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This paper introduces ICODE-MPPI, a novel control framework that integrates Input Concomitant Neural Ordinary Differential Equations (ICODEs) with Model Predictive Path Integral (MPPI) control to learn and compensate for unmodeled dynamics in autonomous vehicle path tracking. By using ICODEs, the framework maintains physical consistency and temporal continuity during MPPI prediction, leading to more accurate trajectory predictions. Results show ICODE-MPPI achieves up to a 69% reduction in cross-tracking error and suppresses control chattering compared to standard MPPI under persistent disturbances.
Autonomous vehicles can now stick to the plan even with disturbances, thanks to a new control method that learns and compensates for unmodeled dynamics.
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.
