Lantao Xing
Papers
1
Total Citations
4
H-Index
1
About
Lantao Xing is a researcher whose work bridges the gap between classical control theory and modern artificial intelligence, with a primary focus on reinforcement learning (RL) for industrial process control. His most-cited paper, "On the Combination of PID control and Reinforcement Learning: A Case Study with Water Tank System" (2021, 4 citations), exemplifies his key contribution: demonstrating how RL can be practically integrated with traditional PID controllers to enhance performance in real-world systems. By addressing the critical challenge of RL’s limited adoption in industrial settings—where reliability and simplicity are paramount—Xing’s work provides a foundational framework for merging data-driven learning with established control methods. This case study, though modest in citation count, highlights his commitment to translating cutting-edge AI techniques into tangible engineering solutions. His research interests lie at the intersection of adaptive control, reinforcement learning, and process automation, aiming to make intelligent control more accessible and robust for applications like water tank systems. Xing’s work is particularly notable for its practical orientation, offering a pathway for RL to move beyond simulation and into real industrial environments, where it can improve efficiency and adaptability.
Research Focus
Key Achievements
Top Papers
- 1