Learning Tracking Control for Cyber-Physical Systems

Chengwei Wu, Wei Pan, Guanghui Sun, Jianxing Liu, Ligang Wu

Research output: Contribution to journalArticleScientificpeer-review


This paper investigates the problem of optimal tracking control for cyber-physical systems (CPS) when the cyber realm is attacked by denial-of-service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and analyze the impact of DoS attacks on tracking performance. First, a Riccati equation for the augmented system including the system model and the reference model is derived under the framework of dynamic programming. The existence and uniqueness of its solution are proved. Second, the impact of the successful DoS attack probability on tracking performance is analyzed. A critical value of the probability is given, beyond which the solution to the Riccati equation cannot converge. The tracking controller cannot be designed. Third, reinforcement learning is introduced to design the optimal tracking control schemes, in which the system dynamics are not necessary to be known. Finally, both a dc motor and an F16 aircraft are used to evaluate the proposed control schemes in this paper.

Original languageEnglish
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2 Feb 2021


  • Actuators
  • Cyber-physical systems
  • DoS attacks.
  • Mathematical model
  • Optimal tracking control
  • Reinforcement learning
  • Riccati equations
  • Signal to noise ratio
  • System dynamics
  • Trajectory

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