Mingyi Yang

About

Mingyi Yang is a robotics researcher specializing in intelligent control systems for robotic manipulators, space robots, and assistive exoskeletons. Their work focuses on addressing critical challenges in nonlinear system control, adaptive algorithms, and human-machine interaction. Yang’s most influential contribution is a self-adaptive PID control strategy based on RBF neural networks for robot manipulators (2010, 5 citations), which significantly improved adaptive ability and robustness over conventional methods. They further advanced this approach for uncertain space robot systems (2011, 2 citations), enabling precise joint and carrier control in free-floating dual-arm configurations. Notably, Yang’s innovative work on walking assistive exoskeletons (2013, 2 citations) introduced a hybrid control method that combines human-machine interaction force prediction with motion intention detection, solving the “man-in-the-loop” challenge that had long hindered exoskeleton control. This pioneering approach to perceiving and predicting intended motion through interaction forces represents a significant step toward more intuitive and responsive assistive robotics. Yang’s research bridges theoretical neural network control with practical robotic applications, demonstrating consistent focus on adaptive, intelligent systems that can handle real-world uncertainties.

Research Focus

Key Achievements

Top Papers

1. 1
   Self-Adaptive PID Control Strategy Based on RBF Neural Network for Robot Manipulator

   5 citations · 2010
2. 2
   Adaptive control based on neural network for uncertain space robot

   2 citations · 2011
3. 3
   Perceiving and predicting the intended motion with human-machine interaction force for walking assistive exoskeleton robot

   2 citations · 2013

Key Collaborators

Contact & Links