A new application of the Extended Kalman Filter to the estimation of roll angles of a motorcycle with Inertial Measurement Unit

Abstract

The capability of providing a real-time reliable measure of the actual roll angle is of great importance for the racing motorcycle dynamics. This study presents a method based on an Extended Kalman Filter (EKF) for the estimation of the actual roll angle of a motorcycle equipped with an Inertial Measurement Unit (IMU), with the advantage of not needing the development and implementation of a complete motorcycle model. Measured data which, depending on the additional instrumentation available on the motorcycle, do not form a complete set of input data for the estimation algorithm, induce the introduction of approximations affecting the accuracy of the results. A thorough error analysis is carried out by means of numerical simulations along with experimental validations. As a result, a novel approximate form for the kinematical model of the IMU is developed, yielding an overall good accuracy in the roll angle estimates.

Keywords

• motorcycle kinematics
• motorcycle dynamics
• roll angle estimation
• real time estimation
• extended kalman filter