In the recent years there has been significant effort in the design of intelligent autonomous vehicles capable of operating in variable conditions. The precise modeling of the vehicles dynamics improves the efficiency of vehicles controllers in adverse cases, for example in high velocity, when performing abrupt maneuvers, under mass and loads changes or when moving on rough terrain. Using model-based control approaches it is possible to design a nonlinear controller that maintains the vehicle’s motion characteristics according to given specifications. When the vehicle’s dynamics is subject to modeling uncertainties or when there are unknown forces and torques exerted on the vehicle it is important to be in position to estimate in real-time disturbances and unknown dynamics so as to compensate for them. In this direction, estimation for the unknown dynamics of the vehicle and state estimation-based control schemes have been developed. Feedback control of robotic ground vehicles can be primarily based on (i) global linearization approaches, (ii) approximate linearization approaches and (iii) Lyapunov methods. The control is applied to (i) 4-wheel vehicles models, and (ii) articulated vehicles. At a second stage, to implement control under model uncertainty, estimation methods can be employed capable of identifying in real-time the vehicles’ dynamics. The outcome of the estimation procedure can be used by the aforementioned feedback controllers thus implementing indirect adaptive control schemes. Finally to implement control of the ground vehicles through the measurement of a small number of its state variables, elaborated nonlinear filtering approaches are developed. The topics treated by the chapter are: (a) Nonlinear optimal control of four-wheel autonomous ground vehicles (b) Nonlinear optimal control for an autonomous truck and trailer system (c) Nonlinear optimal control of four-wheel steering autonomous vehicles and (d) Flatness-based control of autonomous four-wheel ground vehicles.