Papers
10
Total Citations
215
H-Index
7
About
Yu Zhu is a leading researcher in advanced motion control, robotics, and precision engineering, whose work bridges the gap between theoretical optimal control and practical robotic systems. His major contributions span three key areas: time-optimal control under constraints, where he developed novel solutions for triple integrator systems with input saturation and full state constraints (36 citations); hybrid energy storage systems for motor drives with high torque overload capability (72 citations); and bio-inspired locomotion control for modular quadrupedal robots using deep reinforcement learning (33 citations). Zhu’s impact is further demonstrated by his work on task space contouring error estimation for robotic manipulators (23 citations), which addresses critical challenges in industrial machining accuracy, and his innovative non-equidistant toolpath planning for robotic additive manufacturing (17 citations). He has also made notable contributions to semantic keypoint learning for autonomous door opening (12 citations) and real-time multi-axis trajectory planning (10 citations). With a career spanning from early work on robust output feedback control for flexible-joint robots (2002) to ultra-precision motion control for wafer stages (2014), Zhu’s research consistently pushes the boundaries of what robots can achieve in terms of speed, precision, and adaptability—making him a pivotal figure in modern robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 6Learning Semantic Keypoint Representations for Door Opening Manipulation12 citations · 2020
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