Yiyang Chen
Papers
26
Total Citations
736
H-Index
11
About
Yiyang Chen is a control systems researcher whose work has made substantial contributions to the field of iterative learning control (ILC), a sophisticated framework for improving the performance of systems that execute repetitive tasks. His research has consistently pushed the boundaries of classical ILC theory, addressing real-world limitations such as nonuniform trial lengths, input constraints, and flexible motion profiling. His 2022 paper on optimal ILC for systems with nonuniform trial lengths has garnered an impressive 235 citations, reflecting its significance in bridging theoretical rigor with practical applicability. Chen has also advanced point-to-point ILC methodologies, enabling automatic via-point time allocation and performance optimization without requiring fully pre-specified reference trajectories — a meaningful departure from traditional approaches. His work on path-following tasks, robotic manufacturing platforms, and gantry robot verification demonstrates a strong commitment to experimental validation. Beyond ILC, Chen has contributed to autonomous mobile robot navigation using neural network-based approaches. With a cumulative citation count exceeding 700 across his top publications, his research offers both foundational insights and practical tools that continue to influence the control engineering community.
Research Focus
Key Achievements
Top Papers
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