Roll Angle Estimation of a Motorcycle through Inertial Measurements.
Currently, the interest in creating autonomous driving vehicles and progressively more sophisticated active safety systems is growing enormously, being a prevailing importance factor for the end user when choosing between either one or another commercial vehicle model. While four-wheelers are ahead in the adoption of these systems, the development for two-wheelers is beginning to gain importance within the sector. This makes sense, since the vulnerability for the driver is much higher in these vehicles compared to traditional four-wheelers. The particular dynamics and stability that govern the behavior of single-track vehicles (STVs) make the task of designing active control systems, such as Anti-lock Braking System (ABS) systems or active or semi-active suspension systems, particularly challenging. The roll angle can achieve high values, which greatly affects the general behavior of the vehicle. Therefore, it is a magnitude of the utmost importance; however, its accurate measurement or estimation is far from trivial. This work is based on a previous paper, in which a roll angle estimator based on the Kalman filter was presented and tested on an instrumented bicycle. In this work, a further refinement of the method is proposed, and it is tested in more challenging situations using the multibody model of a motorcycle. Moreover, an extension of the method is also presented to improve the way noise is modeled within this Kalman filter.
['Maceira|Diego|D|0000-0002-0073-6793', 'Luaces|Alberto|A|0000-0001-5118-7141', 'Lugrís|Urbano|U|0000-0002-3537-9597', 'Naya|Miguel Á|MÁ|0000-0001-7901-8278', 'Sanjurjo|Emilio|E|0000-0002-3024-911X']
D001334:Automobile Driving D009049:Motorcycles
roll angle estimator Kalman filter LQR controller inertial sensors motorcycle lean angle