Zhenhua Qin
Papers
2
Total Citations
11
H-Index
2
About
Dr. Zhenhua Qin is a control systems researcher whose work focuses on advancing robotic manipulation through intelligent, nonlinear control strategies. His primary research areas include adaptive iterative learning control, terminal sliding mode control, and neural network-based approaches for uncertain robotic systems. Dr. Qin’s most notable contribution is his development of a pulse neural network–based adaptive iterative learning control method for uncertain robots (2012, 8 citations), which integrates bio-inspired neural computation with iterative learning to enhance trajectory tracking in the presence of model uncertainties. He also proposed a novel nonsingular and fast convergent terminal sliding mode controller for robotic manipulators (2011, 3 citations), introducing a new sliding surface that ensures finite-time convergence of tracking errors while avoiding the singularity problem common in conventional terminal sliding mode designs. This work also includes a fast terminal reaching law to accelerate convergence. Although his citation counts are modest, Dr. Qin’s contributions address fundamental challenges in robotic control—namely, achieving rapid, precise, and robust performance under uncertainty—making his research valuable for students and engineers working on advanced robot control systems.
Research Focus
Key Achievements
Top Papers
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