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

Hassan Jardali, Ihab S. Mohamed, Durgakant Pushp, Lantao Liu

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

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

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
Paper Structure (21 sections, 14 equations, 10 figures, 2 tables)

This paper contains 21 sections, 14 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Lateral Vehicle Dynamics
  4. Single-Track Lateral Dynamic Model
  5. Dynamics Parameters Identification
  6. Path Tracking with LPV-MPC
  7. Path Tracking Problem Formulation
  8. Constrained Control Problem
  9. Experimental Validation
  10. Experimental Setup
  11. Results
  12. Tracking Errors:
  13. Dynamics and Model Errors:
  14. Computation Time:
  15. Discussion
  16. ...and 6 more sections

Figures (10)

  • Figure 1: IU-LART IAC AV-24 autonomous racing vehicle jardali2025zero.
  • Figure 2: Overview of the single-track bicycle model, highlighting relevant forces, angles, and lateral errors in their positive directions.
  • Figure 3: Measured cornering forces versus slip angles with the corresponding fitted Pacejka curve, obtained from the final IMS run at a longitudinal velocity of 56ms. Note that the vertical offset in the Pacejka model (typically denoted as $s_{hy}$) was not accounted for.
  • Figure 4: High-level software architecture of our autonomous racing system for a single-car format jardali2025zero.
  • Figure 5: Experimental results for path tracking during our highest speed lap. The highlighted areas mark the start and finish of the lap.
  • ...and 5 more figures