Papers
9
Total Citations
61
H-Index
3
About
Hongbin Chang is a robotics and control systems researcher whose work sits at the intersection of assistive technology and advanced control theory. Specializing in human support robots, rehabilitation walkers, and cushion robots, Chang has dedicated his career to developing intelligent control frameworks that address the complex dynamics of mobility assistance for aging and physically impaired populations. Chang's most significant contributions center on adaptive and robust control strategies for assistive robotics. His most-cited work, "Trajectory Tracking Control of Human Support Robots via Adaptive Sliding-Mode Approach" (2023, 36 citations), introduced a nonsingular fast terminal sliding-mode controller paired with a disturbance observer — a breakthrough that substantially improved trajectory tracking precision under uncertain conditions. His earlier foundational research on omniwheel dynamics and output feedback control established critical groundwork for handling the mechanical challenges unique to sitting-type walking-assistance platforms. More recently, Chang has pioneered data-driven safety control methodologies and iterative learning control frameworks that incorporate human-machine interaction environments and velocity constraints, reflecting a growing emphasis on safe, adaptive autonomy. Across his portfolio, themes of center-of-gravity compensation, input saturation, and real-world robustness are recurrent, demonstrating a deeply applied research philosophy. His work offers meaningful contributions to the global challenge of supporting increasingly aging populations through intelligent robotic assistance.
Research Focus
Key Achievements
Top Papers
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- 7Output Feedback Control for a Human Support Robot with Inputs Constraint2 citations · 2018
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