LLA-MPC: Fast Adaptive Control for Autonomous Racing

Abstract

We present Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a real-time adaptive control framework for autonomous racing that addresses the challenge of rapidly changing tire-surface interactions. Unlike existing approaches requiring substantial data collection or offline training, LLA-MPC employs parallelization over a bank of models for rapid adaptation with no training. It integrates two key mechanisms: a look-back window that uses recent vehicle behavior to optimize the model used in a look-ahead stage for trajectory optimization and control. The optimized model and its associated parameters are then incorporated into an adaptive path planner to optimize reference racing paths in real time. Experiments across diverse racing scenarios demonstrate that LLA-MPC outperforms state-of-the-art methods in adaptation speed and handling, even during sudden friction transitions. Its learning-free, computationally efficient design enables rapid adaptation, making it ideal for high-speed autonomous racing in multi-surface environments.