Esse trabalho apresenta uma contribuição para o projeto de um controlador MPC robusto quanto à estabilidade baseado na realimentação da saída e admitindo restrições nas entradas e incertezas no modelo da planta. Ele estende a abordagem existente para o projeto de um MPC considerando o caso particular de um modelo em espaço de estados, onde o estado é lido diretamente da planta, sendo aplicado para a situação em que o sistema escolhido de entradas possa ficar saturado ou que o processo seja representado por um modelo diferente do modelo considerado na função objetivo do controlador. Para isso, o MPC se propõe a resolver o problema de otimização em dois estágios: No estágio off-line, vários controladores sem restrição são obtidos a partir de um problema de otimização onde inequações de Lyapunov são acrescentadas ao problema como restrições de forma a garantir a contração do estado (estabilidade). Esses controladores, representados por uma matriz de ganhos, correspondem a todas configurações possíveis de saturação das variáveis manipuladas para um dado conjunto possível de variáveis controladas. Nessas combinações, incluídas como restrições no controlador, todos os modelos previstos para o processo são considerados. Dessa forma, perdendo-se uma entrada, o subconjunto de saídas controladas pode ser alterado.Na versão anterior do método proposto por Rodrigues & Odloak (2005), esse estágio off-line envolve um observador de estados o que dificulta a solução do problema de otimização do MPC robusto, consumindo grande tempo computacional. Além disso, requer uma solução inicial viável que nem sempre é trivial. Com a versão proposta do sistema de modelo espaço estado, o estimador de estado torna-se desnecessário pois o estado passa a ser medido. Na etapa on-line do projeto do controlador, uma lei ótima de controle é obtida a partir da combinação convexa das configurações de controle que correspondem ao conjunto de variáveis manipuladas disponíveis (não saturadas). Também nessa etapa é considerada a incerteza do modelo utilizado pelo controlador. O controlador proposto é testado com alguns exemplos simulados a partir de modelos obtidos na indústria de processo. / In this work, it is presented a contribution to the design of a robust MPC with output feedback, input constraints and uncertain model. This work extends existing approaches by considering a particular non-minimal state space model, which transforms the output feedback strategy into a state feedback strategy. The controller is developed to the case in which the system inputs may become saturated and the model is uncertain. We follow a two stages approach: In the off-line stage, a series of unconstrained robust MPCs is obtained by including in the control optimization problem, inequality constraints that force the state of the closed-loop system to contract along the time. Each of these controllers, represented by a gain matrix, is associated to particular sets of manipulated inputs and controlled outputs. When one manipulated input becomes saturated, we may need to reduce the set of controlled variables. In the existing version of the method, the closed loop system involves a state observer that makes the solution to the robust MPC optimization problem a time consuming step. The problem also requires an initial solution that may not be trivial to find. With the adopted version of the system state space model, the state filter becomes trivial and the state can be considered measured. In the on-line step of the proposed controller design, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. The method is illustrated with simulation examples of the process industry.

The present research study is comprised of two main parts. The first part aims at the development of a systematic system-theoretic framework that allows the derivation of optimal chemotherapy protocols for HIV patients. The proposed framework is conceptually aligned with a notion of continuous-time model predictive control of nonlinear dynamical systems, and results in an optimal way to control viral replication, while maintaining low antiretroviral drug toxicity levels. This study is particularly important because it naturally integrates powerful system-theoretic techniques into a clinically challenging problem with worldwide implications, namely the one of developing chemotherapy patterns for HIV patients that are effective and do not induce adverse side-effects. The second part introduces a new digital controller design methodology for nonlinear (bio)chemical processes, that reflects contemporary necessities in the practical implementation of advanced process control strategies via digital computer-based algorithms. The proposed methodology relies on the derivation of an accurate sampled-data representation of the process, and the subsequent formulation and solution to a nonlinear digital controller synthesis problem. In particular, for the latter two distinct approaches are followed that are both based on the methodological principles of Lyapunov design and rely on a short-horizon model-based prediction and optimization of the rate of“energy dissipation" of the system, as it is realized through the time derivative of an appropriately selected Lyapunov function. First, the Lyapunov function is computed by solving the discrete Lyapunov matrix equation. In the second approach however, it is computed by solving a Zubov-like functional equation based on the system's drift vector field. Finally, two examples of a chemical and a biological reactor that both exhibit nonlinear behavior illustrate the main features of the proposed digital controller design method.

