Mutiple-gradient descent algorithm for parametric identification of a powered two-wheeled vehicles

Powered Two-Wheeled vehicles (PTWv) are an increasingly popular means of transport. The cost and the risks of the development phase of this vehicles has to be diminished in order to ensure acceptable levels of comfort and safety for riders upstream of hazardous driving situations. It is required to study motorcycle while riding in cornering and lane-keeping to interpret the dynamics behavior. Thus, we need to obtain dynamic model that tightly adjusts to the real lateral behavior of the motorcycle, in the way that it will lead to precise simulation and experimental results. A technique for cascade identification of parameters based on data and optimization algorithm, is presented here. This methodology makes profit of the possibility given by this type of algorithm for solving multiple objectives function consecutively. After the identification method is outlined, simulations and experimental results are presented in order to confirm the accuracy of the parameters estimation under the persistent condition of the inputs.