用神经微分方程提升车辆路径跟踪鲁棒性
Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
- 引入输入相关神经微分方程建模未建模动态
- 在持续扰动下交叉误差降低69%
- 控制指令更平滑，适合高精度自动驾驶
模型预测路径积分（MPPI）控制是应对非线性自主系统的一种强大采样方法。然而其性能常受限于基准动力学模型的准确性。本文提出一种名为ICODE-MPPI的鲁棒框架，利用输入相关神经常微分方程（ICODEs）学习并补偿未建模的残差动力学。与离散时间学习器不同，ICODEs在MPPI预测时域内保持物理一致性和时间连续性。在复杂轨迹的高保真仿真中，ICODE-MPPI在持续扰动下相比标准MPPI控制，交叉跟踪误差最高减少69%。此外，分析表明，ICODE-MPPI显著抑制了控制抖振，生成更平滑的转向指令，表现出更优的鲁棒性能。
原文摘要 · Abstract (English)
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.
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