Le vieillissement de la population sans doute catalysera l’augmentation des maladies chroniques et intensifiera le besoin de solutions d’assistance à la personne. Pendant que les chercheurs s’activent à apporter des réponses aux problèmes de santé publique qui s’accentuent, en s’appuyant sur les technologies de l’information et de la communication, le nombre des objets connectés connait une expansion fulgurante. Ainsi, le désir de révolution des technologies pour la santé, afin de faire face à la menace pathologique, coïncide avec le développement de l’Internet des objets 1. En effet, grâce aux innovations technologiques et au progrès médical, nombre de pathologies, souvent chroniques pourraient être suivies en temps réel et en tout lieu. Dans ce contexte, la gestion ou le partage des ressources de communication, la compatibilité des technologies et les performances à atteindre constituent des défis importants. Cet accroissement significatif du volume des communications, les contraintes de mobilité imposées par le contexte du suivi de patient ainsi que les besoins de qualité dans les transmissions de données médicales, révèlent une aspiration à des infrastructures de communication plus flexibles.Dans cette thèse, nous présentons une architecture de communication basée sur les réseaux Radio Cognitive pour répondre à cette exigence. Le caractère adaptable, flexible et autonome de la solution proposée permet d’aspirer à de meilleures performances. Ainsi, pour l’évaluation de son efficacité,nous avons choisi d’analyser et de tester trois critères importants pour les transmissions de données médicales urgentes.La connectivité en tout lieu : Ce premier critère est essentiel dans la mesure des performances et l’estimation de la fiabilité d’une infrastructure réseau dédiée à la santé. Plus précisément, toute solution de communication envisagée, doit être en mesure d’accompagner le patient suivi dans son environnement. En effet, la haute disponibilité des services réseaux et la qualité offerte sont déterminantes pour le suivi de patient à distance. Nous proposons dans cette première contribution, un mécanisme de prédiction spectrale capable d’examiner l’état d’occupation des bandes de fréquence. Cet algorithme associé au module de prise de décision Radio Cognitive, permet de parer aux éventuelles discontinuités de connexion réseaux.La gestion des interférences : Il s’agit du second critère qui évalue le degré de coexistence des ondes garantit par l’architecture, dans un contexte de prolifération des réseaux et des objets connectés. Le matériel communicant doit être capable de percevoir, d’analyser son environnement et d’agir en fonction des différentes contraintes. L’intérêt étant de protéger le matériel surtout médical, souvent très sensible aux bruits. Le suivi du patient devient alors possible à domicile ou à l’hôpital par exemple, avec un niveau d’interférence acceptable. Ainsi, tout en proposant un modèle de déploiement du réseau Radio Cognitive dans un centre hospitalier, nous définissons des exemples de fonctions permettant une adaptation dynamique des paramètres de communication en fonction de la sensibilité des équipements médicaux de proximité.L’efficacité dans la transmission de contenu multimédia : Ce dernier critère symbolise la capacité de l’architecture à fournir du contenu de qualité pour une assistance en temps réel. En effet, un réseau de soin à domicile ou une situation d’urgence peut nécessiter la transmission d’images ou de contenu multimédia vers les centres hospitaliers. Une solution de suivi de patient à distance doit être capable de fournir ces facilités qui imposent l’accès au haut débit. Dans une contribution répondant à cette préoccupation, nous suggérons un algorithme de réservation de ressources permettant de mieux gérer la qualité de service pour le contenu multimédia médical. / The aging of the population will probably catalyze the rise of chronic diseases and could intensify the need for personal assistance solutions. While researchers are focusing on information and communication technologies to provide responses to these public health problems, the number of connected objects is experiencing a rapid expansion. Indeed, desired revolution of technologies for health, forprevention and disease treatment coincides with the development of the Internet of Things 2. Thus, technological innovations and medical progress, for making it possible to monitor pathologies, often chronic, anywhere need appropriate equipments. Also, remote and real-time patient monitoring applications would require more network resources. In this context, communication resources management/sharing, technologies and equipments compatibilities and aplication’s desired performances become significant challenges. In this thesis, we propose an architecture based on Cognitive Radio, for meeting the medical applications constraints. We also analyze and test three important criteria for emergency transmissions, using this architecture.Connectivity : Any solution for patients monitoring must have anywhere and anytime capabilities for care continuity needs. High availability of network services and quality offered are critical for patient telemonitoring. We propose in this context, a spectral prediction mechanism able to examine the occupation conditions of the frequency bands. The algorithm we propose, associated learning and Grey Model technique in order to deal with any network connection discontinuities.Interference management : Network equipments must be able to perceive or to analyze their environment and act according to the underlying constraints. The interest is to protect in our case, medical equipment which are very sensitive to noise. Patient monitoring becomes possible at home or in the hospital, for example, with an acceptable level of interference. We propose for this criterion evaluation, a Cognitive Radio Networks deployment model in a hospital area. We define examples of functions for dynamic adaptation of the communication parameters, depending on the nearby medical devices sensitivity.Transmission efficiency under multimedia content delivery : This criterion analizes the ability of the architecture to provide desired quality in multimedia content delivery for real-time assistance or diagnosis. Patient monitoring at home or an emergency event may require the transmission of image or audio content to the hospital center. The remote monitoring solution must be able to provide these facilities which require a broadband network. We suggest an algorithm for resource reservation that performs a better management of the quality of service for medical multimedia content. We combine this algorithm with a transmission parameters control methode for maintaining the QoS at an acceptable level.

