Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

Lu, Weifan, Yin, Xiuxia, Fu, Yichuan and Gao, Zhiwei (2020) Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks. Sensors, 20 (23). p. 6866. ISSN 1424-8220

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This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme’s defense against DoS attacks.

Item Type: Article
Uncontrolled Keywords: event-triggered control; static observer; DoS attack; predictive control; compensation
Subjects: G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 01 Dec 2020 14:48
Last Modified: 31 Jul 2021 14:01

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