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This article studies the adaptive fuzzy asynchronous (AFA) stabilization of discrete networked hidden stochastic semi-Markovian switching power systems under cyber attacks. Due to the complex network environment, cyber-attacks are taken into account, in which the fuzzy logic rule is adopted to describe the unknown deception attacks. Considering the mismatch mechanism between the controller and the system, an adaptive fuzzy controller runs asynchronously with the system, where the hidden semi-Markovian model is used to characterize the asynchronous mechanism. Based on the detected mode and the fuzzy logic rule, an AFA stabilizing controller is designed for the underlying system. Using the stochastic Lyapunov function related to the detected mode and system mode, sufficient criteria are given for the AFA controller design, ensuring that the underlying system is bounded stable in the mean square. Finally, the proposed scheme is verified by the simulated example.
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http://dx.doi.org/10.1109/TCYB.2025.3567360 | DOI Listing |
ISA Trans
August 2025
Department of Vehicle Engineering and Jiangsu Engineering Research Center of Vehicle Distributed Drive and Intelligent Wire Control Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Department of Vehicle Engineering and Jiangsu Engineering Research Center of Vehi
The steer-by-wire (SbW) system, as the core component of vehicle steering, needs to track the front wheel angle accurately. To mitigate the angle tracking accuracy degradation caused by D-Q axes coupling, time-varying motor electrical parameters, and load disturbance, a fractional-order adaptive fuzzy decentralized tracking control (FAFDTC) strategy is proposed in this paper. First, considering time-varying motor parameters, D-Q axes coupling, and fractional-order characteristics of components, a fractional-order SbW interconnected system is constructed to enhance its ability to characterize nonlinearities, time-varying dynamics, and system coupling.
View Article and Find Full Text PDFElectromagn Biol Med
September 2025
Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Coimbatore, India.
Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications.
View Article and Find Full Text PDFISA Trans
August 2025
Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan 430081, China; Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China; School of Artifitial Intelligence and Automation, Wuhan U
As a critical component in hydropower systems, the Hydraulic Turbine Regulation System (HTRS) exhibits strong coupling characteristics that impose substantial challenges on control system design, necessitating the development of high-performance control strategies. To address the complex control requirements, this paper proposes an improved T-S fuzzy modeling method based on the Luenberger observer theory. It constructs a system model that combines high accuracy and simplicity.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
Department of Education, Fuzhou University of International Studies and Trade, Fuzhou, China.
This study explores the integration of traditional Chinese "Fu" culture into the moral education system for students with disabilities across K-12 and higher education through artificial intelligence. By leveraging soft computing to handle cultural ambiguities, it constructs an adaptive educational framework that aligns students' cognitive characteristics with curriculum demands, thereby enhancing their identification with Chinese culture. Guided by the theory of the "Second Combination," the research employs AI-powered soft computing to analyze the semantic and cognitive dimensions of "Fu" culture.
View Article and Find Full Text PDFSci Rep
September 2025
Fukushima Renewable Energy Institute, AIST (FREA), Koriyama, 963-0298, Japan.
This article demonstrates maximum power point tracking (MPPT) using a DC-DC boost converter. It introduces an intelligent control technique with fuzzy-based pattern search (PS) optimization for the MPPT controller, enhancing energy conversion efficiency. The fuzzy-PS approach is further refined with PA optimization.
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