Robust Tracking Controller Design for Uncertain Nonlinear Self-Driving Cars

Abstract

Dynamic robustness of a control system is a crucial requirement for self-driving cars. To this aim, longitudinal control is one of the key tools to effectively track a desired reference profile, that in a smart road scenario can be imposed by a road infrastructure communication. Indeed, uncertainties and disturbances due to environmental, road and traffic conditions strongly impact on the autonomous vehicle motion. For this reason, it is necessary considering an accurate electromechanical model, with realistic uncertain conditions, actuator limitations, and measurement noises. In this paper, an easy and robust smooth control method is proposed, allowing an autonomous electric vehicle to track a sufficiently smooth reference signal with an error in absolute value smaller than a prescribed value, despite bounded parametric uncertainties, disturbances and velocity measurement noises. The theoretical analysis is illustrated through an interesting application, confirming the effectiveness of the proposed method.