Papers
13
Total Citations
255
H-Index
8
About
Zengjie Zhang is a leading researcher in robotics, specializing in human-robot collaboration, disturbance estimation, and safe motion planning for autonomous systems. His work bridges the gap between theoretical control methods and practical robotic applications, with a focus on enhancing robot safety and adaptability in dynamic environments. Zhang’s most impactful contribution is his 2020 paper on online collision detection and identification (CDI) for human-collaborative robots, which uses supervised learning and Bayesian decision theory to monitor sensory signals—garnering 67 citations. He also developed a novel integral sliding-mode observer for Euler-Lagrangian systems (56 citations), enabling precise external disturbance estimation without velocity measurements, a breakthrough for force-sensor-less robotic control. His adaptive incremental sliding mode control for robot manipulators (45 citations) further advances robust control in uncertain conditions. Zhang’s notable achievements include creating a high-fidelity simulation platform for industrial manufacturing (21 citations) and pioneering implicit behavior cloning with dynamic movement primitives to accelerate reinforcement learning for robot motion planning (14 citations). His recent work on distributed coverage control and safe feedback motion planning using control barrier functions demonstrates his commitment to scalable, resilient multi-agent systems. With over 240 total citations, Zhang’s research is essential reading for students and engineers advancing collaborative and autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 3Adaptive incremental sliding mode control for a robot manipulator45 citations · 2021
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