Papers
9
Total Citations
457
H-Index
7
About
Qinmin Yang is a leading researcher in intelligent control systems, reinforcement learning, and robotics, with a particular focus on nonlinear systems and autonomous navigation. His most influential work includes pioneering reinforcement learning-based adaptive critic controllers for affine nonlinear discrete-time systems, which have garnered 188 citations for their innovative use of online approximators to handle bounded disturbances. Yang has also made significant contributions to nanorobotics, with a highly cited 2016 review on nanorobotic manipulation inside scanning electron microscopes (184 citations), highlighting advances in real-time imaging and direct interaction with nanoscale samples. His research extends to multi-robot localization, where he developed a Monte Carlo method using grid cells and characteristic particles, and to adaptive filtering for mobile robots subject to multiplicative noise. More recently, Yang has explored event-triggered control for nonaffine uncertain systems and decentralized temporal-difference learning for multi-agent reinforcement learning, demonstrating his ongoing impact in both theoretical and applied domains. With over 450 total citations across his publications, Yang’s work bridges control theory, robotics, and machine learning, offering practical solutions for autonomous systems in challenging environments.
Research Focus
Key Achievements
Top Papers
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- 2Recent advances in nanorobotic manipulation inside scanning electron microscopes184 citations · 2016
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- 9A histogram sensor fusion method for mobile robots5 citations · 2005