Zhong-Liang Tang
Papers
2
Total Citations
92
H-Index
2
About
Zhong-Liang Tang is a robotics and control systems researcher whose work focuses on adaptive neural control, robotic manipulators, and constrained motion planning — areas critical to the advancement of safe and reliable human-robot interaction. Tang's research addresses one of the most persistent challenges in robotics: designing controllers that remain effective despite uncertainties in both manipulator dynamics and actuator dynamics, while simultaneously respecting physical and safety constraints on joint motion. His most influential contribution, "Adaptive neural control for an uncertain robotic manipulator with joint space constraints" (2015), has garnered 89 citations and demonstrates his innovative use of neural networks to handle system uncertainties while enforcing joint angle boundaries through constrained control frameworks. This work represents a significant step forward in making robotic systems safer and more practically deployable in real-world environments. Complementing this, his research on task-space constrained control employs advanced mathematical tools such as the integral Barrier Lyapunov Functional to rigorously guarantee constraint satisfaction during operation. Tang's contributions sit at the intersection of adaptive control theory, neural network approximation, and robot safety — making his work particularly relevant to researchers and engineers developing next-generation collaborative and assistive robotic systems.
Research Focus
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Top Papers
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