Open AccessAdaptive integral sliding‐mode control strategy of data‐driven cyber‐physical systems against a class of actuator attacks
Author(s) -
Huang Xin,
Zhai Ding,
Dong Jiuxiang
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1278
Subject(s) - control theory (sociology) , actuator , integral sliding mode , sliding mode control , computer science , reinforcement learning , cyber physical system , mode (computer interface) , stability (learning theory) , control (management) , class (philosophy) , control engineering , engineering , nonlinear system , artificial intelligence , physics , quantum mechanics , operating system , machine learning
This study is concerned with the reliable and optimal control problems of data‐driven cyber‐physical systems (CPSs) against a class of actuator attacks. Consider an unknown continuous‐time linear physical system with the external disturbance, and it is assumed that control input signals transmitted via network layers are vulnerable to cyber attacks. By introducing a new integral sliding‐mode function and utilising the available data acquired by an off‐policy reinforcement learning algorithm, a novel data‐based adaptive integral sliding‐mode control strategy is presented. Different from the existing control policies, the novel one uses a data‐driven sliding‐mode compensator to eliminate the effect of the actuator attacks such that the stability and a nearly optimal performance of the CPSs can be guaranteed. Finally, the effectiveness of the proposed control strategy is verified by a numerical example.