Rider model identification: neural networks and quasi-LPV models

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 the present paper 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 model opens up interesting perspectives for the improvement of rider assistances.