The exponential development of biotechnology has lead to a quasi unlimited number of potential products going from biopolymers to vaccines. Cell culture has therefore evolved from the simple cell growth outside its natural environment to its use to produce molecules that they do not naturally produce. This rapid development could not be continued without new control and supervising tools as well as a good process understanding. This requirement involves however a large diversity and a better accessibility of process measurements. In this framework, software sensors show numerous potentialities. The objective of a software sensor is indeed to provide an estimation of the system state variables and particularly those which are not obtained through in situ hardware sensors or laborious and expensive analysis. In this context, This work attempts to join the knowledge of increasing bioprocess complexity and diversity and the time scale of process developments and favours systematic modelling methodology, its flexibility and the speed of development. In the field of state observation, an important modelling constraint is the one induced by the selection of the state to estimate and the available measurements. Another important constraint is the model quality. The central axe of this work is to provide solutions in order to reduce the weight of these constraints to software sensors development. On this purpose, we propose four solutions to four main questions that may arise. The first two ones concern modelling uncertainties. 1."How to develop a software sensor using measurements easily available on pilot scale bioreactor?" The proposed solution is a static software sensor using an artificial neural network. Following this modelling methodology we developed static software sensors for the biomass and ethanol concentrations in a pilot scale S. cerevisae cell culture using the measurement of titrating base quantity, agitation rate and CO₂ concentration in the exhaust gas. 2."How to obtain a reaction scheme and a kinetic model to develop a dynamic observation model?". The proposed solution is to combine three elements: a systematic methodology to generate, identify and select the possible reaction schemes, a general kinetic model and a systematic identification procedure where the last step is particularly dedicated to the identification of observation models. Combining these methodologies allowed us to develop a software sensor for the concentrations of an allergen produced by an animal cell culture using the discrete measurement of glucose, glutamine and ammonium concentrations (which are also estimated in continuous time by the software sensors). The two other questions are dealing with kinetic model uncertainty. 3 "How to correct kinetic model parameters while keeping the system observability?". We consider the possibility to correct some model parameters during the process observation. We propose indeed an adaptive observer based on the theory of the most likely initial conditions observer and exploiting the information from the asymptotic observer. This algorithm allows to jointly estimate the state and some kinetic model parameters. 4 "How to avoid any state observer selection that requires an a priori knowledge on the model quality?". Answering this question lead us to the development of hybrid state observers. The general principle of a hybrid observer is to automatically evaluate the model quality and to select the appropriate state observer. In this work we focus on kinetic model quality and propose hybrid observers that evolves between the state observation from an exponential observer (free convergence rate tuning but model error sensitivity) and the one provided by an asymptotic observer (no kinetic model requirement but a convergence rate depending on the dilution rate). Two strategies are investigated in order to evaluate the model quality and to induce the state observation evolution. Each of them have been validated on two simulated cultures (microbial and animal cells) and one real industrial one (B. subtilis). ∙ In a first strategy, the hybrid observer is based on the determination of a parameter that drives the state estimation from the one obtained with an exponential observer (exponential observation) when the model is of good quality to the one provided by an asymptotic observer (asymptotic observation) when a kinetic model error is detected. The evaluation of this driving parameter is made either with an a priori defined function or is coupled to the identification of the initial conditions in a most likely initial conditions observer. ∙ In another strategy, the hybrid observer is based on a statistical test that compares the state estimations provided by an exponential and an asymptotic observer and corrects the state estimation according to it./ Le rapide développement des biotechnologies permet actuellement d'envisager un nombre quasi illimité de produits potentiels allant du biopolymère au vaccin. La culture cellulaire a dès lors évolué de la simple croissance de cellules en dehors de leur environnement naturel à son exploitation pour la production de molécules qu'elles ne produisent pas naturellement. Un tel développement ne peut se poursuivre sans l'utilisation de nouvelles technologies de contrôle