Sanjurjo, EmilioNaya, Miguel A.Cuadrado, JavierSchwab, Arend L.2025-11-042025-11-042018-12-04Sanjurjo, E., Naya, M. A., Cuadrado, J., & Schwab, A. L. (2018). Roll angle estimator based on angular rate measurements for bicycles. Vehicle System Dynamics, 57(11), 1705–1719. https://doi.org/10.1080/00423114.2018.15515541744-5159https://hdl.handle.net/2183/46260This is an Accepted Manuscript version of the following article, accepted for publication in Vehicle System Dynamics: Sanjurjo, E., Naya, M. A., Cuadrado, J., & Schwab, A. L. (2018). Roll angle estimator based on angular rate measurements for bicycles. Vehicle System Dynamics, 57(11), 1705–1719. https://doi.org/10.1080/00423114.2018.1551554. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.[Abstract] Measuring the roll angle of single-track vehicles has always been a challenging task; however, accurate and reliable measurements of this magnitude are paramount for controlling the stability of these vehicles, both for autonomous riding and for safety reasons. A roll angle estimation is also useful in other situations, such as tests to perform the identification of the parameters of the rider control. In this work, a new algorithm is presented for estimating the roll angle of bicycles. This estimator, based on the well-known Kalman filter, employs a wheel speed sensor to approximate the speed of the vehicle, and three angular rate sensors, which are currently small and affordable sensors. The proposed method was implemented in a microcontroller and tested in a bicycle and the results were compared with measurements obtained with optical sensors, showing a good correlation. Although it has not been tested in motorcycles, comparable results are expected.engAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Roll angle estimatorBicycleSingle-track vehicleKalman filterInertial sensorsjournal articleopen accesshttps://doi.org/10.1080/00423114.2018.1551554