Yuchen Hu

There is a growing demand for autonomous navigation systems that can maintain reliable and comfortable trajectory tracking while operating in highly dynamic and unpredictable environments. As autonomous vehicles and mobile robots move toward practical deployment, they require control algorithms capable of adapting their behavior in real time to changing conditions. Current methods lack sufficient adaptability, creating limitations in safety, robustness, and user experience. This gap highlights the need for intelligent and adaptive control frameworks that can continuously adjust strategies to meet real-world operational requirements.

This research integrates learning-based methods with control theory to develop adaptive trajectory tracking algorithms capable of dynamically adjusting control strategies according to changing environmental and operational factors. The proposed framework seeks to optimize multiple performance metrics—such as tracking precision, stability, and comfort—while maintaining computational efficiency and feasibility for real-world deployment. The study builds on both academic advances and my prior industry experience in autonomous driving planning and control algorithms.

The resulting algorithms are expected to significantly enhance the adaptability and robustness of autonomous vehicles and mobile robots, enabling them to operate safely and smoothly in complex and unpredictable environments. This work contributes to the broader goal of making autonomous navigation systems more efficient, comfortable, and widely deployable, bridging the gap between theoretical development and practical implementation.

Existing approaches, including model-based control methods and static learning algorithms, offer strong theoretical guarantees or high adaptability, but rarely achieve both simultaneously. By combining learning-based adaptability with the structural rigor of control theory, this research aims to overcome the trade-offs of current methods and establish a balanced, scalable, and real-world applicable solution for intelligent trajectory tracking.