The Research Concept Vehicle (RCV) is a pure electric vehicle with four in-wheel motors and individual steering as well as camber actuators. It serves as an experimental research vehicle which is built by the Integrated Transport Research Lab (ITRL). The development of the RCV’s functionality never stops after the platform started running. In order to involve the advanced driver assistance systems and realize autonomous driving in the RCV, accurate information of vehicle dynamic states and the environment is required. In this case, based on the sensors we have on the RCV, sensor fusion and state estimation are critical to be adopted for solving this problem. The purpose of this thesis is to find appropriate estimators, define the specifications and design the corresponding logics to estimate vehicle dynamic parameters and the navigation information. The classic Kalman Filter (KF) and its extension for nonlinear systems Unscented Kalman Filter (UKF) are explained and used for solving the problem. A double-track vehicle model is implemented in the estimator for current use and further development. The results of all estimations are shown, and the mathematical evaluation of position estimates indicate that they outperform the original signals which are inputs to the sensor fusion algorithm. At last, some suggestions for further improvement are presented.
State-Estimator Design for the KTH Research Concept Vehicle
2016-01-01
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |
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