Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
For autonomous vehicle control, this method improves robustness and smoothness over standard MPPI, though it is an incremental improvement on an existing framework.
ICODE-MPPI uses continuous-time residual learning to compensate for unmodeled dynamics in MPPI control, achieving up to 69% reduction in cross-tracking error under disturbances.
Model Predictive Path Integral (MPPI) control is a powerful sampling-based strategy for nonlinear autonomous systems. However, its performance is often bottlenecked by the fidelity of nominal dynamics. We propose ICODE-MPPI, a robust framework that leverages Input Concomitant Neural Ordinary Differential Equations (ICODEs) to learn and compensate for unmodeled residual dynamics. Unlike discrete-time learners, ICODEs maintain physical consistency and temporal continuity during the MPPI prediction horizon. High-fidelity simulations on complex trajectories demonstrate that ICODE-MPPI achieves up to a 69\% reduction in cross-tracking error under persistent disturbances compared to standard MPPI control. Furthermore, our analysis confirms that ICODE-MPPI significantly suppresses control chattering, yielding smoother steering commands and superior robust performance.