Papers
10
Total Citations
122
H-Index
6
About
Minglei Zhu is a robotics and control systems researcher whose work sits at the intersection of parallel robotics, advanced control theory, and human-robot interaction. His research focuses primarily on collaborative robot control, visual servoing, admittance-based control frameworks, and data-driven optimization — areas where he has made substantial and recognizable contributions to the field. Among his most impactful achievements, Zhu has pioneered sensor-based and control-based design methodologies for parallel robots, including Delta and Gough-Stewart platforms, earning 24 and 13 citations respectively for these foundational contributions. His development of vision-admittance hybrid control strategies — integrating RBFNN adaptive learning with sliding mode robust compensation — has attracted significant attention (22 citations), addressing the critical challenge of safe, precise force-position control in collaborative environments. His 2023 work on deterministic approximate dynamic programming for unknown nonlinear systems (19 citations) reflects a strong command of data-driven optimal control. More recently, Zhu has expanded into compliant force control surveys and nuclear robotics applications, demonstrating both breadth and applied relevance. With over 120 cumulative citations across a focused body of work, Zhu is an emerging voice in intelligent robot control, offering methodologies that meaningfully advance robot safety and adaptability in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Sensor-based design of a Delta parallel robot24 citations · 2022
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- 5Design of a Gough–Stewart Platform Based on Visual Servoing Controller13 citations · 2022
- 6Compliant Force Control for Robots: A Survey10 citations · 2025
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- 8Control-Based Design of a DELTA Robot6 citations · 2020
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