Secure bipartite tracking control of a class of nonlinear multi-agent systems with nonsymmetric input constraint against sensor attacks. (English) Zbl 1478.93037
Summary: This paper addresses a secure bipartite tracking control problem for a class of nonlinear multi-agents (MASs) with nonsymmetric input constraints. In the presence of adversarial sensor attacks, a secure measurement preselector, along with an explicit sufficient condition, and a neural network (NN) secure state observer are introduced for achieving secure state estimation. Then, a secure bipartite tracking control strategy is proposed, where observation predictors are designed to reconstruct prediction errors in such a way as to improve control performance. Furthermore, an auxiliary system is presented to eliminate influence from nonsymmetric input saturations. It is theoretically proved that the proposed control strategy not only guarantees bipartite tracking of the MAS but also preserves the stability of the resulting closed-loop system in spite of senor attacks. Finally, two illustrative examples are presented to verify the effectiveness of the obtained results.
References:
| [1] | Zhu, Q.; Basar, T., Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems, IEEE Control Syst. Mag., 35, 1, 46-65 (2015) · Zbl 1476.93098 |
| [2] | Zhang, X.-M.; Han, Q.-L.; Ge, X., Networked control systems: a survey of trends and techniques, IEEE/CAA J. Automatica Sinica, 7, 1, 1-17 (2020) |
| [3] | Stergiou, C.; Plageras, A.; Psannis, K. E.; Gupta, B., Handbook of Computer Networks and Cyber Security: Principles and Paradigms (2019), Springer |
| [4] | Ge, X.; Han, Q.-L.; Zhang, X.-M.; Ding, L.; Yang, F., Distributed event-triggered estimation over sensor networks: a survey, IEEE Trans. Cybern., 50, 3, 1306-1320 (2020) |
| [5] | Dierks, T.; Jagannathan, S., Output feedback control of a quadrotor UAV using neural networks, IEEE Trans. Neural Networks, 21, 1, 50-66 (2009) |
| [6] | Zhao, B.; Xian, B.; Zhang, Y., Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology, IEEE Trans. Industr. Electron., 62, 5, 2891-2902 (2014) |
| [7] | Li, T. S.; Wang, D.; Feng, G., A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems, IEEE Trans. Syst., Man, Cybern.-Part B: Cybern., 40, 3, 915-927 (2010) |
| [8] | Liu, L.; Wang, D.; Peng, Z., Bounded neural network control for target tracking of underactuated autonomous surface vehicles in the presence of uncertain target dynamics, IEEE Trans. Neural Networks Learn. Syst., 30, 4, 1241-1249 (2018) |
| [9] | Li, J.; Yu, C.; Gupta, B. B., Color image watermarking scheme based on quaternion Hadamard transform and Schur decomposition, Multimedia Tools Appl., 77, 4, 4545-4561 (2018) |
| [10] | Li, D.; Deng, L.; Gupta, B. B., A novel CNN based security guaranteed image watermarking generation scenario for smart city applications, Inf. Sci., 479, 432-447 (2019) |
| [11] | Peng, C.; Sun, H.; Yang, M.; Wang, Y., A survey on security communication and control for smart grids under malicious cyber attacks, IEEE Trans. Syst., Man, Cybern.: Syst., 49, 8, 1554-1569 (2019) |
| [12] | Ge, X.; Han, Q.-L.; Zhong, M.; Zhang, X.-M., Distributed Krein space-based attack detection over sensor networks under deception attacks, Automatica, 109 (2019) · Zbl 1429.93135 |
| [13] | Lee, C.; Shim, H.; Eun, Y., On redundant observability: from security index to attack detection and resilient state estimation, IEEE Trans. Autom. Control, 64, 2, 775-782 (2018) · Zbl 1482.93351 |
