Randomized Model Predictive Control for Autonomous Racing

Autonomous racing is an emerging research field that has gained significant interest in recent years. Controlling a vehicle at the limit of tire grip presents a challenging control problem. This paper demonstrates the implementation of a randomized optimization based Model Predictive Control (MPC), referred to as Randomized Model Predictive Control (RMPC), to address the challenge of autonomous racing. The random samples in the proposed RMPC are generated using a novel generation technique in the frequency domain. This approach prevents undesirable oscillations, which are critical to the smoothness required for vehicle control. The proposed RMPC is implemented on a Graphics Processing Unit (GPU), minimizing data transfer to the Central Processing Unit (CPU). The results demonstrate improved control performance and lap times that surpass those achievable with state-of-the-art nonlinear control methods. The proposed scheme proved fast and efficient in driving a full-scale autonomous race car built on a Dallara SF23 Superformula chassis. As part of the Abu Dhabi Autonomous Racing League (A2RL) race of 2024, the proposed implementation set the fastest autonomous lap of the series at the iconic Yas Marina Circuit, achieving a lap time within 10% of a Formula 1 driver in the human-driven version of the same car.