Learning Tracking Control for Cyber-Physical Systems

Journal article (2021)

Authors

C. Wu Harbin Institute of Technology
W. Pan Robot Dynamics - Mechanical, Maritime and Materials Engineering
Guanghui Sun Harbin Institute of Technology
Jianxing Liu Harbin Institute of Technology
Ligang Wu Harbin Institute of Technology
Research Group
Robot Dynamics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 C. Wu, W. Pan, Guanghui Sun, Jianxing Liu, Ligang Wu
DOI: https://doi.org/10.1109/JIOT.2021.3056633
Cyber-physical systems Reinforcement learning Mathematical model Trajectory Actuators System dynamics Signal to noise ratio DoS attacks. Optimal tracking control Riccati equations
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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Robot Dynamics

Abstract

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.

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