This article studies the finite-time adaptive resilient control problem for MIMO nonlinear switched systems with the unknown dead zone. The sensors of the controlled systems suffer from unknown false data injection (FDI) attacks so that all states cannot be directly applied to the design process of the controller. To address this negative impact of FDI attacks, a new coordinate transformation is designed in control design. Moreover, the Nussbaum gain technique is introduced to deal with the difficulty of unknown time-varying weights caused by FDI attacks. Based on the common Lyapunov function method, a finite-time resilient control algorithm is designed by employing compromised state variables, which ensures that all signals of the closed-loop systems are bounded under arbitrary switching rules even in the presence of unknown FDI attacks. Compared with the existing results, the proposed control algorithm not only enables the controlled systems to reach an equilibrium state in a finite time but also removes the assumption that the sign of the attack weights is positive. In the end, a practical simulation example proves that the designed control method is valid.
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http://dx.doi.org/10.1109/TCYB.2023.3258490 | DOI Listing |
ISA Trans
November 2024
Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Sustainable Energy Systems, KFUPM, Dhahran 31261, Saudi Arabia. Electronic address:
This paper addresses the dynamic neural networks (DNNs) based resilient leader-following consensus control of multi-agent systems (MASs) under unidentified false data injection (FDI) attacks. We have examined generic linear leader-following agents in the context of stochastic FDI attacks on the network topology. When information is sent from one agent to another, it is altered as a result of the attacks.
View Article and Find Full Text PDFHeliyon
October 2024
Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently.
View Article and Find Full Text PDFSensors (Basel)
October 2024
School of Automation, Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China.
Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks.
View Article and Find Full Text PDFThe present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem.
View Article and Find Full Text PDFRev Sci Instrum
August 2024
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.
An adaptive event-triggered security control method is proposed for networked robotic teleoperation systems subject to time-varying delays and false data injection (FDI) attacks. An event-triggered scheme is designed via the position and velocity signals of the master and slave robots, where the triggering thresholds can change adaptively with the system states. The position and velocity triggered signals and the feedback error between the operator and the environment forces are utilized to detect whether the transmitted signals suffered from FDI attacks.
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