Ingeniería de Control y Automatización
Permanent URI for this collectionhttp://98.81.228.127/handle/20.500.12404/767
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Item Control de robots móviles autónomos en formación usando el esquema líder-seguidor(Pontificia Universidad Católica del Perú, 2021-05-04) Alfaro Purisaca, Paul Anthony; Morán Cárdenas, Antonio ManuelEl concepto de robots trabajando en conjunto viene siendo cada vez más popular gracias a los avances tecnológicos de la autonomía en robots y a la reducción de riesgos al momento de realizar tareas peligrosas para los seres humanos. Debido a esto se propone el desarrollo de dos sistemas de control para la formación de robots móviles autónomos, que pueden ser utilizados en distintos ámbitos como operaciones militares, búsqueda y rescate, vigilancia, reconocimiento de terrenos y/u objetos en específico, exploración de nuevos hábitats, entre otros. Existen tres tipos de soluciones propuestas en la literatura, estos son la estrategia de estructuras virtuales, la basada en comportamientos y el método líder-seguidor, el cual se va a emplear en esta tesis. Se centrará en el modelamiento, inicialización y control de robots no holonómicos en formación, siguiendo a un robot líder el cual guiará al grupo a través de una trayectoria definida. Se usará el modelo Ackerman de robots móviles junto con la teoría de Linealización por Aproximación y Linealización Entrada-Salida para controlar a cada robot utilizando conjuntos de ecuaciones diferenciales que modelan a la formación. Estas ecuaciones utilizan la distancia y el ángulo de visibilidad entre un líder y su seguidor para determinar cómo se moverán al momento de llegar a su posición dentro del grupo. Finalmente se realizan simulaciones con el software MATLAB variando en formaciones y trayectorias, para analizar la estabilidad y validar el comportamiento de los sistemas diseñados, encontrando a grandes rasgos que ambos controladores son efectivos en realizar la formación deseada desde sus posiciones iniciales, evitando colisiones. Adicionalmente, el grupo de robots es guiada por el robot líder sin inconvenientes, manteniendo estable la estructura de la formación.Item Sintonización de un controlador PID utilizando algoritmos genéticos aplicada a una planta concentradora de cobre(Pontificia Universidad Católica del Perú, 2021-05-04) Martínez Ordoñez, Renato Javier; Morán Cárdenas, Antonio ManuelEl objetivo principal de esta tesis es establecer un método para el modelamiento del proceso en un lazo cerrado de control PID (Controlador Proporcional integrativo y derivativo) y con este poder encontrar los parámetros óptimos usando sintonización basada en algoritmos genéticos. En primer lugar, se explica cuál es la problemática que actualmente se tiene en la industria para realizar la sintonización de los lazos de control PID y se detalla el estado del arte de la sintonización de los controladores PID. En segundo lugar, se evalúa cual es el método de identificación en lazo cerrado que mejor representa la respuesta real del proceso de inyección de agua cruda al cajón de alimentación de las bombas de ciclones. En tercer lugar, se realiza la sintonía basada en algoritmos genéticos con el modelo obtenido con la identificación y se evalúa cual es la función de aptitud más adecuada para poder encontrar los parámetros del controlador PID. Finalmente, se presenta los resultados de la sintonización del controlador PID obtenidos para el proceso de inyección de agua cruda al cajón de alimentación de las bombas de ciclones y el proceso de control de nivel de espuma de una celda de flotación Rougher.Item Autonomous control of a mobile robot with incremental deep learning neural networks(Pontificia Universidad Católica del Perú, 2021-03-29) Glöde, Isabella; Morán Cárdenas, Antonio ManuelOver the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many small steps the vehicle learns to achieve a specific goal. The usage of incremental training leads to growing knowledge-base within the system. It also provides the opportunity to use older training achievements to improve the system, when more training data is available. To demonstrate the capabilities of such an algorithm, two different models have been formulated. First, a more simple model with counter wheel steering, and second, a more complex, nonlinear model with independent steering. These two models are trained incrementally to follow different types of trajectories. Therefore an algorithm was established to generate useful initial points. The incremental steps allow the robot to be positioned further and further away from the desired trajectory in the environ- ment. Afterwards, the effects of different