A Comprehensive Analysis of Iterative Learning Control for Enhanced Lateral Tracking in Autonomous Vehicles

This paper explores the application of Iterative Learning Control (ILC) in enhancing lateral tracking of autonomous vehicles. A kinematic bicycle model, tailored for precise cross-track error computation, forms the basis of an initially nonlinear state-space model, which is later linearized into a time-varying representation. This refined model complements a practical ILC framework designed to address real-world challenges, particularly the handling of unequal-length error trajectories between iterations, a common occurrence in dynamic driving environments. The ILC framework is augmented with a moving average filter (MAF) and a zero-phase filter (ZPF) to mitigate error growth and ensure stability during aggressive learning. By advancing the control law, the ILC algorithm accurately tracks error trajectories across iterations of varying lengths, marking a significant improvement over existing methods. A comprehensive stability proof is provided to ensure the robustness of the proposed approach. Simulations using actual path data illustrate that multi-gain ILC configurations achieve substantial reductions in tracking error, outperforming conventional Proportional-Derivative (PD) controllers. Additionally, the ILC framework is applied to other model-free controllers, such as Stanley and Pure Pursuit, achieving performance comparable to nonlinear model predictive controllers (NMPC), but with much lower computational time. These findings underscore the effectiveness of ILC strategies in enhancing the control of autonomous vehicle systems, particularly in real-time applications where computational efficiency is critical.