تحلیل پایداری سیستم‌های کنترل شده تحت شبکه حین حملات محرومیت سرویس با تئوری سیستم‌های سوئیچنگ
محورهای موضوعی :محمد صیاد حقیقی 1 , فائزه فریور 2
1 - استادیار دانشکده مهندسی برق و کامپیوتر، پردیس دانشکده‌های فنی، دانشگاه تهران
2 - استادیار گروه مهندسی کامپیوتر- مکاترونیک، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی
کلید واژه: سیستم کنترل‌شده تحت شبکه, اتلاف بسته, محرومیت سرویس, سیستم سوییچینگ, سیستم خطی پرش مارکوف, پایداری لیاپانوف.,
چکیده مقاله :
با رشد روز افزون استفاده از شبکه‌های کامپیوتری برای انتقال داده، سیستم‌های سایبری- فیزیکی بسیار مورد توجه قرار گرفته اند. سیستم های کنترل شده تحت شبکه ، از انواع صنعتی این سیستمها هستند که در آن سنسورها و عملگرها، از طریق شبکه اطلاعات را بین واحد های مختلف تبادل می کنند. از دست رفتن داده در شبکه بر عملکرد سیستم فیزیکی و پایداری آن تاثیرگذار است. از دست رفتن عمده داده می تواند بدلیل حمله محرومیت سرویس باشد. در این مقاله، به تحلیل پایداری سیستم‌های خطی کنترل‌شده تحت شبکه با احتمال از دست رفتن داده در مسیر پیشرو بدلیل حمله پرداخته شده است. سیستم کنترل شده تحت شبکه در حین حمله با یک سیستم سوییچینگ تصادفی با مدل پرش مارکوف دو وضعیته مدل شده است. در وضعیت شماره یک شبکه داده ارسالی کنترل‌کننده را به سیستم انتقال میدهد و در وضعیت شماره دو، داده از دست رفته و سیستم از داده دیگری مانند یک مقدار پیش فرض به عنوان ورودی استفاده می کند. در این مقاله پایداری سیستم فیزیکی کنترل شده تحت شبکه حین حملات محرومیت سرویس هم در حوزه زمان پیوسته و هم زمان گسسته مورد تحلیل قرارگرفته است که دستاورد آنها، معرفی شرایط پایداری لیاپانوف برای سیستم با توجه به زمانهای اقامت تصادفی در هر وضعیت است. همچنین با استفاده از نتایج تحلیل انجام شده، یک روش جدید برای پایداری سازی چنین سیستمهایی تحت حمله محرومیت از سرویس از طریق مدیریت مقدار پیش فرض پیشنهاد می شود. در نهایت، مطالعه انجام شده بر روی چند سیستم کنترلی نمونه شبیه‌سازی شده است. نتایج ضمن تایید تئوری استخراج شده، نشان می دهند که چگونه سیستمی تحت حمله که حدود 80% بسته کنترلی خود را از دست می دهد، با روش پیشنهادی پایدار نگاه داشته می شود.
With the development of computer networks, packet-based data transmission has found its way to Cyber-Physical Systems (CPS) and especially, networked control systems (NCS). NCSs are distributed industrial processes in which sensors and actuators exchange information between the physical plant and the controller via a network. Any loss of data or packet in the network links affects the performance of the physical system and its stability. This loss could be due to natural congestions in network or a result of intentional Denial of Service (DoS) attacks. In this paper, we analytically study the stability of NCSs with the possibility of data loss in the feed-forward link by modelling the system as a switching one. When data are lost (or replaced with a jammed or bogus invalid signal/packet) in the forward link, the physical system will not receive the control input sent from the controller. In this study, NCS is regarded as a stochastic switching system by using a two-position Markov jump model. In State 1, the control signal/packet passes through and gets to the system, while in State 2, the signal or packet is lost. We analyze the stability of system in State 2 by considering the situation as an open-loop control scenario with zero input. The proposed stochastic switching system is studied in both continuous and discrete-time spaces to see under what conditions it satisfies Lyapunov stability. The stability conditions are obtained according to random dwell times of the system in each state. Finally, the model is simulated on a DC motor as the plant. The results confirm the correctness of the obtained stability conditions.
