Advanced control based on MPC ideas for offshore hydrogen production

[Abstract] The article deals with the design of a Model Predictive Control strategy in an offshore platform located in the Atlantic Ocean that produces hydrogen from the energy of the wind and the waves. This renewable energy is the energy source that feeds a set of electrolyzers which produce hydrogen, taking into account the energy available and optimizing the operation of the plant. The results of the simulation are presented, showing the correct operation of the platform under the proposed control.Ministerio de Ciencia, Innovación y Universidades; DPI2014-5430-RJunta de Castilla y León; EDU/1083/2013Brasil.Conselho Nacional de Desenvolvimento Científico e Tecnológico; 305785/2015-0Brasil.Conselho Nacional de Desenvolvimento Científico e Tecnológico; 401126/2014-

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

abstract: The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations

Estudio de la Enfermedad de la Fibromialgia como Sistema de Control

La fibromialgia -caracterizada por la hipersensibilidad y por un fuerte dolor musculoesquelético crónico-, es un desorden corporal sin cura universal cuyo tratamiento resulta arduo debido a las causas pseudo-desconocidas de la enfermedad. El objetivo principal de este material es facilitar el tratamiento del desorden de la fibromialgia mediante procedimientos propios de la ingeniería de control, buscando localizar el método que mejor se adapte a las necesidades del paciente y del equipo médico asociado en su lucha contra los significativos síntomas de esta enfermedad. Por otro lado, y como objetivo secundario, este trabajo presenta un desarrollo completo de varias metodologías de control con apoyo de la herramienta MATLAB®, sirviendo como caso ejemplo de aplicación de dichos procedimientos y facilitando así la adaptación de las tecnologías aplicadas a otros proyectos de índole similar, apoyando por tanto el uso de nuevas técnicas en el área de la salud y otros campos complejos que pueden beneficiarse de las ventajas asociadas a la ingeniería de control.Fibromyalgia -characterized by hypersensitivity and a strong chronic musculoskeletal pain- is a body disorder with no universal cure whose treatment is difficult due to the pseudo-unknown causes of the disease. The main objective of this material is to provide the treatment of fibromyalgia using control engineering procedures, trying to locate the best method that meet the needs of the patient and his medical staff in their fight against the symptoms of this disease. On the other hand, and as a secondary objective, this paper presents a complete development of several control methodologies with the support of the MATLAB® tool, serving as an example of the application of these procedures and facilitating the adaptation of technologies applied to other projects with a similar nature, supporting the use of new techniques in the health area and other complex fields that can benefit from the strenghts of the control engineering.Universidad de Sevilla. Grado en Ingeniería Electrónica, Robótica y Mecatrónic

Control systems of offshore hydrogen production by renewable energies

Esta tesis trata sobre un proyecto de diseño de un Sistema de Gestión de Energía (SGE) que utiliza Modelo de Control Predictivo (MPC) para equilibrar el consumo de energía renovable con electrolizadores productores de hidrógeno. La energía generada se equilibra regulando el punto de operación y las conexiones de los electrolizadores usando un MPC basado en un algoritmo de Programación Mixta-Entera Cuadrática. Este algoritmo MPC permite tener en cuenta previsiones de energía, mejorando así el equilibrio y reduciendo el número de encendidos de los equipos. Se han realizado diferentes casos de estudio en instalaciones compuestas por unidades de generación de energía eléctrica a partir de energía renovable. Se considera la técnica de ósmosis inversa como paso intermedio para la producción de agua que alimenta a los electrolizadores. La validación se realiza utilizando datos meteorológicos medidos en un lugar propuesto para el sistema, mostrando el funcionamiento adecuado del SGE desarrollado.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

System Identification, State Estimation, And Control Approaches to Gestational Weight Gain Interventions

abstract: Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually tailored and intensively adaptive intervention to manage weight gain for overweight or obese pregnant women using control engineering approaches. Motivated by the needs of the HMZ, this dissertation presents how to use system identification and state estimation techniques to assist in dynamical systems modeling and further enhance the performance of the closed-loop control system for interventions. Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control. In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.Dissertation/ThesisDoctoral Dissertation Chemical Engineering 201

A Novel Control Engineering Approach to Designing and Optimizing Adaptive Sequential Behavioral Interventions

abstract: Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.   A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.   Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework. The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.Dissertation/ThesisDoctoral Dissertation Chemical Engineering 201

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-