Yeong‐Chan Chang
Papers
18
Total Citations
555
H-Index
12
About
Yeong‐Chan Chang is a control systems researcher whose work sits at the intersection of robotics, adaptive control theory, and intelligent systems. His research focuses primarily on robust tracking control for robotic manipulators, with particular emphasis on handling plant uncertainties, external disturbances, and constrained mechanical systems. Chang has made substantial contributions to the field by developing adaptive fuzzy and neural network-based control frameworks that provide mathematically rigorous performance guarantees, including H∞ disturbance attenuation and mixed H₂/H∞ optimization — approaches that were relatively novel when introduced in the late 1990s. Among his most influential contributions is his adaptive fuzzy tracking framework for both holonomic and nonholonomic mechanical systems (2000, 117 citations), which established a unified design methodology applicable across a broad class of robotic systems. His pioneering work on neural network-based adaptive H∞ tracking control for robotic systems (1997, 87 citations) demonstrated how learning-based methods could deliver provable robust performance. Chang also made notable advances in observer-based control, addressing the practical challenge of designing effective controllers using only position measurements — eliminating the need for velocity sensors in flexible-joint robots. Across more than two decades of research, his cumulative citation record reflects sustained and meaningful impact on the robotics control community.
Research Focus
Key Achievements
Top Papers
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- 3Adaptive control in robotic systems with H∞ tracking performance67 citations · 1997
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- 10An intelligent robust tracking control for electrically-driven robot systems19 citations · 2008