LLA-MPC: Fast Adaptive Control for Autonomous Racing

TL;DR

LLA-MPC is a real-time adaptive control framework for autonomous racing that quickly adjusts to changing tire-surface interactions without offline training, improving performance in diverse and sudden friction changes.

Contribution

The paper introduces LLA-MPC, a novel model predictive control approach that employs a model bank with look-back and look-ahead mechanisms for immediate adaptation without learning.

Findings

Outperforms existing methods in adaptation speed and handling.

Effective in diverse racing scenarios with sudden friction changes.

Computationally efficient and suitable for high-speed 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 a model bank for immediate adaptation without a learning period. It integrates two key mechanisms: a look-back window that evaluates recent vehicle behavior to select the most accurate model and a look-ahead horizon that optimizes trajectory planning based on the identified dynamics. The selected model and estimated friction coefficient are then incorporated into a trajectory planner to optimize reference 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.