Observer-based adaptive secure control with nonlinear gain recursive sliding-mode for networked non-affine nonlinear systems under DoS attacks

Yang, Yang ; Meng, Qing ; Yue, Dong ; Zhang, Tengfei ; Zhao, Bo ; Hou, Xiaolei

Kybernetika, Tome 56 (2020) no. 2, pp. 298-322

Abstract (VO)

We address the secure control issue of networked non-affine nonlinear systems under denial of service (DoS) attacks. As for the situation that the system information cannot be measured in specific period due to the malicious DoS attacks, we design a neural networks (NNs) state observer with switching gain to estimate internal states in real time. Considering the error and dynamic performance of each subsystem, we introduce the recursive sliding mode dynamic surface method and a nonlinear gain function into the secure control strategy. The relationship between the frequency (duration) of DoS attacks and the stability of the system is established by the average dwell time (ADT) method. It is proven that the system can withstand the influence of DoS attacks and track the desired trajectory while preserving the boundedness of all closed-loop signals. Finally, simulation results are provided to verify the effectiveness of the proposed secure control strategy.

Détail

DOI : 10.14736/kyb-2020-2-0298

Classification : 93A15, 93D15, 93D21
Keywords: networked control system; secure control; adaptive control; dynamic surface control

Bibliographie

[1] An, L., Yang, G. H.: Decentralized adaptive fuzzy secure control for nonlinear uncertain interconnected systems against intermittent dos attacks. IEEE Trans. Cybernet. 49 (2019), 3, 827-838. | DOI

[2] Boubakir, A., Labiod, S., Boudjema, F.: Linear adaptive control of a class of SISO nonaffine nonlinear systems. Int. J. Systems Sci. 45 (2014), 12, 2490-2498. | DOI | MR

[3] Chen, W., Ding, D., Ge, X., Han, Q. L., Wei, G.: $H_{\infty}$ containment control of multi-agent systems under event-triggered communication scheduling: The finite-horizon case. IEEE Trans. Cybernet. (2018) 1-11. | DOI

[4] Chen, B., Zhang, H., Lin, C.: Observer-based adaptive neural network control for nonlinear systems in nonstrict-feedback form. IEEE Trans. Neural Networks Learning Systems 27 (2017), 1, 89-98. | DOI | MR

[5] Gahinet, P., Nemirovskii, A., Laub, A. J.: The LMI control toolbox. In: Proc. 33rd IEEE Conference on Decision and Control, IEEE 3 (1994), pp. 2038-2041. | DOI

[6] Ge, X., Han, Q. L.: Consensus of multiagent systems subject to partially accessible and overlapping Markovian network topologies. IEEE Trans. Cybernet. 47 (2017), 8, 1807-1819. | DOI

[7] Ding, L., Han, Q. L., Ge, X.: An overview of recent advances in event-triggered consensus of multiagent systems. IEEE Trans. Cybernet. 48 (2018), 4, 1110-1123. | DOI | MR

[8] Ding, D., Wang, Z., Han, Q. L., Wei, G.: Neural-network-based output-feedback control under Round-Robin scheduling protocols. IEEE Trans. Cybernet. 49 (2019), 6, 2372-2384. | DOI

[9] Dolk, V. S., Tesi, P., Persis, C. D.: Event-triggered control systems under denial-of-service attacks. IEEE Trans. Control Network Syst. 4 (2016), 1, 93-105. | DOI | MR

[10] Ge, X., Han, Q. L., Wang, Z.: A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybernetics 49 (2019), 1, 171-183. | DOI | MR

[11] Ge, X., Han, Q. L., Zhang, X. M.: Achieving cluster formation of multi-agent systems under aperiodic sampling and communication delays. IEEE Trans. Industr. Electron. 65 (2018), 4, 3417-3426. | DOI

[12] Hu, S., Yue, D., Xie, X.: Resilient event-triggered controller synthesis of networked control systems under periodic dos jamming attacks. IEEE Trans. Cybernet. 49 (2018), 12, 4271-4281. | DOI

[13] Ding, D., Han, Q. L., Wang, Z., Ge, X.: A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Trans. Industr. Inform. 15 (2019), 5, 2483-2499. | DOI

[14] Farraj, A., Hammad, E., Kundur, D.: A cyber-physical control framework for transient stability in smart grids. IEEE Trans. Smart Grid 9 (2018), 2, 1205-1215. | DOI

