Papers
15
Total Citations
220
H-Index
8
About
Yongqiang Ye is a leading figure in the field of iterative learning control (ILC), with a career dedicated to improving the precision and efficiency of systems that repeat tasks—most notably industrial robots. His research focuses on frequency-domain design, filtering techniques, and multirate control, where he has made several foundational contributions. In a landmark 2006 paper (22 citations), Ye demonstrated that a negative learning gain can actually expand the learnable frequency range in P-type ILC, challenging conventional assumptions. He further advanced the field by introducing phase lead compensation to broaden the learnable frequency band (17 citations, 2004) and by developing a simple linear matrix inequality (LMI) design that ensures monotonic error decay (18 citations, 2009). His work on all-pass filtering in ILC (49 citations, 2008) and practical sampled-data implementation (33 citations, 2014) has been widely adopted. Across his most-cited papers, Ye’s contributions have earned hundreds of citations, reflecting their lasting impact on control theory and robotics. His multi-channel and cyclic pseudo-downsampled ILC designs have been validated through real-world robot experiments, cementing his reputation as a researcher who bridges rigorous theory with tangible engineering results.
Research Focus
Key Achievements
Top Papers
- 1All-pass filtering in iterative learning control49 citations · 2008
- 2
- 3
- 4Case studies of filtering techniques in multirate iterative learning control20 citations · 2014
- 5Simple LMI based learning control design18 citations · 2009
- 6Better robot tracking accuracy with phase lead compensated ILC17 citations · 2004
- 7Clean system inversion learning control law16 citations · 2005
- 8Multi-channel design for ILC with robot experiments14 citations · 2004
- 9
- 10