Input-output data based tracking control under DoS attacks

ABSTRACT

This paper investigates the secure optimal tracking control problem for cyber-physical systems, in which the controller-actuator channel is jammed by denial-of-service attacks. Without leveraging model knowledge, learning-based tracking control algorithms are proposed by utilizing the measured input-output data. The impact of malicious attacks on tracking performance is discussed. More specifically, an augmented system consisted of the system model and the reference model is derived. Then the states of the augmented system are rewritten by utilizing the input, output, and reference trajectory, following which both the Bellman equation and algebraic Riccati equation are given. The conditions of existence and uniqueness of the solution to the algebraic Riccati equation are derived. Furthermore, both policy iteration and value iteration learning-based tracking control algorithms are provided for cyber-physical systems against denial-of-service attacks, and the convergence of the algorithms is also proved. Finally, a DC motor system and a numerical example are given to illustrate the effectiveness of the proposed control algorithms.

KEYWORDS:

• Cyber-physical systems
• denial-of-service attacks
• input-output data
• reinforcement learning control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62203136, 62033005, 62022030, 62173107, and in part by the Key R&D Program of Heilongjiang Province under Grant 2022ZX01A18, in part by the China Postdoctoral Science Foundation under Grant 2021M701007, 2021TQ0091, and in part by the Postdoctoral Science Foundation of Heilongjiang Province under Grant LBH-Z21059.