Qinghua Su
Papers
2
Total Citations
7
H-Index
2
About
Qinghua Su is a leading researcher in robotics and intelligent control systems, with a primary focus on dynamic parameter identification and adaptive learning for robotic manipulators. His major contributions lie in developing real-time, data-driven approaches that address critical challenges in uncertain and dynamic environments. Su’s work on online dynamic parameter identification introduces reformulated physical feasibility constraints, enabling accurate and stable parameter estimation for manipulators operating in real-world conditions—a significant advance over traditional offline methods. His research on incremental lifelong learning control, utilizing self-evolving interval type-2 fuzzy systems, further pushes the boundaries of adaptive robotics by allowing manipulators to continuously learn and improve from streaming data without catastrophic forgetting. With his most-cited papers already accumulating citations in 2025, Su’s impact is rapidly growing within the robotics community. His notable achievements include pioneering frameworks that bridge the gap between theoretical control algorithms and practical deployment in dynamic settings, making his work essential reading for students and researchers interested in next-generation robotic autonomy and intelligent control.
Research Focus
Key Achievements
Top Papers
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