LPV-MPC for Lateral Control in Full-Scale Autonomous Racing

TL;DR

This paper presents an LPV-MPC controller for autonomous racing that ensures stable lateral control at high speeds, with detailed design methodology and real-world race results demonstrating its effectiveness.

Contribution

Introduces a novel LPV-MPC framework for high-speed autonomous racing lateral control, including design, parameter extraction, and implementation insights.

Findings

Stable control at speeds over 160 mph achieved

Controller successfully used in a real race scenario

Open-source Python implementation available

Abstract

Autonomous racing has attracted significant attention recently, presenting challenges in selecting an optimal controller that operates within the onboard system's computational limits and meets operational constraints such as limited track time and high costs. This paper introduces a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) for lateral control. Implemented on an IAC AV-24, the controller achieved stable performance at speeds exceeding 160 mph (71.5 m/s). We detail the controller design, the methodology for extracting model parameters, and key system-level and implementation considerations. Additionally, we report results from our final race run, providing a comprehensive analysis of both vehicle dynamics and controller performance. A Python implementation of the framework is available at: https://tinyurl.com/LPV-MPC-acados