Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (05): 75-82.doi: 10.12052/gdutxb.220065

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Distributed Model-Free Adaptive Control for Nonlinear Multi-Agent Systems with FDI Attacks

Qu Shen1,2, Che Wei-wei1,2   

  1. 1. School of Automation, Qingdao University, Qingdao 266071, China;
    2. Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China
  • Received:2022-03-20 Published:2022-07-18

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Abstract: The distributed model-free adaptive control (DMFAC) problem for single-input single-output nonlinear multi-agent systems (MASs) with false data injection attacks is studied. A new distributed dynamic linearization method is proposed to obtain an equivalent distributed compact form dynamic linearization data model for nonlinear MASs. Unlike the existing DMFAC results of MASs that the network topology is used in the controller design, the distributed model-free adaptive controller designed in this research uses the input/output data and the controller parameter does not depend on the eigenvalues of the Laplacian matrix. Simulation examples verify that the proposed distributed model-free adaptive control algorithm can acheive bounded consensus control of MASs in the mean square sense. The algorithm can ensure that the multi-agent system achieve the consensus control objective when it is under attacks.

Key words: distributed compact form dynamic linearization, distributed model-free adaptive control, multi-agent systems, FDI (false data injection) attacks

CLC Number: 

  • TP273
[1] TANG Y, XING X, KARIMI H R, et al. Tracking control of networked multi-agent systems under new characterizations of impulses and its applications in robotic systems [J]. IEEE Transactions on Industrial Electronics, 2015, 63(2): 1299-1307.
[2] DONG X, HUA Y, ZHOU Y, et al. Theory and experiment on formation-containment control of multiple multirotor unmanned aerial vehicle systems [J]. IEEE Transactions on Automation Science and Engineering, 2018, 16(1): 229-240.
[3] OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays [J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.
[4] HE W, XU B, HAN Q L, et al. Adaptive consensus control of linear multiagent systems with dynamic event-triggered strategies [J]. IEEE Transactions on Cybernetics, 2019, 50(7): 2996-3008.
[5] DENG C, CHE W W, WU Z G. A dynamic periodic event-triggered approach to consensus of heterogeneous linear multiagent systems with time-varying communication delays [J]. IEEE Transactions on Cybernetics, 2021, 51(4): 1812-1821.
[6] DENG C, CHE W W. Fault-tolerant fuzzy formation control for a class of nonlinear multiagent systems under directed and switching topology [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(9): 5456-5465.
[7] JIN X Z, CHE W W, WU Z G, et al. Adaptive consensus and circuital implementation of a class of faulty multiagent systems [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(1): 226-237.
[8] JIANG C, CHEN Z, GUO Y. Multi-robot formation control: a comparison between model-based and learning-based methods [J]. Journal of Control and Decision, 2020, 7(1): 90-108.
[9] DENG C, CHE W W, SHI P. Cooperative fault-tolerant output regulation for multiagent systems by distributed learning control approach [J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(11): 4831-4841.
[10] YANG H, YE D. Observer-based fixed-time secure tracking consensus for networked high-order multiagent systems against dos attacks [J]. IEEE Transactions on Cybernetics, 2022, 52(4): 2018-2031.
[11] XU W, HO D W, LI L, et al. Event-triggered schemes on leader-following consensus of general linear multiagent systems under different topologies [J]. IEEE Transactions on Cybernetics, 2015, 47(1): 212-223.
[12] YANG Y, LI Y, YUE D, et al. Distributed secure consensus control with event-triggering for multiagent systems under dos attacks [J]. IEEE Transactions on Cybernetics, 2021, 51(6): 2916-2928.
[13] AMINI A, ASIF A, MOHAMMADI A. Formation-containment control using dynamic event-triggering mechanism for multi-agent systems [J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(5): 1235-1248.
[14] ZUO S, YUE D. Resilient output formation containment of heterogeneous multigroup systems against unbounded attacks [J]. IEEE Transactions on Cybernetics, 2022, 52(3): 1902-1910.
[15] DENG C, YANG G H. Distributed adaptive fault-tolerant containment control for a class of multi-agent systems with non-identical matching non-linear functions [J]. IET Control Theory & Applications, 2016, 10(3): 273-281.
[16] CHEN W, DING D, GE X, et al. $ {{{{H}}}}_{\infty}$ containment control of multiagent systems under event-triggered communication scheduling: the finite-horizon case [J]. IEEE Transactions on Cybernetics, 2018, 50(4): 1372-1382.