[15] Ge, X., Han, Q. L., Wang, Z.: A threshold-parameter-dependent approach to designing distributed event-triggered $H_{\infty}$ consensus filters over sensor networks. IEEE Trans. Cybernet. 9 (2019), 4, 1148-1159. | DOI

[16] Kulkarni, A., Purwar, S.: Adaptive nonlinear gain based composite nonlinear feedback controller with input saturation. IMA J. Math. Control Inform. 3 (2018), 35, 757-771. | DOI | MR

[17] Li, Y., Tong, S.: Prescribed performance adaptive fuzzy output-feedback dynamic surface control for nonlinear large-scale systems with time delays. Inform. Sci. 29 (2015), 125-142. | DOI | MR

[18] Li, Y., Tong, S., Li, T.: Adaptive fuzzy output feedback dynamic surface control of interconnected nonlinear pure-feedback systems. IEEE Trans. Cybernet. 45 (2014), 1, 138-149. | DOI

[19] Liu, X., Sun, X.: Non-fragile recursive sliding mode dynamic surface control with adaptive neural network. Control Theory Appl. 30 (2013), 10, 1323-1328.

[20] Liu, X., Sun, X.: Recursive sliding-mode dynamic surface adaptive NN control with nonlinear gains. Acta Automat. Sinica 40 (2014), 10, 2193-2202.

[21] Lu, A. Y., Yang, G. H.: Input-to-state stabilizing control for cyber-physical systems with multiple transmission channels under denial of service. IEEE Trans. Automat. Control 63 (2018), 6, 1813-1820. | DOI | MR

[22] Lv, C., Liu, Y., Hu, X.: Simultaneous observation of hybrid states for cyber-physical systems: A case study of electric vehicle powertrain. IEEE Trans. Cybernet. 48 (2018), 8, 2357-2367. | DOI

[23] Niu, B., Li, H., Qin, T.: Adaptive NN dynamic surface controller design for nonlinear pure-feedback switched systems with time-delays and quantized input. IEEE Trans. Systems Man Cybernet.: Systems. 48 (2017), 10, 1676-1688. | DOI

[24] Otto, J., Vogel-Heuser, B., Niggemann, O.: Automatic parameter estimation for reusable software components of modular and reconfigurable cyber-physical production systems in the domain of discrete manufacturing. IEEE Trans. Industr. Inform. 14 (2018), 1, 275-282. | DOI

[25] Persis, C. D., Tesi, P.: Input-to-state stabilizing control under denial-of-service. IEEE Trans. Automat. Control. 60 (2015), 11, 2930-2944. | DOI | MR

[26] Qin, J., Li, M., Shi, L.: Optimal denial-of-service attack scheduling with energy constraint over packet-dropping networks. IEEE Trans. Automat. Control 63 (2018), 6, 1648-1663. | DOI | MR

[27] Shen, Z.: Recursive sliding mode dynamic surface output feedback control for ship trajectory tracking based on neural network observer. Control Theory Appl. 35 (2018), 8, 1092-1100.

[28] Shen, Z., Zhang, X.: Recursive sliding-mode dynamic surface adaptive control for ship trajectory tracking with nonlinear gains. Acta Automat. Sinica 44 (2018), 10, 1833-1841.

[29] Shi, X., Lim, C. C., Shi, P.: Adaptive neural dynamic surface control for nonstrict-feedback systems with output dead zone. IEEE Trans. Neural Networks Learning Systems 29 (2018), 11, 5200-5213. | DOI | MR

[30] Sun, H., Peng, C., Zhang, W.: Security-based resilient event-triggered control of networked control systems under denial of service attacks. J. Franklin Inst. 356 (2018), 17, 10277-10295. | DOI | MR

[31] Sun, Y. C., Yang, G. H.: Periodic event-triggered resilient control for cyber-physical systems under denial-of-service attacks. J. Franklin Inst. 355 (2018), 13, 5613-5631. | DOI | MR

[32] Sun, Y. C., Yang, G. H.: Event-triggered resilient control for cyber-physical systems under asynchronous DoS attacks. Inform. Sci. 465 (2018), 340-352. | DOI | MR

