Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching...

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Autores principales:	Dániel Fényes, Tamás Hegedus, Balázs Németh, Péter Gáspár
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	vehicle control model-matching robust control neural networks Technology T
Acceso en línea:	https://doaj.org/article/55abd812283943cab89d2a4bf439e01c
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spelling	oai:doaj.org-article:55abd812283943cab89d2a4bf439e01c2021-11-11T16:09:28ZRobust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach10.3390/en142174381996-1073https://doaj.org/article/55abd812283943cab89d2a4bf439e01c2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7438https://doaj.org/toc/1996-1073In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">H</mi><mo>∞</mo></msub></semantics></math></inline-formula> is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker.Dániel FényesTamás HegedusBalázs NémethPéter GáspárMDPI AGarticlevehicle controlmodel-matchingrobust controlneural networksTechnologyTENEnergies, Vol 14, Iss 7438, p 7438 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vehicle control model-matching robust control neural networks Technology T
spellingShingle	vehicle control model-matching robust control neural networks Technology T Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
description	In this paper, a novel neural network-based robust control method is presented for a vehicle-oriented problem, in which the main goal is to ensure stable motion of the vehicle under critical circumstances. The proposed method can be divided into two main steps. In the first step, the model matching algorithm is proposed, which can adjust the nonlinear dynamics of the controlled system to a nominal, linear model. The aim of model matching is to eliminate the effects of the nonlinearities and uncertainties of the system to increase the performances of the closed-loop system. The model matching process results in an additional control input, which is computed by a neural network during the operation of the control system. Furthermore, in the second step, a robust <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">H</mi><mo>∞</mo></msub></semantics></math></inline-formula> is designed, which has double purposes: to handle the fitting error of the neural network and ensure the accurate tracking of the reference signal. The operation and efficiency of the proposed control algorithm are investigated through a complex test scenario, which is performed in the high-fidelity vehicle dynamics simulation software, CarMaker.
format	article
author	Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár
author_facet	Dániel Fényes Tamás Hegedus Balázs Németh Péter Gáspár
author_sort	Dániel Fényes
title	Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
title_short	Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
title_full	Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
title_fullStr	Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
title_full_unstemmed	Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach
title_sort	robust control design for autonomous vehicles using neural network-based model-matching approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/55abd812283943cab89d2a4bf439e01c
work_keys_str_mv	AT danielfenyes robustcontroldesignforautonomousvehiclesusingneuralnetworkbasedmodelmatchingapproach AT tamashegedus robustcontroldesignforautonomousvehiclesusingneuralnetworkbasedmodelmatchingapproach AT balazsnemeth robustcontroldesignforautonomousvehiclesusingneuralnetworkbasedmodelmatchingapproach AT petergaspar robustcontroldesignforautonomousvehiclesusingneuralnetworkbasedmodelmatchingapproach
_version_	1718432413352198144

Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

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