Pingping Lv
Papers
5
Total Citations
30
H-Index
3
About
Pingping Lv is a leading researcher in the field of rehabilitation robotics, specializing in the design and intelligent control of lower limb exoskeleton robots. Her work focuses on solving critical challenges in human-robot interaction, including precise gait phase detection, adaptive trajectory planning, and robust motion control under dynamic uncertainties. Lv has pioneered novel hybrid control strategies that integrate Radial Basis Function Neural Networks (RBFNN) with integral sliding mode and feedforward control, achieving smooth, continuous, and accurate trajectory tracking for exoskeletons. Her most cited paper (2024, 16 citations) introduces the PIDFF-ISMC method, which adaptively compensates for model uncertainties and disturbances. She has also developed innovative machine learning approaches, such as a Fuzzy Support Vector Machine (F-SVM) optimized by Particle Swarm Optimization (PSO) for real-time gait phase recognition using lightweight elastic pressure sensing insoles. Additionally, her work on 6-5-6 polynomial trajectory planning ensures jerk-limited, natural motion. With a cumulative citation count approaching 30, Lv’s contributions are advancing the practicality and safety of wearable robotic systems for rehabilitation and mobility assistance.
Research Focus
Key Achievements
Top Papers
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