Roll Angle Estimation of a Motorcycle through Inertial Measurements
| Autor: | Alberto Luaces, Diego Maceira, Urbano Lugrís, Emilio Sanjurjo, Miguel Ángel Naya |
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| Rok vydání: | 2021 |
| Předmět: |
Automobile Driving
Inertial frame of reference Computer science Stability (learning theory) TP1-1185 Inertial sensors roll angle estimator Biochemistry Article Analytical Chemistry Inertial measurement unit Electrical and Electronic Engineering Motorcycle lean angle motorcycle lean angle Instrumentation Vulnerability (computing) LQR controller Roll angle estimator End user Chemical technology Estimator Control engineering Kalman filter inertial sensors Atomic and Molecular Physics and Optics Noise Motorcycles |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 6626, p 6626 (2021) Sensors Volume 21 Issue 19 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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