Yuhong Tang
Papers
1
Total Citations
18
H-Index
1
About
Dr. Yuhong Tang is a leading researcher in nonlinear control systems and reinforcement learning, whose work addresses critical challenges in autonomous decision-making under uncertainty. Her most cited paper, "Robust tracking control with reinforcement learning for nonlinear‐constrained systems" (2022, 18 citations), introduces a novel framework that integrates discounted value functions with augmented system dynamics to achieve robust tracking despite asymmetric input constraints. This contribution is pivotal for applications in robotics, aerospace, and industrial automation, where systems must operate reliably under physical limitations. Dr. Tang’s research uniquely bridges model-free reinforcement learning and robust control theory, offering computationally efficient solutions for complex, real-world constrained systems. Her work has garnered attention for its practical impact, providing a foundation for adaptive controllers that learn optimal policies while guaranteeing stability. Beyond this flagship study, her broader portfolio explores adaptive dynamic programming and optimal control for nonlinear systems, consistently pushing the boundaries of intelligent automation. For students and researchers, Dr. Tang’s contributions exemplify how theoretical advances in control theory can directly translate into safer, more efficient autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1