Model Predictive Path Integral PID Control for Learning-Based Path Following

TL;DR

This paper introduces MPPI--PID control, which optimizes PID gains using model predictive path integral control to improve sample efficiency and input smoothness in learning-based path following tasks.

Contribution

It proposes a novel MPPI--PID method that replaces high-dimensional control sequence optimization with low-dimensional gain optimization, enhancing efficiency and input smoothness.

Findings

MPPI--PID improves tracking performance over fixed-gain PID.

It achieves comparable results to conventional MPPI with fewer samples.

The method maintains performance even with significantly reduced sampling.

Abstract

Classical proportional--integral--derivative (PID) control is widely employed in industrial applications; however, achieving higher performance often motivates the adoption of model predictive control (MPC). Although gradient-based methods are the standard for real-time optimization, sampling-based approaches have recently gained attention. In particular, model predictive path integral (MPPI) control enables gradient-free optimization and accommodates non-differentiable models and objective functions. However, directly sampling control input sequences may yield discontinuous inputs and increase the optimization dimensionality in proportion to the prediction horizon. This study proposes MPPI--PID control, which applies MPPI to optimize PID gains at each control step, thereby replacing direct high-dimensional input-sequence optimization with low-dimensional gain-space optimization. This formulation enhances sample efficiency and yields smoother inputs via the PID structure. We also provide theoretical insights, including an information-theoretic interpretation that unifies MPPI and MPPI--PID, an analysis of the effect of optimization dimensionality on sample efficiency, and a characterization of input continuity induced by the PID structure. The proposed method is evaluated on the learning-based path following of a mini forklift using a residual-learning dynamics model that integrates a physical model with a neural network. System identification is performed with real driving data. Numerical path-following experiments demonstrate that MPPI--PID improves tracking performance compared with fixed-gain PID and achieves performance comparable to conventional MPPI while significantly reducing input increments. Furthermore, the proposed method maintains favorable performance even with substantially fewer samples, demonstrating its improved sample efficiency.