trajectory types on model behaviour are investigated by over one thousand simulation runs. To do this, path planning for straight lines and circles are introduced. This work demonstrates that even simulations with simple network structures can have high performance.Item Control of autonomous multibody vehicles using artificial intelligence(Pontificia Universidad Católica del Perú, 2021-03-26) Roder, Benedikt; Morán Cárdenas, Antonio ManuelThe field of autonomous driving has been evolving rapidly within the last few years and a lot of research has been dedicated towards the control of autonomous vehicles, especially car-like ones. Due to the recent successes of artificial intelligence techniques, even more complex problems can be solved, such as the control of autonomous multibody vehicles. Multibody vehicles can accomplish transportation tasks in a faster and cheaper way compared to multiple individual mobile vehicles or robots. But even for a human, driving a truck-trailer is a challenging task. This is because of the complex structure of the vehicle and the maneuvers that it has to perform, such as reverse parking to a loading dock. In addition, the detailed technical solution for an autonomous truck is challenging and even though many single-domain solutions are available, e.g. for pathplanning, no holistic framework exists. Also, from the control point of view, designing such a controller is a high complexity problem, which makes it a widely used benchmark. In this thesis, a concept for a plurality of tasks is presented. In contrast to most of the existing literature, a holistic approach is developed which combines many stand-alone systems to one entire framework. The framework consists of a plurality of modules, such as modeling, pathplanning, training for neural networks, controlling, jack-knife avoidance, direction switching, simulation, visualization and testing. There are model-based and model-free control approaches and the system comprises various pathplanning methods and target types. It also accounts for noisy sensors and the simulation of whole environments. To achieve superior performance, several modules had to be developed, redesigned and interlinked with each other. A pathplanning module with multiple available methods optimizes the desired position by also providing an efficient implementation for trajectory following. Classical approaches, such as optimal control (LQR) and model predictive control (MPC) can safely control a truck with a given model. Machine learning based approaches, such as deep reinforcement learning, are designed, implemented, trained and tested successfully. Furthermore, the switching of the driving direction is enabled by continuous analysis of a cost function to avoid collisions and improve driving behavior. This thesis introduces a working system of all integrated modules. The system proposed can complete complex scenarios, including situations with buildings and partial trajectories. In thousands of simulations, the system using the LQR controller or the reinforcement learning agent had a success rate of >95 % in steering a truck with one trailer, even with added noise. For the development of autonomous vehicles, the implementation of AI at scale is important. This is why a digital twin of the truck-trailer is used to simulate the full system at a much higher speed than one can collect data in real life.Item Control of an over-actuated nanopositioning system by means of control allocation(Pontificia Universidad Católica del Perú, 2021-03-26) Seminario Reategui, Renzo Andre; Morán Cárdenas, Antonio ManuelThis Master’s Thesis is devoted to the analysis and design of a control structure for the nanopositioning system LAU based on the dynamic control allocation technique. The objective is to control the vertical displacement with nanometer precision under a control effort distribution criterion among the actuator set. In this case, the pneumatic actuator is used as a passive gravity compensator while the voice coil motor generates the transient forces. The analysis of the system characteristics allows defining the design criterion for the control allocation. In this direction, the proposed dynamic control allocation stage considers a frequency distribution of the control effort. The lower frequency components are assigned to the pneumatic actuator while the higher frequencies are handled by the voice coil drive. The significant actuator dynamics are compensated through a Kalman filter approach. The position controller is based on a feedback linearization framework with a disturbance observer for enhanced robustness. The experimental validation demonstrates the feasibility of the proposed technique.