Direct Data-Driven Trajectory Tracking Control for Autonomous Vehicles via Algebraic Regulator Under Limited Data

In autonomous vehicle trajectory tracking, the absence of an accurate vehicle dynamics model can significantly degrade tracking performance. While model-free approaches, such as neural network-based supervised learning and deep reinforcement learning, have shown promising results, they typically require large-scale datasets and long-term training to ensure control convergence and generalizability. To overcome these challenges, we propose the direct data-driven trajectory tracking algebraic regulator, a model-free control framework designed to achieve high-precision trajectory tracking under limited data availability. The tracking problem is first reformulated as an output regulation problem involving an…

• NN
  National Natural Science Foundation of China

  Awards: U25A20419, 52502513, 52372417