Advertisement

Intelligent robust control for cyber-physical systems of rotary gantry type under denial of service attack

  • Mohammad Sayad HaghighiEmail author
  • Faezeh Farivar
  • Alireza Jolfaei
  • Mohammad Hesam Tadayon
Article
  • 31 Downloads

Abstract

This paper presents an approach for tolerant control and compensation of cyber attacks on the inputs and outputs of a cyber-physical system of rotary gantry type. The proposed control schemes are designed based on classic–intelligent control strategies for trajectory tracking and vibration control of a networked control system, which are developed for tip angular position control, while the system is prone to cyber attacks. The malicious attacks are assumed to be of denial of service (DoS) kind and cause packet loss with high probability in the two signals; control input and sensor output. In this paper, several classic and intelligent control strategies are studied in terms of robustness and effectiveness to attacks. Based on the results, a new hybrid control scheme is designed using linear quadratic regulation, sliding mode control, and artificial radial basis function neural network to alleviate the effect of DoS attacks and maintain the performance of the cyber-rotary gantry system in tracking applications. The neural network controller is trained during the control process. Its learning algorithm is based on the minimization of a cost function which contains the sliding surface. The hybrid control system is analyzed from the stability perspective. Moreover, the efficiency of the proposed scheme is validated by simulation on MATLAB Simulink platform.

Keywords

Cyber attack Cyber-physical system Intrusion Neural network control Rotary gantry Security Sliding mode control 

Notes

Acknowledgements

Authors are indebted to the Advanced Networking and Security research Laboratory (ANSLab) for the support provided during this study.