Esta tese de doutorado propõe um controlador preditivo robusto tipo finite control set aplicado ao controle das potências do gerador de indução duplamente alimentado. Desta forma, a proposta possui dois membros do vetor tensão predita do rotor, sendo que o primeiro termo calcula a tensão considerando as referências de corrente do rotor e o segundo é projetado considerando os erros devido à estimação dos parâmetros da máquina. Os referidos erros devido a variações de parâmetros são modelados como alterações na corrente do rotor. O vetor de tensão a ser fornecido ao rotor da máquina é selecionado através da minimização de uma função custo. Os resultados obtidos na simulação computacional e em bancada experimental confirmam o desempenho do controlador proposto. / This Ph.D. thesis proposes a robust predictive controller type finite control set applied to control of the powers at doubly fed induction generator. In this way, the proposal has two members of the predicted voltage vector of the rotor, the first term calculating the voltage considering the rotor references current and the second one is projected considering the errors due to the estimation of the machine parameters. The errors due to variations parameter are modeled as changes in the rotor current. The voltage vector to be supplied to the machine rotor is selected by minimizing a cost function. The results obtained in the computational simulation and experimental bench confirm the performance of the proposed controller.

Subsea development and production of hydrocarbons is challenging due to remote andharsh conditions. Recent technology development with high speed communication to subsea anddownhole equipment has created a new opportunity to both monitor and control abnormal or undesirableevents with a proactive and preventative approach rather than a reactive approach. Twospecific technology developments are high speed, long-distance fiber optic sensing for productionand completion systems and wired pipe for drilling communications. Both of these communicationsystems offer unprecedented high speed and accurate sensing of equipment and processes that aresusceptible to uncontrolled well situations, leaks, issues with flow assurance, structural integrity,and platform stability, as well as other critical monitoring and control issues. The scope of thisdissertation is to design monitoring and control systems with new theoretical developments andpractical applications. For estimators, a novel `1-norm method is proposed that is less sensitiveto data with outliers, noise, and drift in recovering the true value of unmeasured parameters. Forcontrollers, a similar `1-norm strategy is used to design optimal control strategies that utilize a comprehensivedesign with multivariate control and nonlinear dynamic optimization. A framework forsolving large scale dynamic optimization problems with differential and algebraic equations is detailedfor estimation and control. A first area of application is in fiber optic sensing and automationfor subsea equipment. A post-installable fiber optic clamp is used to transmit structural informationfor a tension leg platform. A proposed controller automatically performs ballast operationsthat both stabilize the floating structure and minimize fatigue damage to the tendons that hold thestructure in place. A second area of application is with managed pressure drilling with movinghorizon estimation and nonlinear model predictive control. The purpose of this application is tomaximize rate of drilling penetration, maintain pressure in the borehole, respond to unexpected gasinflux, detect cuttings loading and pack-off, and better manage abnormal events with the drillingprocess through automation. The benefit of high speed data accessibility is quantified as well asthe potential benefit from a combined control strategy versus separate controllers.

