@article{ref1,
title="A predictive neural hierarchical framework for on-line time-optimal motion planning and control of black-box vehicle models",
journal="Vehicle system dynamics",
year="2023",
author="Piccinini, Mattia and Larcher, Matteo and Pagot, Edoardo and Piscini, Davide and Pasquato, Leone and Biral, Francesco",
volume="61",
number="1",
pages="83-110",
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.<p /> <p>Language: en</p>",
language="en",
issn="0042-3114",
doi="10.1080/00423114.2022.2035776",
url="http://dx.doi.org/10.1080/00423114.2022.2035776"
}