Different model predictive control solutions have been developed for autonomous wheeled vehicles to solve trajectory tracking, path following, and obstacle avoidance problems. Nonlinear MPC schemes can exploit accurate state predictions; however, the underlying computational demand might not be affordable in strict real-time contexts or suffer from local minima. Conversely, linearized MPC approaches have the important advantage of drastically reducing computational burdens at the expense of more conservative control performance. This chapter reviews a recently proposed control paradigm to solve the trajectory-tracking problem for input-constrained wheeled mobile vehicles. The presented scheme applies to differential-drive and unicycle robots and car-like vehicles, and it is the result of the combination of feedback linearization techniques, set-invariant theory, and model predictive control. Notably, the resulting control architecture can resort to accurate predictions of the Cartesian coordinates of the vehicle (as in nonlinear MPC formulations) and compute the vehicle’s control inputs using a recursively feasible convex optimization problem (as in linear or linearized MPC formulations). Simulation results on a car-like vehicle show the effectiveness of the presented control scheme.