A Delay Compensation Framework for Predicting Heading in Teleoperated Ground Vehicles

Abstract

A delay compensation framework for teleoperated ground vehicles is developed in this paper that aims to achieve an improved prediction of vehicle heading. The framework seeks to combine the performance benefits of a model-based feedforward method with the robustness benefits of a model-free prediction scheme through a blended architecture. In particular, the feedforward heading signal from a second-order steering model is combined with the feedback heading signal from a model-free predictor. While leveraging this model-free predictor from the literature, a generic procedure is also developed for designing its gain and the predictor is further augmented with a saturation and resetting scheme to improve its transient performance. The vehicle heading prediction with the blended architecture is evaluated in two open-loop case studies and it is shown that blending the outputs of these model-based and model-free approaches can yield a more accurate prediction than either approach alone.