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About

Nur Uddin is a control systems and robotics researcher whose work centers on the modeling, stabilization, and autonomous navigation of two-wheeled robots (TWRs) — inherently unstable platforms that demand sophisticated control strategies to operate effectively. His research spans a diverse range of control methodologies, including Lyapunov-based control, Linear Quadratic Regulator (LQR) design with state estimation, Model Reference Adaptive Control (MRAC), and pole domination approaches, demonstrating a systematic effort to solve TWR stabilization and trajectory tracking challenges from multiple theoretical angles. His most-cited work, a Lyapunov-based control design published in 2017 with 17 citations, laid important groundwork for applying rigorous stability theory to underactuated robotic systems. Uddin has also advanced the field of adaptive and intelligent control, incorporating neural networks for system identification and developing adaptive trajectory tracking frameworks. Beyond theoretical contributions, he has shown commitment to accessible robotics education, developing a low-cost Wi-Fi-connected robot as a practical teaching aid for IoT and robotics coursework. With a body of work accumulating over 60 citations, Uddin's research offers valuable tools for engineers and students working at the intersection of control theory, robotics, and autonomous systems.