Papers
9
Total Citations
122
H-Index
5
About
Long Teng is a leading researcher in rehabilitation and bio-inspired robotics, whose work bridges intelligent control, human–robot interaction, and assistive technologies. His primary contributions lie in developing advanced control strategies—particularly fuzzy sliding mode control and central pattern generator (CPG) algorithms—to enhance the safety, adaptability, and performance of robotic systems. Teng’s most influential work, a 2020 paper on PD-based fuzzy sliding mode control for wheelchair exoskeletons (71 citations), addresses the critical challenge of ensuring comfortable, safe human–robot collaboration during activities of daily living. He has also pioneered CPG-based locomotion control for quadruped robots, introducing methods for continuous phase adjustment and adaptive frequency to improve bio-inspired gait stability. More recently, Teng has explored soft robotic gloves for assist-as-needed rehabilitation, cable-driven robots for extended workspaces, and collaborative learning in remote robotics labs. With over 120 cumulative citations and publications spanning from 2012 to 2025, his research demonstrates sustained impact in both theoretical control design and practical assistive robotics. Teng’s work is essential reading for anyone interested in the intersection of nonlinear control, human-centered robotics, and bio-inspired locomotion.
Research Focus
Key Achievements
Top Papers
- 1PD-Based Fuzzy Sliding Mode Control of a Wheelchair Exoskeleton Robot71 citations · 2020
- 2Fuzzy Sliding Mode Control of An Upper-Limb Exoskeleton Robot12 citations · 2019
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- 4CPGs with Continuous Adjustment of Phase Difference for Locomotion Control10 citations · 2013
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- 7Design and Motion Planning of a Cable Robot Utilizing Cable Slackness2 citations · 2024
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