et de supervision ainsi q'une bonne compréhension et maîtrise du biprocédé. Cette exigence nécessite cependant une meilleure accessibilité et une plus grande variabilité des mesures des différentes variables de ce procédé. Dans ce contexte, les capteurs logiciels présentent de nombreuses potentialités. L'objectif d'un capteur logiciel est en effet de fournir une estimation des états d'un système et particulièrement de ceux qui ne sont pas mesurés par des capteurs physiquement installés sur le système ou par de longues et coûteuses analyses. Cet objectif peut être obtenu en combinant un modèle du système avec certaines mesures physiques au sein d'un algorithme d'observation d'état. Dans ce domaine de l'observation des bioprocédés, ce travail tente de considérer, à la fois, l'augmentation de la complexité et de la diversité des bioprocédés et l'exigence d'un développement rapide en favorisant le caractère systématique, flexible et rapide des méthodes proposées. Dans le cadre de l'observation des bioprocédés, une importante contrainte de modélisation est induite par la sélection des états à estimer et des mesures disponibles pour cette estimation. Une seconde contrainte est la qualité du modèle. L'axe central de ce travail est de fournir certaines solutions afin de réduire le poids de ces contraintes dans le développement de capteurs logiciels. Pour ce faire, nous proposons quatre réponses à quatre questions qui peuvent survenir lors de ce développement. Les deux premières questions concernent l'incertitude de modélisation. Quant aux deux questions suivantes, elles concernent l'incertitude du modèle lui-même. 1."Comment développer un capteur logiciel exploitant des mesures facilement disponibles sur un bioréacteur pilote?". La réponse que nous apportons à cette question est le développement d'un capteur logiciel statique basé sur un réseau de neurones artificiels. Cette structure nous a permis de développer des capteurs logiciels de concentrations en biomasse et éthanol au sein d'une culture de S. cerevisae utilisant les mesures en ligne de quantité de base titrante, de vitesse d'agitation et de concentration en CO₂ dans le gaz sortant du réacteur. 2."Comment obtenir un schéma réactionnel et un modèle cinétique pour l'identification d'un modèle dynamique d'observation". Afin de répondre à cette question, nous proposons de combiner trois éléments: une méthode de génération systématique de schémas réactionnels, une structure générale de modèle cinétique et une méthode d'identification dont la dernière étape est particulièrement dédiée à l'identification de modèles d'observation. La combinaison de ces éléments nous a permis de développer un capteur logiciel permettant l'estimation continue de la concentration en un allergène produit par une culture de cellules animales en utilisant des mesures échantillonnées de glucose, glutamine et ammonium (qui sont elles aussi estimées en continu par le capteur logiciel). 3."Comment corriger certains paramètres cinétiques tout en maintenant l'observabilité du système?". Nous considérons ici la possibilité de corriger certains paramètres du modèle cinétique durant le procédé de culture. Nous proposons, en effet, un observateur d'état adaptatif exploitant la théorie de l'observateur par identification des conditions initiales les plus vraisemblables et l'information fournie par un observateur asymptotique. L'algorithme proposé permet ainsi de fournir une estimation conjointe de l'état et de certains paramètres cinétiques. 4."Comment éviter la sélection d'un observateur d'état nécessitant une connaissance, a priori, de la qualité du modèle?". La dernière contribution de ce travail concerne le développement d'observateurs d'état hybrides. Le principe général d'un observateur hybride est d'évaluer automatiquement la qualité du modèle et de sélectionner l'observateur d'état approprié. Au sein de ce travail nous considérons la qualité du modèle cinétique et proposons des observateurs d'état hybrides évoluant entre un observateur dit exponentiel (libre ajustement de la vitesse de convergence mais forte sensibilité aux erreurs de mesures) et un observateur asymptotique (ne nécessite aucun modèle cinétique mais présente une vitesse de convergence dépendante du taux de dilution). Afin de réaliser cette évaluation et d'induire l'évolution de l'observation d'état entre ces deux extrémités, deux stratégies sont proposées. Chacune d'elle est illustrée sur deux cultures simulées (une croissance bactérienne et une culture de cellules animales) et une culture réelle de B. subtilis. ∙ Une première stratégie est basée sur la détermination d'un paramètre de pondération entre l'observation fournie par un observateur exponentiel et un observateur asymptotique. L'évaluation de ce paramètre peut être obtenue soit au moyen d'une fonction définie a priori soit par une identification conjointe aux conditions initiales d'un observateur par identification des conditions initiales les plus vraisemblables. ∙ Une seconde stratégie est basée sur une comparaison statistique entre les observations fournies par les deux types d'observateurs. Le résultat de cette comparaison, lorsqu'il indique une incohérence entre les deux observateurs d'état, est alors utilisé pour corriger l'estimation fournie par l'observateur exponentiel.

