Papers
10
Total Citations
494
H-Index
7
About
Ke Fan is a multidisciplinary researcher whose work spans robotics control theory, computer vision, and biomedical engineering. He is best known for his foundational contributions to model predictive control (MPC) for nonholonomic mobile robots, particularly in developing robust frameworks that address real-world constraints such as coupled inputs, unknown dynamics, and bounded disturbances. His 2018 paper combining tube-based MPC with adaptive control for trajectory tracking has accumulated 123 citations, establishing it as a landmark contribution to the field. Fan has been especially influential in visual servoing, pioneering image-based control strategies that eliminate the need for precise camera calibration — a persistent practical challenge — as demonstrated across several highly cited works from 2016 to 2019 collectively garnering over 300 citations. His research on multi-robot formation control, leveraging neural-dynamic optimization and leader-follower architectures, has further shaped autonomous systems research. In more recent years, Fan has expanded into medical robotics and biomedical applications, including a markerless augmented reality framework for safer robot-assisted surgery and automated SARS-CoV-2 diagnostic systems, signaling a compelling evolution toward healthcare-oriented robotics with meaningful translational impact.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10