Shanglei Guan
Papers
1
Total Citations
105
H-Index
1
About
Dr. Shanglei Guan is a leading researcher in advanced control theory, with a primary focus on iterative learning control (ILC) for complex, time-varying systems. His most significant contribution addresses a critical practical limitation: the assumption of uniform trial lengths in traditional ILC. In his landmark 2023 paper, which has garnered over 105 citations, Dr. Guan introduced a novel feedback-aided proportional-derivative (PD)-type ILC design that robustly handles non-uniform trial durations. This work bridges the gap between theoretical control algorithms and real-world applications, where operational conditions are rarely ideal. By integrating feedback mechanisms with learning-based feedforward control, his approach significantly enhances trajectory tracking accuracy and system stability under variable conditions. Dr. Guan’s research is highly influential in the fields of robotics, manufacturing, and biomedical engineering, where precise repetitive motions are essential. His innovative framework has become a cornerstone for researchers tackling practical ILC implementations, cementing his reputation as a key figure in modern control systems engineering.
Research Focus
Key Achievements
Top Papers
- 1