Modelado matemático, simulación y control de una mini motocicleta autónoma con rueda de reacción

Se presenta el modelado, la simulación y el control de una mini motocicleta autónoma, estabilizada mediante rueda de reacción, que se utilizará para la enseñanza del diseño de sistemas de control. El modelo se construye a partir de un diseño CAD para posteriormente ser integrado en Simulink, junto con los módulos de control. Se realiza la modelización del sistema completo, incluyendo elementos mecánicos, sensores, actuadores, así como la dinámica de contacto de las ruedas con el suelo, consiguiéndose un comportamiento muy similar al de la motocicleta física. A partir del modelo matemático (ecuaciones diferenciales, funciones de transferencia y ecuación de estado), se diseñan un controlador PI para la velocidad, y varios controladores para la inclinación (PID, LQR y LQI). Los controladores han sido probados también en la motocicleta física.In this paper, the modelling and subsequent control of an autonomous mini motorcycle, which will be used to teach about control systems, is presented. The model is initially built from a CAD design and then integrated into Simulink, together with the control modules. The modelling of the complete system, including the mechanical parts, sensors, actuators and wheels behaviour with the ground is carried out, to achieve the same behaviour as the physical motorcycle. From the mathematical model (differential equations, transfer functions and state equation), a PI controller is designed for speed, and several controllers for inclination (PID, LQR and LQI). The controllers have also been tested on the physical motorcycle.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadore

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing