Abstract
Resilient control and cyber security are of great importance for multiagent systems (MASs) due to the vulnerability during their coordination through information interaction among agents. Unexpected cyber-attacks on a single agent can spread quickly and can affect critically the safety and the performance of the system. This paper proposes a distributed adaptive resilient neural network (NN)-based control procedure to guarantee its stability and to make the agents to follow the leader's profile when there exist cyber-attacks and external disturbances in the system. Firstly, an adaptive neural network is designed to estimate the nonlinear part of the MAS. Then, a variable structure super twisting approach is proposed in which the adaptive weights of the NN, and a virtual disturbance are designed adaptively via updating laws. Moreover, stability criteria and control objectives are investigated through Lyapunov theorem, which leads to a novel scheme that can handle nonlinearity, cyber-attacks, and external disturbances without requirement of designing different controllers in an extra algorithm and considering any limiting predefined condition such as Lipschitz on the nonlinear function. As an application, an adaptive cruise control of a platoon of connected automated vehicles is considered to scrutinize the proposed procedure in the absence and the presence of cyber-attacks, which verify the theoretical results.
Similar content being viewed by others
Data availability
Enquiries about data availability should be directed to the authors.
References
Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 22(7), 4579–4587 (2021). https://doi.org/10.1109/TITS.2020.3017183
Ly, B., Ly, R.: Cybersecurity in unmanned aerial vehicles (UAVs). J. Cyber Secur. Technol. 5(2), 120–137 (2021). https://doi.org/10.1080/23742917.2020.1846307
Guo, H., Liu, J., Dai, Q., Chen, H., Wang, Y., Zhao, W.: A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet Things J. 7(5), 3861–3871 (2020). https://doi.org/10.1109/JIOT.2020.2973977
Wang, Q., Jin, S., Hou, Z.: Data-driven event-triggered cooperative control for multiple subway trains with switching topologies. IEEE Trans. Intell. Transp. Syst. (2021). https://doi.org/10.1109/TITS.2021.3131997
Wang, Y., Bian, N., Zhang, L., Huang, Y., Chen, H.: Resilient path-following control of autonomous vehicles subject to intermittent denial-of-service attacks. IET Intel. Transp. Syst. 15(12), 1508–1521 (2021). https://doi.org/10.1049/itr2.12114
Farivar, F., Haghighi, M.S., Barchinezhad, S., Jolfaei, A.: Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control. In: Proceedings of the IEEE International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers Inc., pp. 1143–1148. (2019). Doi: https://doi.org/10.1109/ICIT.2019.8755007.
Zhang, D., Feng, G., Shi, Y., Srinivasan, D.: Physical safety and cyber security analysis of multi-agent systems: a survey of recent advances. IEEE/CAA J. Automatica Sinica 8(2), 319–333 (2021). https://doi.org/10.1109/JAS.2021.1003820
Chen, C., et al.: Resilient adaptive and H ∞ controls of multi-agent systems under sensor and actuator faults. Automatica 102, 19–26 (2019). https://doi.org/10.1016/j.automatica.2018.12.024
Sun, Y., Shi, P., Lim, C.C.: Event-triggered adaptive leaderless consensus control for nonlinear multi-agent systems with unknown backlash-like hysteresis. Int. J. Robust Nonlinear Control 31(15), 7409–7424 (2021). https://doi.org/10.1002/rnc.5692
Zhang, Y., Liang, H., Ma, H., Zhou, Q., Yu, Z.: Distributed adaptive consensus tracking control for nonlinear multi-agent systems with state constraints. Appl. Math. Comput. 326, 16–32 (2018). https://doi.org/10.1016/j.amc.2017.12.038
Yang, F., Gu, Z., Yan, S.: Switched event-based control for nonlinear cyber-physical systems under deception attacks. Nonlinear Dyn. 106(3), 2245–2257 (2021). https://doi.org/10.1007/s11071-021-06825-1
