Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing

Abstract

This work presents a novel Learning Model Predictive Control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in high-speed operational domains. We start from existing LMPC formulations and modify the system dynamics learning method. In particular, our approach uses a nominal, global, nonlinear, physics-based model with a local, linear, data-driven learning of the error dynamics. We conducted experiments in simulation and on 1/10th scale hardware, and deployed the proposed LMPC on a full-scale autonomous race car used in the Indy Autonomous Challenge (IAC) with closed loop experiments at the Putnam Park Road Course in Indiana, USA. The results show that the proposed control policy exhibits improved robustness to parameter tuning and data scarcity. Incremental and safety-aware exploration toward the limit of handling and iterative learning of the vehicle dynamics in high-speed domains is observed both in simulations and experiments.

Accepted by ICRA 2024

References in corpus (4)

• TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
• Robust adaptive MPC using control contraction metrics
• Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing
• Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes

Cited by in corpus (4)

• Autonomous Driving Small-Scale Cars: A Survey of Recent Development
• Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
• Longitudinal Control for Autonomous Racing with Combustion Engine Vehicles
• A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7