Mao Zhang
Papers
3
Total Citations
43
H-Index
3
About
Mao Zhang is a leading researcher in robotics and neural network-based control systems, with a focus on adaptive teleoperation and robot manipulator control. Their work addresses critical challenges in traditional teleoperation systems, which often suffer from high computational costs and poor portability due to reliance on specific kinematic models. Zhang’s most-cited paper, "Toward Unified Adaptive Teleoperation Based on Damping ZNN for Robot Manipulators With Unknown Kinematics" (2022, 29 citations), introduces a groundbreaking unified framework that eliminates the need for prior kinematic knowledge, significantly enhancing robustness and adaptability across diverse robotic platforms. Additionally, Zhang has advanced discrete robot manipulator control through gradient-based recursive neural networks (2020, 9 citations) and tackled the complex control of continuum robots with unknown models using gradient neural networks (2022, 5 citations). These contributions demonstrate Zhang’s expertise in developing inverse-free, computationally efficient solutions that push the boundaries of robotic dexterity and compliance. With a growing citation record, Zhang is recognized for pioneering neural network approaches that simplify and unify robot control, making their work highly influential for researchers and students in robotics, automation, and intelligent systems.
Research Focus
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Top Papers
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