LPV-MPC for Lateral Control in Full-Scale Autonomous Racing
This addresses the problem of computational and operational constraints in autonomous racing, representing an incremental improvement with specific application to high-speed racing scenarios.
The paper tackled the challenge of selecting an optimal controller for autonomous racing by introducing a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) for lateral control, achieving stable performance at speeds exceeding 160 mph on a full-scale vehicle.
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