부산대학교 도서관

Autonomous vehicle racing has emerged as vibrant, innovative technology development, demonstration platform in recent years. Universities, companies demonstrate their achievements on various vehicles - from 1:10th to full-scale prototypes. One of those platforms is the Dallara AV-21, and the spec-vehicle for the Indy Autonomous Challenge. This paper outlines the robust model predictive control (MPC) concept used within the software stack of the TUM Autonomous Motorsport team. It is based on a simplified friction-limited point mass model and a set of low-level feedback controllers. The remaining model uncertainties are managed via introducing a constraint-tightening approach based on a Tube-MPC approach. In contrast to classical tracking controllers, the optimization problem is formulated to freely optimize the trajectory while staying within certain maximum deviations of the reference. This approach allows to rely on a coarse output of the trajectory planning approach while maintaining smoothness requirements in steering, throttle, and brake actuation. The paper highlights the advantages of the proposed robust reoptimization concept compared to pure tracking formulations. It showcases the performance compared to a classical LQR controller and an MPC, which utilizes a vehicle model with a more sophisticated tire model. The controller achieved a top speed of $\text{265 kmh}^{-1}$ and lateral accelerations up to ${\text{21 ms}}^{-2}$ during a two-vehicle competition involving dynamic overtaking maneuvers on the Las Vegas Motor Speedway, a famous racetrack with turns banked up to $20^{\circ }$.