Duan Zhang
Papers
3
Total Citations
15
H-Index
3
About
Duan Zhang is a researcher specializing in advanced control strategies for robotic systems, with a focus on adaptive iterative learning control and sliding mode techniques. Their work addresses critical challenges in robotics, including uncertainty, external disturbances, and environmental constraints. Zhang’s most-cited paper, “Pulse neural network–based adaptive iterative learning control for uncertain robots” (2012, 8 citations), introduces a novel approach that combines neural networks with iterative learning to enhance robot adaptability. Another key contribution, “Adaptive iterative learning control of robot manipulators in the presence of environmental constraint” (2012, 4 citations), proposes a dual-domain estimation method—estimating uncertain parameters in the time domain while compensating for repetitive disturbances in the iteration domain—significantly improving performance under constraints. Additionally, Zhang’s work on “Nonsingular and fast convergent terminal sliding mode control of robotic manipulators” (2011, 3 citations) develops a new sliding surface that ensures finite-time convergence without singularities, advancing the field of robust control. With a total of 15 citations across these foundational papers, Zhang’s research has laid important groundwork for intelligent, constraint-aware robotic control, making their contributions valuable for students and researchers in robotics and automation.
Research Focus
Key Achievements
Top Papers
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