A Path Tracking Approach for Autonomous Driving on Slippery Surfaces In this paper a linear Model Predictive Control (MPC) based controller for the path tracking of a given trajectory is proposed, which generates suitable references for the dynamical vehicle states. The goal of the path tracking is to achieve good performance on slippery conditions. The equations describing the evolution of the path are linearized in order to obtain a suitable model, and linear constraints on the produced reference states are applied based on the prior knowledge of the reachable equilibrium states which characterize steadystate cornering. A previously proposed lowlevel LQRISM (Integral Sliding Mode) controller is exploited for reference tracking, where the ISM component is required in order to enhance the robustness with respect to the several approximations introduced by the linearized models. IPGCarMaker, a highfidelity vehicle dynamics simulation software, is used for the validation. The performance is verified on a wet asphalt track characterized by segments of different curvature, and compared against the CarMaker driver.

Model Predictive Control based Dynamic Path Tracking of a FourWheel Steering Mobile Robot This paper develops a new constrained model predictive control for a dynamic path tracking of an offroad mobile robot with a double steering axle. The controller is based on a dynamic model that includes wheelground lateral slippage and terrain geometry parameters. It is formulated as an optimization problem that computes at each timestep the optimal front and rear steering angles required to perform a desired path, with respect to multiple constraints, essentially the steering joint limits and the tire adhesion area bounds (i.e., pseudosliding zone limits). The capabilities of such a path tracking controller are shown and discussed through numerical simulations and experiments on a real offroad mobile robot at different speeds.