| [14] | Plageras, P. Andreas, et al., Efficient IoT-based sensor big data collection-processing and analysis in smart buildings, Future Gener. Comput. Syst. 82 (2018) 349-357. |
| [15] | Cao, N.; Liu, P.; Li, G., Evaluation models for the nearest closer routing protocol in wireless sensor networks, IEEE Access, 6, 77043-77054 (2018) |
| [16] | Xiao, S.; Han, Q.-L.; Ge, X.; Zhang, Y., Secure distributed finite-time filtering for positive systems over sensor networks under deception attacks, IEEE Trans. Cybern., 50, 3, 1220-1229 (2020) |
| [17] | Guan, Y.; Ge, X., Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks, IEEE Trans. Signal Inform. Process. Networks, 4, 5, 48-59 (2018) |
| [18] | Zhang, X.-M.; Han, Q.-L.; Ge, X.; Ding, L., Resilient control design based on a sampled-data model for a class of networked control systems under denial-of-service attacks, IEEE Trans. Cybern. (2019) |
| [19] | Kim, J.; Lee, C.; Shim, H., Detection of sensor attack and resilient state estimation for uniformly observable nonlinear systems having redundant sensors, IEEE Trans. Autom. Control, 64, 3, 1162-1169 (2018) · Zbl 1482.93091 |
| [20] | Ao, W.; Song, Y.; Wen, C., Distributed secure state estimation and control for CPSs under sensor attacks, IEEE Trans. Cybern., 50, 1, 259-269 (2018) |
| [21] | Altafini, C., Consensus problems on networks with antagonistic interactions, IEEE Trans. Autom. Control, 58, 4, 935-946 (2012) · Zbl 1369.93433 |
| [22] | Shahvali, M.; Naghibi-Sistani, M. B.; Askari, J., Adaptive output-feedback bipartite consensus for nonstrict-feedback nonlinear multi-agent systems: a finite-time approach, Neurocomputing, 318, 7-17 (2018) |
| [23] | Yang, H.; Ye, D., Adaptive fixed-time bipartite tracking consensus control for unknown nonlinear multi-agent systems: an information classification mechanism, Inf. Sci., 459, 238-254 (2018) · Zbl 1448.93309 |
| [24] | Wang, D.; Ma, H.; Liu, D., Distributed control algorithm for bipartite consensus of the nonlinear time-delayed multi-agent systems with neural networks, Neurocomputing, 174, 928-936 (2016) |
| [25] | Swaroop, D.; Hedrick, J. K.; Yip, P. P., Dynamic surface control for a class of nonlinear systems, IEEE Trans. Autom. Control, 45, 10, 1893-1899 (2000) · Zbl 0991.93041 |
| [26] | Choi, Y. H.; Yoo, S. J., Minimal-approximation-based distributed consensus tracking of a class of uncertain nonlinear multiagent systems with unknown control directions, IEEE Trans. Cybern., 47, 8, 1994-2007 (2017) |
| [27] | Ding, D.; Wang, Z.; Han, Q.-L., Neural-network-based output-feedback control under round-robin scheduling protocols, IEEE Trans. Cybern., 49, 6, 2372-2384 (2018) |
| [28] | Peng, Z.; Wang, D.; Wang, J., Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form, IEEE Trans. Neural Networks Learn. Syst., 28, 9, 2156-2167 (2016) |
| [29] | Yucelen, T.; Haddad, W. M., Low-frequency learning and fast adaptation in model reference adaptive control, IEEE Trans. Autom. Control, 58, 4, 1080-1085 (2013) · Zbl 1369.93322 |
| [30] | Cao, C.; Hovakimyan, N., Novel \(\mathcal{L}_1\) neural network adaptive control architecture with guaranteed transient performance, IEEE Trans. Neural Networks, 18, 4, 1160-1171 (2007) |
| [31] | Bai, W.; Zhou, Q.; Li, T.; Li, H., Adaptive reinforcement learning neural network control for uncertain nonlinear system with input saturation, IEEE Trans. Cybern. (2019) |
