Self-learning path-tracking control of autonomous vehicles using kernel-based approximate dynamic programming

With the fast development of robotics and intelligent vehicles, there has been much research work on modeling and motion control of autonomous vehicles. However, due to model complexity, and unknown disturbances from dynamic environment, the motion control of autonomous vehicles is still a difficult problem. In this paper, a novel self-learning path-tracking control method is proposed for a car-like robotic vehicle, where kernel-based approximate dynamic programming (ADP) is used to optimize the controller performance with little prior knowledge on vehicle dynamics. The kernel-based ADP method is a recently developed reinforcement learning algorithm called kernel least-squares policy iteration (KLSPI), which uses kernel methods with automatic feature selection in policy evaluation to get better generalization performance and learning efficiency. By using KLSPI, the lateral control performance of the robotic vehicle can be optimized in a self-learning and data-driven style. Compared with previous learning control methods, the proposed method has advantages in learning efficiency and automatic feature selection. Simulation results show that the proposed method can obtain an optimized path-tracking control policy only in a few iterations, which will be very practical for real applications.