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About

Yuxiang Hu is a researcher specializing in fault detection, fault estimation, and fault-tolerant control for dynamic systems, with a particular focus on robustness against periodic disturbances. Their major contributions center on the development of novel observer-based methods, including the disturbance decoupling generalized proportional–integral observer (DD-GPIO) and the approximate input disturbance decoupling generalized proportional–integral observer (ADD-GPIO), which enable early and accurate detection of sensor and actuator faults even in the presence of external periodic disturbances. Hu’s work also extends to discrete-time systems, where they have proposed robust fault-tolerant control schemes integrating fault estimators and dynamic disturbance compensation loops to enhance the reliability of digital automation systems in the Industry 4.0 era. Their most cited paper, “A Disturbance Decoupling Generalized Proportional–Integral Observer Design for Robust Sensor Fault Detection” (2022), has garnered 23 citations, reflecting its impact on the field. More recently, Hu has pioneered the integration of machine learning with observer-based methods, as demonstrated in their 2025 work on an observer-driven temporal graph convolutional network (OD-TGCN) for early fault detection, addressing the challenge of incipient faults masked by noise.