| [32] | Tian, E.; Wang, Z.; Zou, L.; Yue, D., Chance-constrained \(H_\infty\) control for a class of time-varying systems with stochastic nonlinearities: the finite-horizon case, Automatica, 107, 296-305 (2019) · Zbl 1429.93090 |
| [33] | Esfandiari, K.; Abdollahi, F.; Talebi, H. A., Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks, IEEE Trans. Neural Networks Learn. Syst., 26, 10, 2311-2322 (2014) |
| [34] | Li, H.; Bai, L.; Zhou, Q., Adaptive fuzzy control of stochastic nonstrict-feedback nonlinear systems with input saturation, IEEE Trans. Syst., Man, Cybern.: Syst., 47, 8, 2185-2197 (2017) |
| [35] | Gao, Y. F.; Sun, X. M.; Wen, C., Observer-based adaptive NN control for a class of uncertain nonlinear systems with nonsymmetric input saturation, IEEE Trans. Neural Networks Learn. Syst., 28, 7, 1520-1530 (2016) |
| [36] | An, L.; Yang, G. H., LQ secure control for cyber-physical systems against sparse sensor and actuator attacks, IEEE Trans. Control Network Syst., 6, 2, 833-841 (2018) · Zbl 1515.93061 |
| [37] | Zhao, L.; Yu, J.; Lin, C., Command filter based adaptive fuzzy bipartite output consensus tracking of nonlinear coopetition multi-agent systems with input saturation, ISA Trans., 80, 187-194 (2018) |
| [38] | Yu, T.; Ma, L.; Zhang, H., Prescribed performance for bipartite tracking control of nonlinear multiagent systems with hysteresis input uncertainties, IEEE Trans. Cybern., 49, 4, 1327-1338 (2018) |
| [39] | Peng, Z.; Hu, J.; Shi, K., A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning, Appl. Math. Comput., 369, Article 124821 pp. (2020) · Zbl 1433.93008 |
| [40] | Achili, B.; Madani, T.; Daachi, B., Adaptive observer based on MLPNN and sliding mode for wearable robots: application to an active joint orthosis, Neurocomputing, 197, 69-77 (2016) |
| [41] | Li, T.; Li, Z.; Wang, D., Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions, IEEE Trans. Neural Networks Learn. Syst., 26, 6, 1188-1201 (2014) |
| [42] | Li, T. S.; Wang, D.; Feng, G., A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems, IEEE Trans. Syst., Man, Cybern. -Part B: Cybern., 40, 3, 915-927 (2010) |
| [43] | Li, Y.; Tong, S.; Li, T., Adaptive Fuzzy output feedback dynamic surface control of interconnected nonlinear pure-feedback systems, IEEE Trans. Cybern., 45, 1, 138-149 (2015) |
| [44] | Qin, J.; Fu, W.; Zheng, W. X., On the Bipartite consensus for generic linear multiagent systems with input saturation, IEEE Trans. Cybern., 47, 8, 1948-1958 (2017) |
| [45] | Hu, H. X.; Wen, G.; Yu, W., Finite-time coordination behavior of multiple Euler-Lagrange systems in cooperation-competition networks, IEEE Trans. Cybern., 49, 8, 2967-2979 (2019) |
| [46] | Guo, B. Z.; Zhao, Z. L., On convergence of tracking differentiator, Int. J. Control, 84, 4, 693-701 (2011) · Zbl 1246.93085 |
| [47] | Wang, D.; Huang, J., Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form, IEEE Trans. Neural Networks, 16, 1, 195-202 (2005) |
| [48] | Skjetne, R.; Fossen, T. I.; Kokotovic, P. V., Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory, Automatica, 41, 2, 289-298 (2005) · Zbl 1096.93026 |
| [49] | Shi, K.; Wang, J.; Zhong, S., Non-fragile memory filtering of T-S fuzzy delayed neural networks based on switched fuzzy sampled-data control, Fuzzy Sets Syst. (2019) |
| [50] | Shi, K.; Liu, X.; Tang, Y., Some novel approaches on state estimation of delayed neural networks, Inf. Sci., 372, 313-331 (2016) · Zbl 1429.93142 |
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