Yuan, S., Yu, C., Sun, J.: Adaptive event-triggered consensus control of linear multi-agent systems with cyber attacks. Neurocomputing 442, 1–9 (2021). https://doi.org/10.1016/j.neucom.2021.02.040
Meng, M., Xiao, G., Li, B.: Adaptive consensus for heterogeneous multi-agent systems under sensor and actuator attacks. Automatica 122, 109242 (2020). https://doi.org/10.1016/j.automatica.2020.109242
Adeli, M., Hajatipour, M., Yazdanpanah, M.J., Hashemi-Dezaki, H., Shafieirad, M.: Optimized cyber-attack detection method of power systems using sliding mode observer. Electr. Power Syst. Res. 205, 107745 (2022). https://doi.org/10.1016/j.epsr.2021.107745
Petrillo, A., Pescape, A., Santini, S.: A secure adaptive control for cooperative driving of autonomous connected vehicles in the presence of heterogeneous communication delays and cyberattacks. IEEE Trans. Cybern. 51(3), 1134–1149 (2021). https://doi.org/10.1109/TCYB.2019.2962601
Rezaee, H., Parisini, T., Polycarpou, M.M.: Resiliency in dynamic leader–follower multiagent systems. Automatica 125, 109384 (2021). https://doi.org/10.1016/j.automatica.2020.109384
He, W., Xu, W., Ge, X., Han, Q.L., Du, W., Qian, F.: Secure control of multiagent systems against malicious attacks: a brief survey. IEEE Trans. Industr. Inform. 18(6), 3595–3608 (2022). https://doi.org/10.1109/TII.2021.3126644
Ren, C.E., Fu, Q., Zhang, J., Zhao, J.: Adaptive event-triggered control for nonlinear multi-agent systems with unknown control directions and actuator failures. Nonlinear Dyn. 105(2), 1657–1672 (2021). https://doi.org/10.1007/s11071-021-06684-w
Guo, S., You, R., Ahn, C.K.: Adaptive consensus for multi-agent systems with switched nonlinear dynamics and switching directed topologies. Nonlinear Dyn. (2022). https://doi.org/10.1007/s11071-022-07895-5
Zhang, X., Chen, S., Zhang, J.X.: Adaptive sliding mode consensus control based on neural network for singular fractional order multi-agent systems. Appl. Math. Comput. 434, 127442 (2022). https://doi.org/10.1016/j.amc.2022.127442
Hu, J., Bhowmick, P., Arvin, F., Lanzon, A., Lennox, B.: Cooperative control of heterogeneous connected vehicle platoons: an adaptive leader-following approach. IEEE Robot. Autom. Lett. 5(2), 977–984 (2020). https://doi.org/10.1109/LRA.2020.2966412
Bian, Y., Li, S.E., Ren, W., Wang, J., Li, K., Liu, H.X.: Cooperation of multiple connected vehicles at unsignalized intersections: distributed observation, optimization, and control. IEEE Trans. Industr. Electron. 67(12), 10744–10754 (2020). https://doi.org/10.1109/TIE.2019.2960757
Tan, L., Li, C., Wang, X., Huang, T.: Neural network-based adaptive synchronization for second-order nonlinear multiagent systems with unknown disturbance. Chaos (2022). https://doi.org/10.1063/5.0068958
Jiang, Y., Wang, F., Liu, Z., Chen, Z.: Composite learning adaptive tracking control for full-state constrained multiagent systems without using the feasibility condition. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3190286
Mitchell, R., Chen, I.R.: Effect of intrusion detection and response on reliability of cyber physical systems. IEEE Trans. Reliab. 62(1), 199–210 (2013). https://doi.org/10.1109/TR.2013.2240891
Jin, X., Haddad, W.M.: An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks. Int. J. Control 92(11), 2561–2570 (2019). https://doi.org/10.1080/00207179.2018.1450524
Liu, Y., Yang, G.H.: Event-triggered distributed state estimation for cyber-physical systems under dos attacks. IEEE Trans. Cybern. 52(5), 3620–3631 (2022). https://doi.org/10.1109/TCYB.2020.3015507
Funding
This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), which is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Khoshnevisan, L., Liu, X. Resilient neural network-based control of nonlinear heterogeneous multi-agent systems: a cyber-physical system approach. Nonlinear Dyn 111, 19171–19185 (2023). https://doi.org/10.1007/s11071-023-08840-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-023-08840-w