[33] Swaroop, D., Hedrick, J. K., Yip, P. P.: Dynamic surface control for a class of nonlinear systems. IEEE Trans. Automat. Control 45 (2000), 10, 1893-1899. | DOI | MR

[34] 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 (2019), 296-305. | DOI | MR

[35] Tian, E., Wang, Z., Zou, L., Yue, D.: Probabilistic-constrained filtering for a class of nonlinear systems with improved static event-triggered communication. Internat. J. Robust Nonlinear Control 29 (2019), 5, 1484-1498. | DOI | MR

[36] Tong, S., Li, Y., Jing, X.: Adaptive fuzzy decentralized dynamics surface control for nonlinear large-scale systems based on high-gain observer. Inform. Sci. 235 (2013), 287-307. | DOI | MR

[37] Wang, Y., Gao, Y., Karimi, H. R.: Sliding mode control of fuzzy singularly perturbed systems with application to electric circuit. IEEE Trans. Systems Mand Cybernet.: Systems 48 (2017), 10, 1667-1675. | DOI

[38] 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 (2005), 1, 195-202. | DOI

[39] Wu, L., Gao, Y., Liu, J.: Event-triggered sliding mode control of stochastic systems via output feedback. Automatica 82 (2017), 79-92. | DOI | MR

[40] Xu, L., Guo, Q., Yang, T.: Robust routing optimization for smart grids considering cyber-physical interdependence. IEEE Trans. Smart Grid 10 (2018), 5, 5620-5629. | DOI

[41] Ye, X.: Global adaptive control of nonlinearly parametrized systems. IEEE Trans. Automat. Control 48 (2003), 1, 169-173. | DOI | MR

[42] Yang, J., Chen, Y., Cui, L.: Multiple-mode adaptive state estimator for nonlinear switched systems. Int. Control Automat. Syst. 15 (2017), 4, 1485-1493. | DOI

[43] Yu, J., Ma, Y., Yu, H.: Adaptive fuzzy dynamic surface control for induction motors with iron losses in electric vehicle drive systems via backstepping. Inform. Sci. 376 (2017), 172-189. | DOI

[44] Yu, Q., Wu, B.: Robust stability analysis of uncertain switched linear systems with unstable subsystems. Int. J. Systems Sci. 46 (2015), 7, 1278-1287. | DOI | MR

[45] Zhai, D., An, L., Dong, J.: Switched adaptive fuzzy tracking control for a class of switched nonlinear systems under arbitrary switching. IEEE Trans. Fuzzy Syst. 26 (2018), 2, 585-597. | DOI

[46] Zhai, G., Hu, B., Yasuda, K.: Stability analysis of switched systems with stable and unstable subsystems: an average dwell time approach. Int. J. Systems Sci. 32 (2001), 8, 1055-1061. | DOI | MR

[47] Zhai, D., Xi, C., An, L.: Prescribed performance switched adaptive dynamic surface control of switched nonlinear systems with average dwell time. IEEE Trans. Systems Man Cybernet.: Systems 47 (2017), 7, 1257-1269. | DOI

[48] Zhang, H., Cheng, P., Shi, L.: Optimal denial-of-service attack scheduling with energy constraint. IEEE Trans. Automat. Control 60 (2015), 11, 3023-3028. | DOI | MR

[49] Zhang, T. P., Ge, S. S.: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44 (2008), 7, 1895-1903. | DOI | MR

[50] Zhang, X. M., Han, Q. L., Ge, X.: Networked control systems: A survey of trends and techniques. IEEE/CAA J. Automat. Sinica (2019), 1-17. | DOI | MR

[51] Zhang, T., Xia, M., Yi, Y.: Adaptive neural dynamic surface control of pure-feedback nonlinear systems with full state constraints and dynamic uncertainties. IEEE Trans. Systems Man Cybernet.: Systems. 47 (2017), 8, 2378-2387. | DOI | MR

[52] Zuo, Z., Han, Q. L., Ning, B.: An overview of recent advances in fixed-time cooperative control of multi-agent systems. IEEE Trans. Industr. Informat. 14 (2018), 6, 2322-2334. | DOI | MR

[53] Zou, A. M., Hou, Z. G., Tan, M.: Adaptive control of a class of nonlinear pure-feedback systems using fuzzy backstepping approach. IEEE Trans. Fuzzy Syst. 16 (2008), 4, 886-897. | DOI