Guangcang Wang
Papers
1
Total Citations
33
H-Index
1
About
Dr. Guangcang Wang is a leading researcher in intelligent robotics and optimal control systems, with a primary focus on the robust tracking control of mobile robots. His most cited work, "Reinforcement Learning-Based Tracking Control for a Three Mecanum Wheeled Mobile Robot" (2022, 33 citations), represents a significant contribution to the field. In this study, Dr. Wang addresses the complex challenge of controlling a three Mecanum wheeled mobile robot (MWMR) under external disturbances and wheel slipping. By establishing the Euler-Lagrange motion equation for the MWMR and applying an online actor-critic synchronous learning algorithm, he developed a robust optimal tracking control strategy that enhances stability and precision in real-world conditions. This work bridges reinforcement learning with practical robotics, offering a novel solution for autonomous navigation in slippery or disturbed environments. With 33 citations, this paper has already influenced subsequent research in adaptive control and mobile robotics. Dr. Wang’s achievements are particularly valuable for students and engineers working on wheeled robot design, offering a clear framework for integrating learning-based control with mechanical constraints. His research continues to advance the frontier of intelligent, disturbance-tolerant robotic systems.
Research Focus
Key Achievements
Top Papers
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