About

Pyung Hun Chang is a distinguished robotics and control systems researcher whose work has fundamentally advanced the field of robot manipulator control. His research centers on three interconnected areas: time-delay estimation (TDE)-based control, sliding mode control, and robot kinematics, with particular emphasis on making robot control simultaneously robust, accurate, and practical. Chang's most influential contribution lies in developing and refining time-delay control (TDC) methodologies, which elegantly bypass the need for precise robot dynamic models by estimating uncertainties using immediately preceding system data. His 2009 paper on nonsingular terminal sliding-mode control combined with TDE (422 citations) exemplifies this approach, achieving fast convergence and high-accuracy tracking without requiring prior knowledge of robot dynamics. This work, alongside his adaptive integral sliding mode framework (315 citations), has become foundational reading in robust robot control. Beyond single-arm systems, Chang extended his expertise to dual-arm robotics, proposing relative impedance control for asymmetric bimanual tasks (155 citations) and resolving the long-standing accuracy-robustness dilemma in impedance control (122 citations). His early contribution on closed-form inverse kinematics for redundant manipulators (194 citations) demonstrated lasting theoretical depth. With over 1,900 cumulative citations across his top works, Chang's research has profoundly shaped modern robust robot control design.

Research Focus

Key Achievements

25
H-Index
60
Papers
2,921
Total Citations
49
Avg Citations/Paper
🏆 Most Cited Paper
Practical Nonsingular Terminal Sliding-Mode Control of Robot Manipulators for High-Accuracy Tracking Control
422 citations · 2009
📈 Most Prolific Year: 2017 (7 Papers)
🤝 Key Collaborators: 63
🏛 Institutions: Korea Advanced Institute of Science and Technology, Daegu Gyeongbuk Institute of Science and Technology, Massachusetts Institute of Technology, Université de technologie de belfort-montbéliard

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 33 days ago