Application of Sideslip Estimation Architecture to a Formula Student Prototype

Abstract

This paper describes an estimator architecture for a Formula Student Prototype, based on data from an inertial measurement unit (IMU), a global positioning system (GPS), and from the underlying dynamic model of the car. A non-linear dynamic model of the car and realistic models for the sensors are presented. The estimates of attitude, rate-gyro bias, position, velocity and sideslip are based on Kalman filtering techniques. The resulting system is validated on a Formula Student prototype and assessed given ground truth data obtained by a set of differential GPS receivers installed onboard.