Controle preditivo EPSAC aplicado ao controle de trajetória de um veículo

Abstract

This TCC implements Extended Prediction Self-Adaptive Control (EPSAC) for minimizing a cost function that quantifies the system's performance in relation to its trajectory, taking into account the desired position, velocity, and constraints for an electric formula vehicle. The approach used for controlling the trajectory of the electric formula vehicle utilizes the bicycle modeling as a simplified mathematical representation of the vehicle's dynamic behavior. The study shows that it is possible to precisely and smoothly follow a desired trajectory while keeping the vehicle stable under different driving conditions. The simulation results demonstrate the effectiveness of the proposed controller and the validity of the bicycle model as an approximation for the electric formula vehicle. This approach has the potential to be applied in autonomous vehicle control systems, student motorsport competitions and other applications.