Yigang Wang

Nanyang Technological University, Beijing Academy of Agricultural and Forestry Sciences, Ningbo University of Technology

Papers

Total Citations

H-Index

About

Yigang Wang’s research bridges advanced control theory and intelligent robotics, with a focus on iterative learning control (ILC), agricultural automation, and industrial welding systems. His most influential work, “Simple LMI based learning control design” (2009, 18 citations), introduced a streamlined linear matrix inequality approach that guarantees monotonic error decay in ILC, validated through near-perfect tracking on a SCARA robot—a foundational contribution to precision motion control. More recently, his 2025 review on deep learning for fruit and vegetable picking robots (11 citations) synthesizes fragmented research into a cohesive vision for agricultural modernization, highlighting object detection and classification challenges. Wang also advanced industrial robotics with a novel hand-eye calibration method using structured light planes (2019, 3 citations), addressing a critical bottleneck in intelligent welding automation. His work demonstrates a clear trajectory from theoretical control design to applied robotics, impacting both manufacturing and agriculture. With a growing citation footprint, Wang’s contributions are shaping the next generation of autonomous systems, from factory floors to farms.