Qiao Zhu
Papers
4
Total Citations
113
H-Index
3
About
Qiao Zhu is a leading researcher in the field of iterative learning control (ILC), with a particular focus on linear discrete-time systems. His work centers on developing advanced control schemes that improve the convergence and performance of systems operating under repetitive tasks. Zhu’s major contributions include the introduction of multiple high-order internal models (HOIMs) to handle iteration-varying references, unknown initial states, and disturbances—a significant advancement over traditional ILC methods. His most cited paper, “Iterative learning control design for linear discrete-time systems with multiple high-order internal models” (2015), has garnered 91 citations, underscoring its impact on the control theory community. In this work, he proposed a dual ILC scheme that ensures convergence in both time and iteration domains, dramatically improving convergence rates. Zhu’s subsequent studies, such as his 2017 paper on time-frequency analysis for unknown HOIMs, further expanded the applicability of ILC to more realistic, uncertain environments. His research has been instrumental in bridging theoretical control design with practical implementation, making him a notable figure in the advancement of learning-based control systems.
Research Focus
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