Learning-Based Uncertainty-Aware Predictive Control of Truck-Trailer Systems in Rough Terrain

This paper presents a model predictive controller for truck-trailer systems in off-road environments that takes into account uncertainties of the employed vehicle model. Model predictive controllers are widely used in the field of truck-trailer systems, as they enable to plan complex maneuvers, account for obstacles and consider system limits. A common model is derived from the kinematics of the vehicle. This model is based on the assumptions of flat surfaces and no slip. Driving in harsh environments, however, these assumptions are often violated, resulting in model uncertainties and thus in an impaired tracking accuracy. To address this issue, this paper enhances the kinematic model by Gaussian Process-based correction models that are adapted to the road conditions online. To avoid potentially dangerous maneuvers, the paper further proposes to consider the prevention of high model uncertainties an objective of the controller. The presented methods are evaluated in a high-fidelity simulation environment.