Rider model identification: neural networks and quasi‐LPV models. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Loiseau, Paul; Boultifat, Chaouki Nacer Eddine; Chevrel, Philippe; Claveau, Fabien; Espié, Stéphane; Mars, Franck
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 10(2020)
• Page Start: 1259
• Page End: 1264
• Publication Date: 2020-10-01
• Subjects: motorcycles -- vehicle dynamics -- driver information systems -- identification -- linear parameter varying systems -- automobiles -- road traffic control -- neurocontrollers -- nonlinear dynamical systems; quasiLPV model -- precise human rider modelling -- motorbike rider modelling -- original cybernetic rider model -- dynamic neural network -- nonlinear rider model -- car driver modelling -- nonlinear dynamics -- advanced rider assistance systems -- ARAS oriented applications -- quasilinear parameter varying rider model -- explicit speed dependence -- linear dynamics -- Rider model identification process -- instrumented motorbike; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2020.0088
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.