为固定翼无人机控制设计平滑轨迹优化方法，解决传统算法易振荡问题。
$π$-MPPI: A Projection-based Model Predictive Path Integral Scheme for Smooth Optimal Control of Fixed-Wing Aerial Vehicles
- 引入投影滤波器π，约束控制量及导数，实现平滑控制序列。
- 在固定翼飞行器上测试，控制更平滑且调参更简单，鲁棒性提升。
- 可插入任意MPPI框架，适配复杂系统实时控制需求。
模型预测路径积分（MPPI）是一种流行的采样型模型预测控制（MPC）算法，适用于非线性系统。它通过采样控制序列并进行平均来优化轨迹。然而，MPPI存在最优控制序列不连续的问题，导致固定翼空中车辆（FWVs）出现振荡。现有方法采用事后平滑，但无法约束控制导数。本文提出一种新方法：引入投影滤波器π，对控制样本进行最小修正，确保控制量及高阶导数有界。经滤波后的样本再用于MPPI平均，形成π-MPPI。为降低计算开销，采用神经加速的定制优化器实现投影滤波。π-MPPI可实现任意阶平滑控制序列。尽管聚焦于FWVs，该投影滤波器可嵌入任意MPPI流程。在固定翼飞行器上，π-MPPI比基线方法更易调参，性能更平滑、更鲁棒。
原文摘要 · Abstract (English)
Model Predictive Path Integral (MPPI) is a popular sampling-based Model Predictive Control (MPC) algorithm for nonlinear systems. It optimizes trajectories by sampling control sequences and averaging them. However, a key issue with MPPI is the non-smoothness of the optimal control sequence, leading to oscillations in systems like fixed-wing aerial vehicles (FWVs). Existing solutions use post-hoc smoothing, which fails to bound control derivatives. This paper introduces a new approach: we add a projection filter $π$ to minimally correct control samples, ensuring bounds on control magnitude and higher-order derivatives. The filtered samples are then averaged using MPPI, leading to our $π$-MPPI approach. We minimize computational overhead by using a neural accelerated custom optimizer for the projection filter. $π$-MPPI offers a simple way to achieve arbitrary smoothness in control sequences. While we focus on FWVs, this projection filter can be integrated into any MPPI pipeline. Applied to FWVs, $π$-MPPI is easier to tune than the baseline, resulting in smoother, more robust performance.
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