Ming-Han Chung
Papers
1
Total Citations
2
H-Index
1
About
Ming-Han Chung is a robotics researcher whose work centers on intelligent control systems and visual servoing for robotic manipulators. His key contributions lie in the intersection of computer vision and adaptive control, particularly through the development of advanced proportional controllers that enhance robot precision during image-based tasks. In his most cited work, "Image Based Visual Servoing Using Proportional Controller with Compensator" (2015), Chung introduced a novel approach that integrates a fuzzy cerebellar model articulation controller within a Takagi-Sugeno framework, paired with a compensator to improve system stability and accuracy. This research addresses critical challenges in real-time visual feedback control, enabling robots to better interpret and respond to visual data during manipulation tasks. While his citation count remains modest, Chung's work demonstrates foundational thinking in adaptive visual servoing—a field essential for applications ranging from automated manufacturing to assistive robotics. His emphasis on system identification and compensator design offers practical pathways for enhancing robotic autonomy, making his contributions valuable for researchers exploring robust, vision-guided control systems.
Research Focus
Key Achievements
Top Papers
- 1