Bin Ren
Papers
9
Total Citations
120
H-Index
7
About
Bin Ren is a leading researcher in the field of advanced robotics control, specializing in trajectory tracking, human–robot cooperation, and lower limb exoskeleton systems. His work addresses critical challenges in robot manipulator and wearable exoskeleton control, including unpredictable disturbances, chattering, and dynamic model uncertainties. Ren’s major contributions include developing adaptive neural network sliding mode controllers and robust finite-time trajectory control schemes that significantly improve coordination between humans and robots. His research on gait trajectory optimization for lower limb exoskeletons, particularly for construction workers, has practical implications for enhancing safety and mobility in demanding environments. With over 120 total citations across his most-cited papers, Ren’s work on “Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators” (24 citations) and “Gait trajectory‐based interactive controller for lower limb exoskeletons” (23 citations) demonstrates his impact. He has also pioneered novel approaches like successive approximation using radial basis function neural networks and nonlinear disturbance observer-embedded controllers. Ren’s innovative hybrid adaptive control strategies and gait phase recognition systems using multi-layer perceptrons highlight his commitment to solving real-world human–robot interaction challenges, making him a notable figure in robotics and control engineering.
Research Focus
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Top Papers
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- 9A Hybrid Adaptive Control Strategy for Industrial Robotic Joints4 citations · 2019