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

• Published in: The Journal of supercomputing 2020-04, Vol.76 (4), p.3063-3085
• Main Authors: Sayad Haghighi, Mohammad, Farivar, Faezeh, Jolfaei, Alireza, Tadayon, Mohammad Hesam
• Format: Article
• Language: English
• Subjects: Algorithms; Angular position; Compilers; Computer Science; Computer simulation; Control stability; Control systems; Cost function; Cyber-physical systems; Cybersecurity; Denial of service attacks; Hybrid control; Hybrid systems; Intelligent and Pervasive Computing for Cyber-Physical Systems; Interpreters; Linear quadratic regulator; Machine learning; Neural networks; Processor Architectures; Programming Languages; Radial basis function; Robust control; Sliding mode control; Stability analysis; Tracking control; Trajectory control; Vibration control
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-019-03075-2

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.