Model-Free Speed Tracking Control for Automated Cars

Ensuring that longitudinal control in autonomous driving is accurate, robust, and smooth is key to enhance vehicle autonomy and reduce driver intervention, improving user acceptance of autonomous vehicles. Vehicles have complex dynamics that make accurately following the speed reference in various driving situations a challenging task. Model-Free Control (MFC) has shown its performance and robustness in systems which are difficult to model or with time-varying dynamics, making it relevant for this application. In this paper, a cascade control architecture based on MFC is proposed. This strategy keeps the MFC principle of simplicity in control while, due to the cascade structure, using all the information generated by the motion planner and the measured speed and acceleration, which are easy to obtain. Regulators with this structure have been systematically designed to keep the tracking quality, safety and passenger comfort in a wide variety of driving situations.These regulators have been evaluated both in simulation and real-world scenarios, showing improvements in robustness and performance when compared with the baseline.