Study and control of an intelligent autonomous hybrid vehicle using on-board sources” “Etude et contrôle d’un véhicule hybride autonome intelligent utilisant des sources embarquées

Abstract

Autonomous electric vehicles have emerged as an innovative and groundbreaking solution in the field of transportation. The development of autonomous electric vehicle control has been swiftly progressing, fueled by the constant advancement of sophisticated control techniques. This progress has led to an expansion of the potential of autonomous electric vehicle technology. However, despite these impressive advancements, the field still confronts a wide array of challenges that demand attention and solutions to unlock its full capabilities. This thesis delves into the realm of autonomous electrical vehicle control, focusing on three essential aspects: lateral control strategy, longitudinal control strategy, and traction system emulation. The first aspect introduces a dynamic model tailored for autonomous vehicles, with a primary focus on the lateral control strategy. To achieve precise lateral motion along a predefined trajectory, an advanced super-twisting control technique is employed. This technique ensures exceptional accuracy, responsiveness, and stability throughout the vehicle's lateral movement. The second aspect centers around optimizing the longitudinal control strategy through the development of a sophisticated speed planning algorithm. This algorithm takes various factors into account to intelligently generate a suitable speed profile. By considering these crucial elements, the algorithm ensures safe and secure motion within predefined boundaries. The third aspect focuses on emulating the behavior of the traction system. A meticulously designed back-stepping technique is employed with the aim of controlling the speed of an induction motor to mimic the desired performance of the traction system. The results of the mentioned aspects of this thesis are obtained through numerical simulation using the Matlab/Simulink software. Additionally, real-time implementation is conducted in the electrical engineering laboratory of Biskra (LGEB), equipped with dSpace 1104. By addressing the lateral control strategy, longitudinal control strategy, and traction system emulation, this thesis makes significant contributions to the field of autonomous electrical vehicle control.