Papers
77
Total Citations
3,268
H-Index
25
About
Yongping Pan is a prominent robotics and control systems researcher whose work sits at the intersection of human-robot interaction, adaptive control, and intelligent robotics. His research has made transformative contributions to rehabilitation robotics, robot manipulator control, and compliant actuator systems, with a particular focus on making robotic systems safer and more effective for direct physical interaction with humans. Pan's most influential work addresses the critical challenge of controlling rehabilitation robots equipped with series elastic actuators (SEAs), recognizing that physical human-robot interaction fundamentally alters system dynamics and stability. His 2015 paper on this topic has accumulated over 355 citations, reflecting its foundational importance to the field. Complementing this, he has pioneered composite learning control frameworks that guarantee parameter convergence, advanced neural network-based sliding mode adaptive control, and developed personalized teleoperation strategies with tremor attenuation for operators with limited motor functionality. Notably, Pan demonstrated that straightforward PID controllers can be theoretically validated for compliant actuator-driven manipulators, bridging practical engineering and rigorous theory. Across his most-cited works alone, Pan has accumulated well over 1,900 citations, underscoring his sustained impact on advancing safe, adaptive, and high-performance robotic control for real-world human-centered applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural network-based sliding mode adaptive control for robot manipulators249 citations · 2011
- 3Composite learning robot control with guaranteed parameter convergence210 citations · 2018
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- 10Robust Sliding Mode Control for Robots Driven by Compliant Actuators115 citations · 2018