Guang‐Da Hu
Papers
2
Total Citations
107
H-Index
2
About
Guang-Da Hu is a leading researcher in iterative learning control (ILC) and adaptive repetitive learning control (RLC) for discrete-time systems. His work focuses on overcoming periodic uncertainties and tracking challenges in nonlinear systems, particularly through the design of high-order internal models and backstepping techniques. His most influential paper, "Iterative learning control design for linear discrete-time systems with multiple high-order internal models" (2015), has garnered 91 citations, establishing a foundational framework for improving convergence and robustness in ILC. In another key contribution, "Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties" (2014), Hu introduced an innovative approach to handle parametric uncertainty and external disturbances with known periodicity, achieving precise tracking without requiring full system knowledge. This work has been cited 16 times and is recognized for advancing RLC theory in discrete-time domains. Hu’s research is highly relevant to robotics, manufacturing, and biomedical applications where repetitive tasks and periodic disturbances are common. His contributions have shaped modern adaptive learning control, making him a respected figure in the control systems community.
Research Focus
Key Achievements
Top Papers
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