<p>The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control principles is the discrete-time method Model Predictive Control (MPC). The main advantage with MPC, compared to most other control principles, is that constraints on control signals and states can easily be handled. In each time step, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended from constrained linear systems to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem. Generally, for this type of optimization problems, the computational complexity is exponential in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel, where the information sent by different users is separated by using almost orthogonal codes. Since the codes are not completely orthogonal, the decoded information at the receiver is slightly correlated between different users. Further, noise is added during the transmission. To estimate the information originally sent, a maximum likelihood problem involving binary variables is solved. The process of simultaneously estimating the information sent by multiple users is called multiuser detection. In this thesis, the problem to efficiently solve MIQP problems originating from MPC is addressed. Two different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In simulations, the algorithm is applied to unconstrained MPC problems with a mixture of real and binary control signals. It has also been applied to the multiuser detection problem, where simulations have shown that the bit error rate can be significantly reduced by using the proposed algorithm as compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT-systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the QP solver and the MIQP solver proposed have lower computational complexity than corresponding generic solvers.</p> / Report code: LiU-TEK-LIC-2005:71.

Nonlinear systems occur in industrial processes, economical systems, biotechnology and in many other areas. The thesis treats methods for system identification and control of such nonlinear systems, and applies the proposed methods to a solar heating/cooling plant. Two applications, an anaerobic digestion process and a domestic solar heating system are first used to illustrate properties of an existing nonlinear recursive prediction error identification algorithm. In both cases, the accuracy of the obtained nonlinear black-box models are comparable to the results of application specific grey-box models. Next a convergence analysis is performed, where conditions for convergence are formulated. The results, together with the examples, indicate the need of a method for providing initial parameters for the nonlinear prediction error algorithm. Such a method is then suggested and shown to increase the usefulness of the prediction error algorithm, significantly decreasing the risk for convergence to suboptimal minimum points. Next, the thesis treats model based control of systems with input signal dependent time delays. The approach taken is to develop a controller for systems with constant time delays, and embed it by input signal dependent resampling; the resampling acting as an interface between the system and the controller. Finally a solar collector field for combined cooling and heating of office buildings is used to illustrate the system identification and control strategies discussed earlier in the thesis, the control objective being to control the solar collector output temperature. The system has nonlinear dynamic behavior and large flow dependent time delays. The simulated evaluation using measured disturbances confirm that the controller works as intended. A significant reduction of the impact of variations in solar radiation on the collector outlet temperature is achieved, though the limited control range of the system itself prevents full exploitation of the proposed feedforward control. The methods and results contribute to a better utilization of solar power.

The main interest of the present thesis is the constrained control of time-delay system, more specifically taking into consideration the discretization problem (due to, for example, a communication network) and the presence of constraints in the system's trajectories and control inputs. The effects of data-sampling and modeling problem are studied in detail, where an uncertainty is added into the system due to additional effect of the discretization and delay. The delay variation with respect to the sampling instants is characterized by a polytopic supra-approximation of the discretization/delay induced uncertainty. Some stabilizing techniques, based on Lyapunov's theory, are then derived for the unconstrained case. Lyapunov-Krasovskii candidates were also used to obtain LMI conditions for a state feedback, in the ''original" state-space of the system. For the constrained control purposes, the set invariance theory is used intensively, in order to obtain a region where the system is ''well-behaviored", despite the presence of constraints and (time-varying) delay. Due to the high complexity of the maximal delayed state admissible set obtained in the augmented state-space approach, in the present manuscript we proposed the concept of set invariance in the ''original" state-space of the system, called D-invariance. Finally, in the las part of the thesis, the MPC scheme is presented, in order to take into account the constraints and the optimality of the control solution.

This thesis focuses on the application of model predictive control approaches to optimize the combined cycle power plants start-ups. Generally, the optimization of start-up is a very problematic issue that poses significant challenges. The development of the proposed approaches is progressive. In the first part a physical model of plant is developed and adapted to optimization purposes, by using a methodology which transforms Modelica model components into optimization-oriented models. By applying this methodology, a library suitable for optimization purposes has been built.In the second part, based on the developed model, an optimization procedure to improve the performances of the start-up phases is suggested. The proposed solution optimizes, in continuous time, the load profile of the turbines, by seeking in specific sets of functions. The optimal profile is derived by considering that this profile can be described by a parameterized function whose parameters are computed by solving a constrained optimal control problem. In the last part, the open-loop optimization procedure has been integrated into a receding horizon control strategy. This strategy represents a robust solution against perturbation and models errors, and enables to improve the trade-off between computation time and optimality of the solution. Nevertheless, the control approach leads to a significant computation time. In order to obtain real-time implementable results, a hierarchical model predictive control structure with two layers, working at different time scales and over different prediction horizons, has been proposed.

