Open AccessRider model identification: neural networks and quasi‐LPV models
Author(s) -
Loiseau Paul,
Boultifat Chaouki Nacer Eddine,
Chevrel Philippe,
Claveau Fabien,
Espié Stéphane,
Mars Franck
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0088
Subject(s) - identification (biology) , process (computing) , artificial neural network , computer science , cybernetics , system identification , free rider problem , control engineering , control theory (sociology) , engineering , artificial intelligence , data modeling , economics , biology , microeconomics , operating system , control (management) , botany , public good , database
The current development of Advanced Rider Assistance Systems (ARAS) would interestingly benefit from precise human rider modelling. Unfortunately, important questions related to motorbike rider modelling remain unanswered. The goal of this study is to propose an original cybernetic rider model suitable for ARAS oriented applications. The identification process is based on experimental data recorded in real driving conditions with an instrumented motorbike. Starting with a dynamic neural network, the proposed methodology firstly presents a non‐linear rider model. The analysis of this model and some analogies with car driver modelling allow to deduce a quasi‐linear parameter varying (quasi‐LPV) rider model with explicit speed dependence and a clear distinction between linear and non‐linear dynamics. This quasi‐LPV model is further analysed and simplified and finally leads to a rider model with a reduced number of parameters and nice prediction capabilities. Such a model opens up interesting perspectives for the improvement of rider assistances.