Adaptive model-based velocity control by a robotic driver for vehicles on roller dynamometers

This paper presents an adaptation algorithm for a model-based velocity control of a vehicle driven by a robotic driver. In order to ensure that a robotic driver follows a desired velocity trajectory with an arbitrary vehicle, the overlaid controls must be robust as well as highly accurate. Controllers of existing robotic drivers must be adjusted manually or several learning cycles have to be driven. As each learning cycle is very time-consuming and the vehicle has to be conditioned again, a self-adaptation of the applied controls during normal cycle driving is proposed in this paper. This adaptive control depends only on little previous knowledge for initialization and yields a significant improvement of the accuracy already after a short time of driving. Results of the adaptive control both from simulations and from measurements on a roller dynamometer are shown.