The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Design and development of algorithms and control systems by artificial cloning of a viscosity sensor

Este trabajo se desarrollo con el fin de implementar algoritmos y sistemas de control para clonar un sensor de viscosidad en la planta de refinamiento de la Empresa Colombiana de Petróleos. Se entrega con una aplicación del software diseñado por los autores, en el cual se puede observar el comportamiento del algoritmo genético que permitió comprobar la hipótesis planteada en el anteproyecto. Además se explica claramente como en conjunto con un equipo interdisciplinario de ingenieros, se desarrolló un sensor virtual mediante la utilización de redes neuronales y las conclusiones a las cuales se llegó. La investigación permite entregar un desarrollo a nivel de control utilizando FPGA, para comenzar la etapa de desarrollo del hardware necesario en la realización de un sistema de clonación mediante aplicaciones físicas y no de software solamente.Instituto Tecnológico de Estudios Superiores de Monterrey ITESMMaestríaThis work was developed in order to implement algorithms and control systems to clone a viscosity sensor in the refining plant of the Colombian Petroleum Company. It is delivered with a software application designed by the authors, in which you can observe the behavior of the genetic algorithm that allowed to verify the hypothesis raised in the draft. In addition, it is clearly explained how, together with an interdisciplinary team of engineers, a virtual sensor was developed through the use of neural networks and the conclusions reached. The research allows to deliver a development at the control level using FPGA, to begin the development stage of the necessary hardware in the realization of a cloning system through physical applications and not software only.Modalidad Presencia

Predictive functional control in autonomous underwater vehicles

El control autónomo del vehículo submarino AUVs ha sido un tema de investigación en las últimas décadas. Los desafíos tratados varían según los intereses de cada grupo de investigación. En esta tesis, nos enfocamos en el Control Funcional Predictivo (PFC), que es una estrategia de control que es fácil de entender, instalar, ajustar y optimizar. El PFC se está desarrollando y aplicando en aplicaciones industriales como destilación, reactores y hornos. Este documento presenta la primera aplicación del Control Funcional Predictivo en vehículos submarinos autónomos, así como los resultados de simulación de controles PFC, TSK-difusos y controladores Gain Scheduling. A través de simulaciones y pruebas reales de navegación con el vehículo Guanay II en las costas de Barcelona, España, se valida la estrategia PFC en el control de movimiento del AUV, el rendimiento PFC se compara con otras técnicas de control como el control difuso y de programación de ganancia. -- Tomado del Formato de Documento de Grado"Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group?s interests. In this study, we focus on the Predictive Functional Control (PFC), which is a control strategy that is easy to understand, install, and tune and optimize. PFC is being developed and applied in industrial applications such as distillation, reactors, and furnaces. This document presents the first application of the Predictive Functional Control in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared to other control techniques such as fuzzy and gain scheduling control." -- Tomado del Formato de Documento de GradoDoctor en IngenieríaDoctorad

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied