Citation

Piccinini M, Larcher M, Pagot E, Piscini D, Pasquato L, Biral F. Veh. Syst. Dyn. 2023; 61(1): 83-110.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2022.2035776

PMID

unavailable

Abstract

This paper addresses the on-line minimum-time motion planning and control of a black-box racing vehicle model. We present a hierarchical control framework, composed of a high-level non-linear model predictive controller (NMPC) based on an advanced kineto-dynamical vehicle model, a low-level neural network to compute the inverse steering dynamics and a longitudinal controller for the low-level tracking of speed profiles. An off-line identification procedure, consisting of simulated manoeuvres, is defined to learn the high-level and low-level models. A closed-loop simulation is setup to control the black-box vehicle near the limits of handling along a racetrack. Simulation results are compared with the off-line solution of a minimum-time-optimal control problem.

Language: en

Keywords

Autonomous racing; identification of vehicle dynamics; model-predictive control; motion planning; neural networks