Qing Lin

Jiangsu University of Science and Technology

Papers

Total Citations

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About

Qing Lin is a leading researcher in advanced robotics and intelligent control systems, with a particular focus on parallel robot dynamics and nonlinear control theory. Their most impactful work introduces an adaptive sliding mode control method enhanced by fuzzy neural networks, addressing the critical challenge of robust trajectory tracking in complex robotic systems. This research, published in 2018 and garnering 26 citations, demonstrates how integrating sliding mode variable structure control—valued for its inherent robustness—with adaptive fuzzy neural networks can significantly improve the performance of parallel robots operating under nonlinear conditions. By bridging classical control theory with modern computational intelligence, Lin’s contributions offer practical solutions for high-precision automation in manufacturing, medical robotics, and other demanding applications. Their work stands as a notable achievement in the ongoing effort to make robotic systems more adaptive, reliable, and efficient, providing a valuable reference for students and researchers exploring the intersection of control engineering and intelligent systems.