, and Y. Qin, “Passivity-based robust control against quantified false data injection attacks in cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1440–1450, 2021. doi: 10.1109/JAS.2021.1004012
[16]	S. Zuo, F. L. Lewis, and A. Davoudi, “Resilient output containment of heterogeneous cooperative and adversarial multigroup systems,” IEEE Trans. Automatic Control, vol. 65, no. 7, pp. 3104–3111, 2019.
[17]	G. De La Torre, T. Yucelen, and J. D. Peterson, “Resilient networked multiagent systems: A distributed adaptive control approachy,” in Proc. 53rd IEEE Conf. Decision and Control, 2014, pp. 5367–5372.
[18]	C. Xie and G. Yang, “Decentralized adaptive fault-tolerant control for large-scale systems with external disturbances and actuator faults,” Automatica, vol. 85, pp. 83–90, 2017. doi: 10.1016/j.automatica.2017.07.037
[19]	Y. Wu, J. Liu, Z. Wang, and Z. Ju, “Distributed resilient tracking of multiagent systems under actuator and sensor faults,” IEEE Trans. Cybernetics, 2021. DOI: 10.1109/TCYB.2021.3132380
[20]	S. Zuo, T. Altun, F. L. Lewis, and A. Davoudi, “Distributed resilient secondary control of DC microgrids against unbounded attacks,” IEEE Trans. Smart Grid, vol. 11, no. 5, pp. 3850–3859, 2020. doi: 10.1109/TSG.2020.2992118
[21]	K. Manandhar, X. Cao, F. Hu, and Y. Liu, “Detection of faults and attacks including false data injection attack in smart grid using Kalman filter,” IEEE Trans. Control of Network Systems, vol. 1, no. 4, pp. 370–379, 2014. doi: 10.1109/TCNS.2014.2357531
[22]	A. Gusrialdi, Z. Qu, and M. A. Simaan, “Competitive interaction design of cooperative systems against attacks,” IEEE Trans. Automatic Control, vol. 63, no. 9, pp. 3159–3166, 2018. doi: 10.1109/TAC.2018.2793164
[23]	J. Zhou, Y. Lv, G. Wen, and X. Yu, “Resilient consensus of multiagent systems under malicious attacks: Appointed-time observer-based approach,” IEEE Trans. Cybernetics, vol. 52, no. 10, pp. 10 187–10 199, 2022. doi: 10.1109/TCYB.2021.3058094
[24]	S. Xiao and J. Dong, “Distributed fault-tolerant containment control for nonlinear multi-agent systems under directed network topology via hierarchical approach,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 806–816, 2021. doi: 10.1109/JAS.2021.1003928
[25]	Q. Zhu and Z. Xu, Cross-Layer Design for Secure and Resilient Cyber-Physical Systems: A Decision and Game Theoretic Approach. Berlin, Germany: Springer Nature, 2020, vol. 81.
[26]	Y. Fan, L. Liu, G. Feng, and Y. Wang, “Self-triggered consensus for multi-agent systems with Zeno-free triggers,” IEEE Trans. Automatic Control, vol. 60, no. 10, pp. 2779–2784, 2015. doi: 10.1109/TAC.2015.2405294
[27]	C. Gehrmann and M. Gunnarsson, “A digital twin based industrial automation and control system security architecture,” IEEE Trans. Industrial Informatics, vol. 16, no. 1, pp. 669–680, 2019.
[28]	H. K. Khalil and J. W. Grizzle, Nonlinear Systems. Ssddle River, USA: Prentice Hall Upper, 2002.
[29]	D. S. Bernstein, Matrix Mathematics: Theory, Facts, and Formulas. NJ, USA: Princeton University Press, 2009.
[30]	X. Gong, Y. Cui, J. Shen, Z. Shu, and T. Huang, “Distributed prescribed-time interval bipartite consensus of multi-agent systems on directed graphs: Theory and experiment,” IEEE Trans. Network Science Engineering, vol. 8, no. 1, pp. 613–624, 2020.
[31]	X. Chen, Y. Wang, and S. Hu, “Event-based robust stabilization of uncertain networked control systems under quantization and denial-of-service attacks,” Information Sciences, vol. 459, pp. 369–386, 2018. doi: 10.1016/j.ins.2018.05.019
[32]	J. Zhang and G. Yang, “Fault-tolerant output-constrained control of unknown Euler-Lagrange systems with prescribed tracking accuracy,” Automatica, vol. 111, p. 108606, 2020. doi: 10.1016/j.automatica.2019.108606
[33]	J. Zhang and G. Yang, “Robust adaptive fault-tolerant control for a class of unknown nonlinear systems,” IEEE Trans. Industrial Electronics, vol. 64, no. 1, pp. 585–594, 2016.
[34]	W. Liu and J. Huang, “Leader-following consensus for linear multiagent systems via asynchronous sampled-data control,” IEEE Trans. Automatic Control, vol. 65, no. 7, pp. 3215–3222, 2019.
[35]	W. E. Roth, “The equations AX – YB = C and AX – XB = C in matrices,” Proc. American Mathematical Society, vol. 3, no. 3, pp. 392–396, 1952.
[36]	C. Murguia, F. Farokhi, and I. Shames, “Secure and private implementation of dynamic controllers using semihomomorphic encryption,” IEEE Trans. Automatic Control, vol. 65, no. 9, pp. 3950–3957, 2020. doi: 10.1109/TAC.2020.2992445
[37]	G. Zhao and C. Hua, “Leaderless and leader-following bipartite consensus of multiagent systems with sampled and delayed information,” IEEE Trans. Neural Networks Learning Systems, 2021. DOI: 10.1109/TNNLS.2021.3106015
[38]	X. Deng, F. Long, B. Li, D. Cao, and Y. Pan, “An influence model based on heterogeneous online social network for influence maximization,” IEEE Trans. Network Science and Engineering, vol. 7, no. 2, pp. 737–749, 2019.
[39]	X. Zhang, J. Wu, C. Duan, M. T. Emmerich, and T. Bäck, “Towards robustness optimization of complex networks based on redundancy backup,” in Proc. IEEE Congr. Evolutionary Computation, 2015, pp. 2820–2826.
[40]	D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in Proc. IEEE Int. Conf. Robotics and Automation, 2011, pp. 2520–2525.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(15) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (631) PDF downloads(125)

Highlights

Inspired by the recently sprung-up digital twin technology, a two-layered resilient control scheme is designed, which introduces a Twin Layer (TL) apart from the conventional Cyber-Physical Layer (CPL). Note that the TL has higher security and higher privacy than the CPL. Consequently, this TL can effectively suppress most attacks, such as camouflage attacks and false-data injection ones. Moreover, owing to the existence of TL, the resilient control scheme is decoupled into the defense against Denial-of-Services (DoS) attacks on the TL and the defense against potentially unbounded actuation attacks (AAs) on the CPL
The resilient design of the TL against DoS attacks is investigated. In order to optimize the sampling sequence and alleviate the communication load among agents on the TL, we employ a Zeno-free event-triggered protocol. This protocol employs data encrypted by a private matrix such that the distributed safe and private estimation of the well-tuned state can be achieved asymptotically. We propose a topology-repairing strategy for the TL, which further improves the network connectivity under DoS attacks
A novel decentralized adaptive controller against unbounded AAs is proposed, which provides the UUB convergence and chattering-relief performance. Furthermore, the error bound of the UUB performance is given explicitly
A resilient formation-tracking experiment on a swarm of UAVs is conducted, which verifies the effectiveness and practicability of the designed two-layered control scheme

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

doi: 10.1109/JAS.2023.123339

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content