Model Predictive Control of an Autonomous Vehicle for Predefined Path Tracking Simulation

Over the last ten years, there has been an uptick in research centered on autonomous vehicle development, echoing the rising demand for dependable and streamlined automated transport solutions. However, crafting control mechanisms for such vehicles presents substantial hurdles, given the intricate nature of the systems and the tight limitations regarding steering and speed. This article puts forth a Model Predictive Control (MPC) approach tailored for autonomous transport. This method is favored due to its proficiency in addressing control action constraints and in managing the vehicle's side-to-side movement while upholding steady longitudinal speeds. The advocated MPC is anchored in Laguerre Networks—a broader interpretation of the Receding Horizon Control method. This technique refines upcoming control trajectories by integrating a series of distinct orthonormal basis functions. To validate the efficiency of this suggested MPC technique, a predetermined route tracking algorithm will be showcased through simulations, emphasizing its capability in ensuring prime control over autonomous vehicles.