[17] WANG D, WANG Z, WANG Z, et al. Design of hybrid event-triggered containment controllers for homogeneous and heterogeneous multiagent systems [J]. IEEE Transactions on Cybernetics, 2021, 51(10): 4885-4896.
[18] WANG W, LONG J, WEN C, et al. Recent advances in distributed adaptive consensus control of uncertain nonlinear multi-agent systems [J]. Journal of Control and Decision, 2020, 7(1): 44-63.
[19] FATTAHI M, AFSHAR A. Controller-based observer design for distributed consensus of multi-agent systems with fault and delay [J]. Journal of Control and Decision, 2019, 6(4): 217-235.
[20] ZHANG H, YUE D, DOU C, et al. Data-driven distributed optimal consensus control for unknown multiagent systems with input-delay [J]. IEEE Transactions on Cybernetics, 2018, 49(6): 2095-2105.
[21] JIANG H, HE H. Data-driven distributed output consensus control for partially observable multiagent systems [J]. IEEE Transactions on Cybernetics, 2018, 49(3): 848-858.
[22] WANG S, DUAN J, SHI D, et al. A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning [J]. IEEE Transactions on Power Systems, 2020, 35(6): 4644-4654.
[23] HOU Z, JIN S. Model free adaptive control: theory and applications[M]. Boca Raton: CRC press, 2019.
[24] HOU Z, CHI R, GAO H. An overview of dynamic-linearization-based data-driven control and applications [J]. IEEE Transactions on Industrial Electronics, 2016, 64(5): 4076-4090.
[25] HOU Z, JIN S. A novel data-driven control approach for a class of discrete-time nonlinear systems [J]. IEEE Transactions on Control Systems Technology, 2010, 19(6): 1549-1558.
[26] HOU Z, JIN S. Data-driven model-free adaptive control for a class of mimo nonlinear discrete-time systems [J]. IEEE Transactions on Neural Networks, 2011, 22(12): 2173-2188.
[27] HOU Z, XIONG S. On model-free adaptive control and its stability analysis [J]. IEEE Transactions on Automatic Control, 2019, 64(11): 4555-4569.
[28] BU X, HOU Z, ZHANG H. Data-driven multiagent systems consensus tracking using model free adaptive control [J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29(5): 1514-1524.
[29] SONG W, FENG J, SUN S. Data-based output tracking formation control for heterogeneous mimo multiagent systems under switching topologies [J]. Neurocomputing, 2021, 422: 322-331.
[30] BU X, YU Q, HOU Z, et al. Model free adaptive iterative learning consensus tracking control for a class of nonlinear multiagent systems [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(4): 677-686.
[31] BU X, ZHU P, HOU Z, et al. Finite-time consensus for linear multi-agent systems using data-driven terminal ilc [J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019, 67(10): 2029-2033.
[32] BU X, CUI L, HOU Z, et al. Formation control for a class of nonlinear multiagent systems using model-free adaptive iterative learning [J]. International Journal of Robust and Nonlinear Control, 2018, 28(4): 1402-1412.
[33] REN Y, HOU Z. Robust model-free adaptive iterative learning formation for unknown heterogeneous non-linear multi-agent systems [J]. IET Control Theory & Applications, 2019, 14(4): 654-663.
[34] ZHANG J, CHAI S, ZHANG B, et al. Relay cooperative tracking control of networked nonlinear multi-agent systems with communication delays: a data-driven method [J]. Neurocomputing, 2019, 363: 9-16.
[35] WANG Y, LI H, QIU X, et al. Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control [J]. Applied Mathematics and Computation, 2020, 365: 124701.
[36] FENG J, SONG W, ZHANG H, et al. Data-driven robust iterative learning consensus tracking control for mimo multiagent systems under fixed and iteration-switching topologies [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(2): 1331-1344.
[37] DENG C, WEN C. Distributed resilient observer-based fault-tolerant control for heterogeneous multiagent systems under actuator faults and dos attacks [J]. IEEE Transactions on Control of Network Systems, 2020, 7(3): 1308-1318.
[38] FENG Z, HU G. Secure cooperative event-triggered control of linear multiagent systems under dos attacks [J]. IEEE Transactions on Control Systems Technology, 2019, 28(3): 741-752.
[39] HE W, MO Z, HAN Q L, et al. Secure impulsive synchronization in lipschitz-type multi-agent systems subject to deception attacks [J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(5): 1326-1334.
[40] HE W, GAO X, ZHONG W, et al. Secure impulsive synchronization control of multi-agent systems under deception attacks [J]. Information Sciences, 2018, 459: 354-368.
[41] MA Y S, CHE W W, DENG C, et al. Distributed model-free adaptive control for learning nonlinear mass under dos attacks[J/OL]. IEEE Transactions on Neural Networks and Learning Systems, 2021: 1-10. (2021-08-24) [2022-03-20]. https://doi.org/10.1109/TNNLS.2021.3104978.
[42] XU D, JIANG B, SHI P. A novel model-free adaptive control design for multivariable industrial processes [J]. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6391-6398.
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