Yingjie Gong
Papers
1
Total Citations
10
H-Index
1
About
Yingjie Gong is a rising scholar in advanced control systems, whose research focuses on iterative learning control (ILC), sliding mode control (SMC), and disturbance rejection for linear systems. His most-cited work, “ILC-Based Tracking Control for Linear Systems With External Disturbances via an SMC Scheme” (2024, 10 citations), addresses a fundamental challenge in control theory: achieving both high-precision tracking and rapid convergence in repetitive processes. By integrating ILC’s learning capability with SMC’s robustness, Gong proposes a novel framework that effectively suppresses external disturbances while accelerating convergence speed—a critical advancement for applications in robotics, manufacturing, and precision automation. His contributions bridge the gap between theoretical control design and practical implementation, offering a systematic approach to improve performance under real-world uncertainties. Though early in his career, Gong’s work has already garnered attention for its clarity and potential impact, marking him as a promising researcher in the field. His ongoing efforts continue to push the boundaries of learning-based control, aiming to deliver faster, more reliable solutions for complex dynamic systems.
Research Focus
Key Achievements
Top Papers
- 1