The lungs are particularly vulnerable to acute, critical illness. Respiratory failure can result not only from primary lung pathology, such as pneumonia, but also as a secondary consequence of heart failure or inflammatory illness, such as sepsis or trauma. When this occurs, it is essential to support patients with mechanical ventilation while the fundamental disease process is addressed. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation that ensures organ function. Achieving these goals is complicated by the fact that mechanical ventilation can actually cause acute lung injury, either by inflating the lungs to excessive volumes or by using excessive pressures to inflate the lungs. Thus, the challenge to mechanical ventilation is to produce the desired blood levels of carbon dioxide and oxygen without causing further acute lung injury. In this research, we develop an analysis and control synthesis framework for a pressure-limited respirator and lung mechanics system using compartment models. Specifically, a general mathematical model is developed for the dynamic behavior of a multicompartment respiratory system. Then, based on this multicompartment model, an optimal respiratory pattern is characterized using classical calculus of variations minimization techniques for inspiratory and expiratory breathing cycles. Furthermore, model predictive controller frameworks are designed to track the given optimal respiratory air flow pattern while satisfying control input amplitude and rate constrains.

Aquesta tesi està inspirada en els agents naturals per tal de planificar de manera dinàmica la navegació d'un robot diferencial de dues rodes. Les dades dels sistemes de percepció són integrades dins una graella d'ocupació de l'entorn local del robot. La planificació de les trajectòries es fa considerant la configuració desitjada del robot, així com els vértexs més significatius dels obstacles més propers. En el seguiment de les trajectòries s'utilitzen tècniques locals de control predictiu basades en el model, amb horitzons de predicció inferiors a un segon. La metodologia emprada és validada mitjançant nombrosos experiments. / In this thesis are used natural agents for dinamic navigation of a differential driven wheeled mobile robot. The perception data are integrated on a local occupancy grid framework where planar floor model is assumed. The path-planning is done by considering the local desired configuration, as well as the meaningful local obstacle vertexes. The trajectory-tracking is implemented by using LMPC (local model predictive control) techniques, with prediction horizons of less than one second. Many experiments are tested in order to report the validity of the prosed methodology.

The aim of this master thesis was to develop a versatile and reliable experimentalplatform of mobile robots, solving tracking problems, for education and research.Evaluation of parallel bearings-only tracking and control algorithms on a multi-corearchitecture has been performed. The platform was implemented as a mobile wirelesssensor network using multiple mobile robots, each using a mounted camera for dataacquisition. Data processing was performed on the mobile robots and on a server,which also played the role of network communication hub. A major focus was toimplement this platform in a flexible manner to allow for education and futureresearch in the fields of signal processing, wireless sensor networks and automaticcontrol. The implemented platform was intended to act as a bridge between the idealworld of simulation and the non-ideal real world of full scale prototypes.The implemented algorithms did estimation of the positions of the robots, estimationof a non-cooperating target's position and regulating the positions of the robots. Thetracking algorithms implemented were the Gaussian particle filter, the globallydistributed particle filter and the locally distributed particle filter. The regulator triedto move the robots to give the highest possible sensor information under givenconstraints. The regulators implemented used model predictive control algorithms.Code for communicating with filters in external processes were implementedtogether with tools for data extraction and statistical analysis.Both implementation details and evaluation of different tracking algorithms arepresented. Some algorithms have been tested as examples of the platformscapabilities, among them scalability and accuracy of some particle filtering techniques.The filters performed with sufficient accuracy and showed a close to linear speedupusing up to 12 processor cores. Performance of parallel particle filtering withconstraints on network bandwidth was also studied, measuring breakpoints on filtercommunication to avoid weight starvation. Quality of the sensor readings, networklatency and hardware performance are discussed. Experiments showed that theplatform was a viable alternative for data acquisition in algorithm development and forbenchmarking to multi-core architecture. The platform was shown to be flexibleenough to be used a framework for future algorithm development and education inautomatic control.