Xinrui Chang
Papers
1
Total Citations
13
H-Index
1
About
Xinrui Chang is a researcher specializing in autonomous vehicle control and robotics, with a particular focus on path tracking and real-time motion planning for car-like robots. Their most notable contribution is a novel approach that integrates neural networks with nonlinear model predictive control (NMPC), addressing a critical trade-off between control accuracy and computational efficiency. By using NMPC as a learning sample to train neural networks, Chang’s work enables high-performance path tracking while overcoming the poor real-time performance traditionally associated with NMPC. This innovation, detailed in their highly cited 2022 paper (13 citations), has significant implications for the deployment of autonomous systems in dynamic environments where rapid decision-making is essential. Chang’s research bridges the gap between theoretical control methods and practical implementation, offering a scalable solution for intelligent vehicles. Their work is particularly relevant for students and researchers exploring machine learning-based control, constraint handling, or autonomous navigation, as it demonstrates how data-driven methods can enhance traditional optimization techniques in real-world robotics applications.
Research Focus
Key Achievements
Top Papers
- 1