[1] H. R. Shaker and R. Wisniewski, “Model reduction of switched systems based on switching generalized gramians,” Int. J. Innov. Comput. Inf. Control, 2012.
[2] G. Wang, Y. Liu, C. Wen, and W. Chen, “Delay-dependent stability criterion and H∞ state-feedback control for uncertain discrete-time switched systems with time-varying delays,” Int. J. Innov. Comput. Inf. Control, 2011.
[3] C. Jiang, K. L. Teo, R. Loxton, and G. R. Duan, “A neighboring extremal solution for optimal switched impulsive control problems with large perturbations,” Int. J. Innov. Comput. Inf. Control, 2012.
[4] J. P. Hespanha, “A model for stochastic hybrid systems with application to communication networks,” Nonlinear Anal. Theory, Methods Appl., 2005.
[5] Q. Zhang, “Stochastic Switching Systems: Analysis and Design-[Book review; E. K. Boukas],” IEEE Trans. Automat. Contr., 2007.
[6] R. Yang, P. Shi, G. P. Liu, and H. Gao, “Network-based feedback control for systems with mixed delays based on quantization and dropout compensation,” Automatica, 2011.
[7] Q. Lu, L. Zhang, M. Basin, and H. Tian, “Analysis and synthesis for networked control systems with uncertain rate of packet losses,” J. Franklin Inst., 2012.
[8] L. Zhang, H. Gao, and O. Kaynak, “Network-induced constraints in networked control systems-A survey,” IEEE Trans. Ind. Informatics, 2013.
[9] H. S. Foroush and S. Martínez, “On single-input controllable linear systems under periodic DoS jamming attacks,” arXiv Prepr. arXiv1209.4101, 2012.
[10] A. A. Ahmadi and R. M. Jungers, “On complexity of Lyapunov functions for switched linear systems,” in IFAC Proceedings Volumes (IFAC-PapersOnline), 2014.
[11] A. Bacciotti, “Stability of switched systems: An introduction,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014.
[12] D. Liberzon, J. P. Hespanha, and A. S. Morse, “Stability of switched systems: A Lie-algebraic condition,” Syst. Control Lett., 1999.
[13] J. Daafouz, P. Riedinger, and C. Iung, “Stability analysis and control synthesis for switched systems: A switched Lyapunov function approach,” IEEE Trans. Automat. Contr., 2002.
[14] J. P. Hespanha and A. S. Morse, “Stability of switched systems with average dwell-time,” in Proceedings of the IEEE Conference on Decision and Control, 1999.
[15] G. Zhai, B. Hu, K. Yasuda, and A. N. Michel, “Stability analysis of switched systems with stable and unstable subsystems: An average dwell time approach,” Int. J. Syst. Sci., 2001.
[16] H. Zhang, D. Xie, H. Zhang, and G. Wang, “Stability analysis for discrete-time switched systems with unstable subsystems by a mode-dependent average dwell time approach,” ISA Trans., 2014.
[17] H. Ishii, T. Başar, and R. Tempo, “Randomized algorithms for synthesis of switching rules for multimodal systems,” IEEE Trans. Automat. Contr., 2005.
[18] Y. Zhang, M. Wang, H. Xu, and K. L. Teo, “Global stabilization of switched control systems with time delay,” Nonlinear Anal. Hybrid Syst., 2014.
[19] J. Xiong, J. Lam, Z. Shu, and X. Mao, “Stability analysis of continuous-time switched systems with a random switching signal,” IEEE Trans. Automat. Contr., 2014.
[20] H. Liu, Y. Shen, and X. Zhao, “Finite-time stabilization and boundedness of switched linear system under state-dependent switching,” J. Franklin Inst., 2013.
[21] M. Prandini, J. P. Hespanha, and M. C. Campi, “Hysteresis-based switching control of stochastic linear systems,” in European Control Conference, ECC 2003, 2003.
[22] S. Miani and A. C. Morassutti, “Switching controllers for networked control systems with packet dropouts and delays in the sensor channel,” IFAC Proc. Vol., 2009.
[23] J. Nygren and K. Pelckmans, “A closed loop stability condition of switched systems applied to NCSs with packet loss,” IFAC-PapersOnLine, 2015.
