In two-wheeled vehicles the mass of the rider is a significant part of the total mass of the system and the rider influences the dynamic behavior both by means of the voluntary control actions and by means of the passive response of his body to the oscillations of the vehicle. The passive response of the rider’s body has a particular influence on roll motion, which is typical of two-wheeled vehicles. Roll oscillations generate inertia forces on the rider’s body, which moves with respect to the vehicle. Forces and torques generated by the rider on the handlebars, saddle and foot rests are different from the ones that would be generated if the body was rigidly fixed to the vehicle. Therefore, advanced simulation of two wheeled vehicles requires passive biomechanical models of the rider. This paper proposes a novel approach for the study of the passive response of the rider’s body that is based on measurements in the laboratory of the interaction forces between the rider and the vehicle. A special motorcycle mock-up is developed, it is driven by a hydraulic shaker that generates roll excitation with variable frequency. A system of load cells measures the lateral force and torque between the rider and the motorcycle mock-up. The study is carried out in the frequency domain, the passive response of rider’s body is represented by means of three frequency response functions (FRFs): lateral force FRF and torque FRF are the ratios between the lateral force/torque and the roll input; motion FRF is the ratio between the roll motion of the rider’s trunk and the roll input. The biomechanical models of the rider’s body that are developed in this work are able to simulate its response both in terms of interaction forces and motion. These models are composed by some rigid bodies with lumped stiffness and damping parameters in the articulations and in this way they represent a good compromise between accuracy and complexity. The biomechanical parameters of the models are identified by means of a genetic algorithm that aims to minimize a penalty function based on the difference between the three FRFs predicted by the model and the measured FRFs. Results show that a 5 degree of freedom model of the rider is able to represent the measured behavior both in terms of interaction forces and trunk motion.

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