  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.
