Accurate tire slip estimation might be regarded as a small portion of the vehicle safety but is important criterion. Furthermore, as an autonomous vehicle system gets sophisticated, this type of technique will be more necessary. In this paper performance analysis for slip estimation in various situations is presented with several commonly known filter- extended Kalman filter (EKF) and unscented Kalman filter (UKF). Tire slip behaves differently depending on the surface type, the motion of the robot, and other environmental factors. Therefore, different kinds of situations and conditions are considered to estimate more accurate tire slip. Also as far as the tire slip is not based on actual data, it will be assumed to imitate the real tire slip behavior based on other study data. Finally, the performances of two filtering algorithms are compared to find more adequate algorithm with respect to the given condition for the future experimental results.
|Journal||MATEC Web of Conferences|
|Publication status||Published - 2016 Aug 11|
|Event||2016 3rd International Conference on Manufacturing and Industrial Technologies, ICMIT 2016 - Istanbul, Turkey|
Duration: 2016 May 25 → 2016 May 27
Bibliographical notePublisher Copyright:
© The Authors, published by EDP Sciences, 2016.
All Science Journal Classification (ASJC) codes
- Materials Science(all)