Papers
2
Total Citations
40
H-Index
2
About
Yaowen Ge is a rising researcher in the field of intelligent control systems, with a primary focus on reinforcement learning-based adaptive control for uncertain nonlinear and mechanical systems. His work addresses critical challenges in real-world engineering, such as unmodeled dynamics, time-varying disturbances, and the need for asymptotic tracking performance. In his highly cited 2023 paper, "Disturbance observer based actor-critic learning control for uncertain nonlinear systems," Ge pioneered the integration of a filter-based design with actor-critic architectures, achieving robust control against complex uncertainties—a contribution that has already garnered 28 citations. His subsequent work, "Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking," extends these principles to Euler-Lagrange systems, which model a wide array of engineering platforms including robotic manipulators and vehicular systems. With 12 citations, this paper demonstrates his ability to bridge theoretical learning algorithms with practical applications. Ge’s research is notable for its rigorous approach to ensuring stability and precision, making his methods highly relevant for next-generation autonomous systems. His growing citation record underscores his impact on the control theory community.
Research Focus
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