Modelling, planning and nonlinear control techniques for autonomous vehicles

Autonomous driving has been an important topic of research in recent years. Autonomous driving is a very challenging research topic that requires from different disciplines such as electronics, computer vision, geolocalization, control or planning. This paper tackles the problem of the vehicle control planning and performs a comparison of two nonlinear model-based control strategies for autonomous cars. These control techniques rely on the so called bicycle model and follow a reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a nonlinear control law that is designed by means of the Lyapunov direct approach. The second strategy is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high-order sliding mode control. To test and compare the proposed control strategies a quintic path planner has been implemented in order to provide the desired temporal variables to the control block. Different scenarios have been used to prove such control techniques. First both methods were proved in simulation (Matlab/Simulink and Unity1 ) and finally they were used in scenarios with a real car.