References

  1. 1.
    Ahmad A (2009) Sway reduction on gantry crane system using delayed feedback signal and pd-type fuzzy logic controller: a comparative assessment. Int J Intell Syst Technol 4(3):143–148Google Scholar
  2. 2.
    Ahmad M (2009) Optimal tracking with sway suppression control for a gantry crane system. Eur J Sci Res 33(4):630–641Google Scholar
  3. 3.
    Ahmad M, Nasir A, Ismail RR, Ramli M (2010) Control schemes for input tracking and anti-sway control of a gantry crane. Aust J Basic Appl Sci 4(8):2280–2291Google Scholar
  4. 4.
    Ahmad MA (2009) Active sway suppression techniques of a gantry crane system. Eur J Sci Res 27(3):322–333Google Scholar
  5. 5.
    Amin S, Cárdenas AA, Sastry SS (2009) Safe and secure networked control systems under denial-of-service attacks. In: International Workshop on Hybrid Systems: Computation and Control. Springer, pp 31–45Google Scholar
  6. 6.
    Ashibani Y, Mahmoud QH (2017) Cyber physical systems security: analysis, challenges and solutions. Comput Secur 68:81–97CrossRefGoogle Scholar
  7. 7.
    Bartolini G, Orani N, Pisano A, Usai E (2000) Load swing damping in overhead cranes by sliding mode technique. In: Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No. 00CH37187), vol 2. IEEE, pp 1697–1702Google Scholar
  8. 8.
    Bruins S (2010) Comparison of different control algorithms for a gantry crane system. Intell Control Autom 1(02):68CrossRefGoogle Scholar
  9. 9.
    Corriga G, Giua A, Usai G (1998) An implicit gain-scheduling controller for cranes. IEEE Trans Control Syst Technol 6(1):15–20CrossRefGoogle Scholar
  10. 10.
    Farivar F, Nekoui MA, Shoorehdeli MA et al (2010) Neural sliding mode control for chaos synchronization of uncertain nonlinear gyros. Adv Appl Math Sci 4(1):41–56MathSciNetzbMATHGoogle Scholar
  11. 11.
    Farivar F, Sayad Haghighi M, Barchinezhad S, Jolfaei A (2019) Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control. In: 20th IEEE International Conference on Industrial TechnologyGoogle Scholar
  12. 12.
    Farivar F, Sayad Haghighi M, Jolfaei A, Alazab M (2019) Artificial intelligence for detection, estimation, and compensation of malicious attacks in nonlinear cyber physical systems and industrial IoT. In: IEEE transactions on industrial informatics (in press)Google Scholar
  13. 13.
    Farivar F, Shoorehdeli MA, Nekoui MA, Teshnehlab M (2013) Synchronization of underactuated unknown heavy symmetric chaotic gyroscopes via optimal gaussian radial basis adaptive variable structure control. IEEE Trans Control Syst Technol 21(6):2374–2379CrossRefGoogle Scholar
  14. 14.
    Feddema JT (1993) Digital filter control of remotely operated flexible robotic structures. In: 1993 American Control Conference. IEEE, pp 2710–2715Google Scholar
  15. 15.
    Gao W, Morris T, Reaves B, Richey D (2010) On SCADA control system command and response injection and intrusion detection. In: 2010 eCrime Researchers Summit. IEEE, pp 1–9Google Scholar
  16. 16.
    Haghighi MS, Mohamedpour K, Varadharajan V, Quinn BG (2011) Stochastic modeling of hello flooding in slotted csma/ca wireless sensor networks. IEEE Trans Inf Forensics Secur 6(4):1185–1199CrossRefGoogle Scholar
  17. 17.
    Hellinger A, Seeger H (2011) Cyber-physical systems. Driving force for innovation in mobility, health, energy and production. Acatech Position Paper, National Academy of Science and Engineering, vol 1, no. 2Google Scholar
  18. 18.
    Humayed A, Lin J, Li F, Luo B (2017) Cyber-physical systems security: a survey. IEEE Internet Things J 4(6):1802–1831CrossRefGoogle Scholar
  19. 19.
    Jolfaei A, Kant K (2017) A lightweight integrity protection scheme for fast communications in smart grid. In: 14th International Conference on Security and Cryptography, pp 31–42Google Scholar
  20. 20.
    Jolfaei A, Kant K (2018) A lightweight integrity protection scheme for low latency smart grid applications. Comput Secur 86:471–483CrossRefGoogle Scholar
  21. 21.
    Kirk DE (2012) Optimal control theory: an introduction. Courier Corporation, North ChelmsfordGoogle Scholar
  22. 22.
    Korondi P (2006) Tensor product model transformation-based sliding surface design. Acta Polytech Hung 3(4):23–35Google Scholar
  23. 23.
    Lian B, Zhang Q, Li J (2018) Integrated sliding mode control and neural networks based packet disordering prediction for nonlinear networked control systems. IEEE Trans Neural Netw Learn Syst 2324–2335MathSciNetCrossRefGoogle Scholar
  24. 24.
    Long M, Wu CH, Hung JY (2005) Denial of service attacks on network-based control systems: impact and mitigation. IEEE Trans Ind Inform 1(2):85–96CrossRefGoogle Scholar
  25. 25.
    Manson G (1982) Time-optimal control of an overhead crane model. Optim Control Appl Methods 3(2):115–120CrossRefGoogle Scholar
  26. 26.
    Nguyen VAQ, Yoo M (2015) Packet loss compensation for control systems over industrial wireless sensor networks. Int J Distrib Sens Netw 11(8):256757CrossRefGoogle Scholar
  27. 27.
    Noakes MW, Jansen JF (1992) Generalized inputs for damped-vibration control of suspended payloads. Robot Auton Syst 10(2–3):199–205CrossRefGoogle Scholar
  28. 28.
    Nozari HA, Nazeri S, Banadaki HD, Castaldi P (2018) Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques’a comparative study. Control Eng Pract 73:134–148CrossRefGoogle Scholar
  29. 29.
    Omar HM, Nayfeh AH (2005) Anti-swing control of gantry and tower cranes using fuzzy and time-delayed feedback with friction compensation. Shock Vib 12(2):73–89CrossRefGoogle Scholar
  30. 30.
    Pasqualetti F, Dorfler F, Bullo F (2015) Control-theoretic methods for cyberphysical security: geometric principles for optimal cross-layer resilient control systems. IEEE Control Syst Mag 35(1):110–127MathSciNetCrossRefGoogle Scholar
  31. 31.
    Popadić T, Kolonić F, Poljugan A (2006) A fuzzy control scheme for the gantry crane position and load swing control. In: S MIPRO-m u društvo znanja. 29th International Convention MIPRO 2006. CTS&CIS, Computers in Technical Systems, Intelligent Systems and MicroelectronicsGoogle Scholar
  32. 32.
    2 DOF Inverted Pendulum/Gantry Manual. Technical report. http://www.quanser.com. Accessed 24 May 2019
  33. 33.
    Sayad Haghighi M, Mohamedpour K (2010) Neighbor discovery: security challenges in wireless ad hoc and sensor networks. In: Bouras CJ (ed) Trends in telecommunications technologies. Intech, LondonGoogle Scholar
  34. 34.
    Singhose WE, Porter LJ, Seering WP (1997) Input shaped control of a planar gantry crane with hoisting. In: Proceedings of the 1997 American Control Conference (Cat. No. 97CH36041), vol 1. IEEE, pp 97–100Google Scholar
  35. 35.
    Slotine JJE, Li W et al (1991) Applied nonlinear control, vol 199. Prentice Hall, Englewood CliffszbMATHGoogle Scholar
  36. 36.
    Wang Z, Surgenor B (2004) Performance evaluation of the optimal control of a gantry crane. In: ASME 7th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, pp 869–878Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringUniversity of TehranTehranIran
  2. 2.Department of Mechatronics and Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  3. 3.Department of ComputingMacquarie UniversitySydneyAustralia
  4. 4.Iran Telecom Research CenterTehranIran

Personalised recommendations