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About

Jau-Woei Perng is a leading researcher in intelligent control systems, robotics, and fault diagnosis, whose work bridges theoretical innovation with real-world applications. His most impactful research, "Multisensor Fusion Time–Frequency Analysis of Thruster Blade Fault Diagnosis Based on Deep Learning" (2022, 35 citations), addresses a critical challenge in marine robotics—detecting propulsion system anomalies to prevent costly failures. By integrating deep learning with multisensor data, Perng developed robust methods for identifying blade faults under various conditions, advancing autonomous underwater vehicle reliability. Earlier, his work on "Design of robust PI control systems based on sensitivity analysis and genetic algorithms" (2016, 16 citations) demonstrated expertise in optimizing control performance through evolutionary computation. Perng also contributed to adaptive neurocontrol for robots with unknown nonlinearities (2006, 5 citations), enabling position-only feedback control, and developed vision-based human-following robots for assisting elderly and disabled pedestrians (2012, 4 citations). His research on robotic arm object detection (2019) addresses flexible automation for modern manufacturing. With a career spanning deep learning, adaptive control, and assistive robotics, Perng’s work has practical impact in marine, industrial, and service robotics, making him a notable figure in intelligent systems engineering.