In this paper, the authors investigate the issue of constructing and incorporating an accurate vehicle dynamic model for model predictive control (MPC) with an application to autonomous vehicle path following. To achieve the desired performance, MPC employs a precise dynamic model. However, the uncertainty of complex systems and their operating environments presents a challenge to the development of an adequately accurate vehicle dynamic model. This paper proposes a Deep Gaussian Process Regression (DGPR) method to improve model precision. Meanwhile, the learning model is incorporated into a novel MPC framework to enhance closed‐loop performance. High‐fidelity simulations using CarSim‐MATLAB demonstrate the validity of the proposed approach in terms of enhancing the path following performance and lateral stability under the condition of large curvature at medium to high speeds on roads with different friction coefficients when compared to the nominal MPC approach.
DGPR‐MPC: Learning‐based model predictive controller for autonomous vehicle path following
IET Intelligent Transport Systems ; 17 , 10 ; 1992-2003
2023-10-01
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DGPR‐MPC: Learning‐based model predictive controller for autonomous vehicle path following
DOAJ | 2023
|A Predictive Controller for Autonomous Vehicle Path Tracking
Online Contents | 2009
|Path Following Control of Autonomous Underwater Vehicle Using Nonlinear Model Predictive Control
British Library Conference Proceedings | 2020
|Taylor & Francis Verlag | 2022
|