La voiture autonome pourrait réduire le nombre de morts et de blessés sur les routes tout en améliorant l'efficacité du trafic. Cependant, afin d'assurer leur déploiement en masse sur les routes ouvertes au public, leur sécurité doit être garantie en toutes circonstances. Cette thèse traite de l'architecture de planification et de contrôle pour la conduite automatisée et défend l'idée que l'intention du véhicule doit correspondre aux actions réalisées afin de garantir la sécurité à tout moment. Pour cela, la faisabilité cinématique et dynamique de la trajectoire de référence doit être assurée. Sinon, le contrôleur, aveugle aux obstacles, n'est pas capable de la suivre, entraînant un danger pour la voiture elle-même et les autres usagers de la route. L'architecture proposée repose sur la commande à modèle prédictif fondée sur un modèle bicyclette cinématique afin de planifier des trajectoires de référence sûres. La faisabilité de la trajectoire de référence est assurée en ajoutant une contrainte dynamique sur l'angle au volant, contrainte issue de ces travaux, afin d'assurer que le modèle bicyclette cinématique reste valide. Plusieurs contrôleurs à haute-fréquence sont ensuite comparés afin de souligner leurs avantages et inconvénients. Enfin, quelques résultats préliminaires sur les contrôleurs à base de commande sans modèle et leur application au contrôle automobile sont présentés. En particulier, une méthode efficace pour ajuster les paramètres est proposée et implémentée avec succès sur la voiture expérimentale de l'ENSIAME en partenariat avec le laboratoire LAMIH de Valenciennes. / Autonomous vehicles are believed to reduce the number of deaths and casualties on the roads while improving the traffic efficiency. However, before their mass deployment on open public roads, their safety must be guaranteed at all time.Therefore, this thesis deals with the motion planning and control architecture for autonomous vehicles and claims that the intention of the vehicle must match with its actual actions. For that purpose, the kinematic and dynamic feasibility of the reference trajectory should be ensured. Otherwise, the controller which is blind to obstacles is unable to track it, setting the ego-vehicle and other traffic participants in jeopardy. The proposed architecture uses Model Predictive Control based on a kinematic bicycle model for planning safe reference trajectories. Its feasibility is ensured by adding a dynamic constraint on the steering angle which has been derived in this work in order to ensure the validity of the kinematic bicycle model. Several high-frequency controllers are then compared and their assets and drawbacks are highlighted. Finally, some preliminary work on model-free controllers and their application to automotive control are presented. In particular, an efficient tuning method is proposed and implemented successfully on the experimental vehicle of ENSIAME in collaboration with the laboratory LAMIH of Valenciennes.

Sistemas de pulverização com injeção direta possibilitam o uso de diferentes agrotóxicos em uma mesma aplicação, reduzindo o desperdício de agrotóxicos e minimizando desta forma os impactos toxicológico e ambiental relacionados com o preparo e descarte da calda. Neste trabalho foram desenvolvidos modelos matemáticos para um sistema de pulverização com injeção direta de agrotóxico, incluindo a dinâmica da concentração da calda. Também foi desenvolvida uma estratégia de controle preditivo com antecipação das taxas de aplicação para ajustar as taxas de aplicação do agrotóxico e da calda. Também, uma plataforma flexível para o desenvolvimento de pulverizadores foi projetada e construída. A sua automação foi baseada em um controlador embarcado de tempo real adequado para aplicações de controle, aquisição e temporização. Para obter os parâmetros dos modelos e avaliar a estratégia de controle ensaios de vazão e concentração para diferentes pontas de pulverização foram propostos. Com o emprego da abordagem de controle preditivo, os erros das vazões do agrotóxico e da calda ficaram abaixo do nível admissível de 5%. O uso da estratégia de antecipação das taxas de aplicação permitiu aumentar a eficiência da aplicação, reduzindo em até 40% os erros de aplicação. Resultados experimentais são apresentados para validar os modelos e mostrar a eficiência da estratégia de controle desenvolvida. / Sprayer systems with direct injection allow the use of different pesticides in a single application, reducing the waste of chemicals and thereby minimizing the toxicologic and environmental risks associated with the carrier-chemical mix preparation and discard. In this work, mathematical models for a direct chemical injection sprayer system including the dynamics of the carrier-chemical mix concentration are developed. Also, a predictive control strategy with anticipative reference of application rates was developed to adjust the carrier-chemical mix and chemical flow rates. Also, a flexible platform for the development of sprayers was designed and constructed. The automation of this platform was based on a programmable automation controller suitable for control, acquisition and timming applications. To obtain the models and analyse the control strategy, essays flow and concentration for different spray nozzles were proposed. With the use of predictive control approach, the errors of the carrier-chemical mix and chemical flow rates were lower than the admissible level of 5 %. The use of the advanced references increased the efficiency of the variable rate application, reducing up to 40 % application errors. Practical results are presented to validate the models and show the efficiency of the developed control strategy.

Increasing worldwide demand for petroleum motivates greater efficiency, safety, and environmental responsibility in upstream oil and gas processes. The objective of this research is to improve these areas with advanced control methods. This work develops the integration of optimal control methods including model predictive control, moving horizon estimation, high fidelity simulators, and switched control techniques applied to subsea riser slugging and managed pressure drilling. A subsea riser slugging model predictive controller eliminates persistent offset and decreases settling time by 5% compared to a traditional PID controller. A sensitivity analysis shows the effect of riser base pressure sensor location on controller response. A review of current crude oil pipeline wax deposition prevention, monitoring, and remediation techniques is given. Also, industrially relevant control model parameter estimation techniques are reviewed and heuristics are developed for gain and time constant estimates for single input/single output systems. The analysis indicates that overestimated controller gain and underestimated controller time constant leads to better controller performance under model parameter uncertainty. An online method for giving statistical significance to control model parameter estimates is presented. Additionally, basic and advanced switched model predictive control schemes are presented. Both algorithms use control models of varying fidelity: a high fidelity process model, a reduced order nonlinear model, and a linear empirical model. The basic switched structure introduces a method for bumpless switching between control models in a predetermined switching order. The advanced switched controller builds on the basic controller; however, instead of a predetermined switching sequence, the advanced algorithm uses the linear empirical controller when possible. When controller performance becomes unacceptable, the algorithm implements the low order model to control the process while the high fidelity model generates simulated data which is used to estimate the empirical model parameters. Once this online model identification process is complete, the controller reinstates the empirical model to control the process. This control framework allows the more accurate, yet computationally expensive, predictive capabilities of the high fidelity simulator to be incorporated into the locally accurate linear empirical model while still maintaining convergence guarantees.

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.