Deqiang Mu
Papers
2
Total Citations
49
H-Index
2
About
Deqiang Mu is a leading researcher in robotics and control systems, specializing in safe human-robot interaction and robust motion control. His work addresses critical challenges in uncertain robotic environments, including collision detection without external sensors and high-precision tracking under actuator constraints. Mu’s most influential contribution, a nonlinear momentum observer for sensorless collision detection (2021, 39 citations), enables robots to safely detect and respond to collisions in real time, a breakthrough for collaborative robotics. He further advanced the field with a neural network-based continuous finite-time tracking controller (2022, 10 citations), which ensures robust, high-precision control despite model uncertainties, external disturbances, and actuator saturation—a vital achievement for industrial automation. By integrating sliding mode control with neural networks, Mu’s methods achieve both speed and accuracy, pushing the boundaries of autonomous robotic systems. His work is widely cited by engineers and researchers developing safer, more adaptive robots for manufacturing, healthcare, and service applications, cementing his reputation as an innovator in nonlinear control and robot safety.
Research Focus
Key Achievements
Top Papers
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