[24] N. A. Baleghi and M. H. Shafiei, “Stability analysis for discrete-time switched systems with uncertain time delay and affine parametric uncertainties,” Trans. Inst. Meas. Control, 2018.
[25] A. R. Teel, A. Subbaraman, and A. Sferlazza, “Stability analysis for stochastic hybrid systems: A survey,” Automatica, 2014.
[26] P. Shi and F. Li, “A survey on Markovian jump systems: Modeling and design,” Int. J. Control. Autom. Syst., 2015.
[27] Y. Fang and K. A. Loparo, “Stochastic stability of jump linear systems,” IEEE Trans. Automat. Contr., 2002.
[28] O. L. V. Costa, M. D. Fragoso, and R. P. Marques, Discrete-time Markov jump linear systems. Springer Science & Business Media, 2006.
[29] W. Zhou, Q. Zhu, P. Shi, H. Su, J. Fang, and L. Zhou, “Adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching parameters,” IEEE Trans. Cybern., 2014.
[30] A. S. Morse, “Supervisory control of families of linear set-point controllers - Part 1: Exact matching,” IEEE Trans. Automat. Contr., 1996.
[31] Y. Ji and H. J. Chizeck, “Controllability, Stabilizability, and Continuous-Time Markovian Jump Linear Quadratic Control,” IEEE Trans. Automat. Contr., 1990.
[32] Z. Ning, L. Zhang, and J. Lam, “Stability and stabilization of a class of stochastic switching systems with lower bound of sojourn time,” Automatica, 2018.
[33] H. Yang, H. Xu, Y. Xia, and J. Zhang, “Stability analysis on networked control systems under double attacks with predictive control,” Int. J. Robust Nonlinear Control, 2020.
[34] C. Peng and H. Sun, “Switching-Like Event-Triggered Control for Networked Control Systems under Malicious Denial of Service Attacks,” IEEE Trans. Automat. Contr., 2020.
[35] F. Farivar, M. S. Haghighi, A. Jolfaei, and S. Wen, “On the Security of Networked Control Systems in Smart Vehicle and Its Adaptive Cruise Control,” IEEE Trans. Intell. Transp. Syst., 2021.
[36] F. Farivar, M. S. Haghighi, S. Barchinezhad, and A. Jolfaei, “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, 2019, vol. 2019-Febru.
[37] Y. Deng, X. Yin, and S. Hu, “Event-triggered predictive control for networked control systems with DoS attacks,” Inf. Sci. (Ny)., 2021.
[38] J. Liu, Z. G. Wu, D. Yue, and J. H. Park, “Stabilization of Networked Control Systems with Hybrid-Driven Mechanism and Probabilistic Cyber Attacks,” IEEE Trans. Syst. Man, Cybern. Syst., 2021.
[39] S. Y. Lü, X. Z. Jin, H. Wang, and C. Deng, “Robust adaptive estimation and tracking control for perturbed cyber-physical systems against denial of service,” Appl. Math. Comput., 2021.
[40] N. Toorchi, M. A. Attari, M. S. Haghighi, and Y. Xiang, “A Markov model of safety message broadcasting for vehicular networks,” in IEEE Wireless Communications and Networking Conference, WCNC, 2013.
[41] M. S. Haghighi and Z. Aziminejad, “Highly Anonymous Mobility-Tolerant Location-based Onion Routing for VANETs,” IEEE Internet Things J., 2019.
[42] A. R. Javed, M. Usman, S. U. Rehman, M. U. Khan, and M. S. Haghighi, “Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network,” IEEE Trans. Intell. Transp. Syst., 2021.
[43] F. Farivar, M. S. Haghighi, A. Jolfaei, and M. Alazab, “Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT,” IEEE Trans. Ind. Informatics, 2020.
[44] F. Farivar, M. S. Haghighi, A. Jolfaei, and S. Wen, “Covert Attacks through Adversarial Learning: Study of Lane Keeping Attacks on the Safety of Autonomous Vehicles,” IEEE/ASME Trans. Mechatronics, 2021.
[45] G. Wangg and Z. Li, “Stability analysis of discrete-time randomly switched systems via an LMI approach,” in Chinese Control Conference, CCC, 2017.
[46] Debabrata Pal, “Modeling, Analysis and Design of a DC Motor based on State Space Approach,” Int. J. Eng. Res., 2016.
