Papers
2
Total Citations
7
H-Index
2
About
Yang Ming is a researcher at the intersection of robotics, control theory, and agricultural automation. His work focuses on developing data-driven control strategies for systems with unknown dynamics, particularly through the use of sparse identification techniques like SINDYc. In his 2023 paper on adaptive tracking control, he advanced machine learning-based system identification, offering a more efficient alternative to neural networks that require extensive training data. This contribution has garnered 4 citations and holds promise for real-time control applications. Earlier, in 2016, Ming addressed practical challenges in agricultural robotics with a method for haze removal using dark channel prior, specifically designed for the visual system of apple harvest robots. This work, with 3 citations, demonstrates his commitment to solving real-world problems in precision agriculture. Ming’s research bridges theoretical advancements in control and practical robotic vision, making him a notable figure in the growing field of intelligent agricultural systems.
Research Focus
Key Achievements
Top Papers
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- 2