Résumé : Le travail réalisé au cours de cette thèse traite de la mise au point de méthodes d’estimation d’état robuste, avec deux domaines d’application en ligne de mire. Le premier concerne le positionnement sécuritaire en transport. L’objectif est de fournir la position et la vitesse du véhicule sous la forme d’intervalles avec un grand degré de confiance. Le second concerne la synthèse de capteurs logiciels pour les bioprocédés, et en particulier la reconstruction des concentrations de composants réactionnels à partir d’un nombre limité de mesures et d’un modèle mathématique interprétant le comportement dynamique de ces composants. L’objectif principal est de concevoir des algorithmes qui puissent fournir des estimations acceptables en dépit des incertitudes provenant de la mauvaise connaissance du système comme les incertitudes sur les paramètres du modèle ou les incertitudes de mesures. Dans ce contexte, plusieurs algorithmes ont été étudiés et mis au point. Ainsi, dans le cadre du positionnement de véhicule, la recherche s’est dirigée vers les méthodes robustes Hinfini et les méthodes par intervalles. Les méthodes Hinfini sont des méthodes linéaires prenant en compte une incertitude dans la modélisation et réalisant une optimisation min-max, c’est-à-dire minimisant une fonction de coût qui représente la pire situation compte tenu des incertitudes paramétriques. La contribution de ce travail concerne l’extension à des modèles faiblement non linéaires et l’utilisation d’une fenêtre glissante pour faire face à des mesures asynchrones. Les méthodes par intervalles développées ont pour but de calculer les couloirs de confiance des variables position et vitesse en se basant sur la combinaison d’intervalles issus des capteurs d’une part et sur l’utilisation conjointe d’un modèle dynamique et cinématique du véhicule d’autre part. Dans le cadre des capteurs logiciels pour bioprocédés, trois familles de méthodes ont été étudiées: le filtrage particulaire, les méthodes par intervalles et le filtrage par horizon glissant. Le filtrage particulaire est basé sur des méthodes de Monte-Carlo pour estimer la densité de probabilité conditionnelle de l’état connaissant les mesures. Un de ses principaux inconvénients est sa sensibilité aux erreurs paramétriques. La méthode développée s’applique aux bioprocédés et profite de la structure particulière des modèles pour proposer une version du filtrage particulaire robuste aux incertitudes des paramètres cinétiques. Des méthodes d’estimation par intervalles sont adaptées à la situation où les mesures sont disponibles à des instants discrets, avec une faible fréquence d’échantillonnage, en développant des prédicteurs appropriés. L’utilisation d’un faisceau de prédicteurs grâce à des transformations d’état et le couplage entre les prédicteurs avec des réinitialisations fréquentes permettent d’améliorer les résultats d’estimation. Enfin, une méthode basée sur le filtre à horizon glissant est étudiée en effectuant une optimisation min-max : la meilleure condition initiale est reconstruite pour le plus mauvais modèle. Des solutions sont aussi proposées pour minimiser la quantité de calculs. Pour conclure, les méthodes et résultats obtenus constituent un ensemble d’améliorations dans le cadre de la mise au point d’algorithmes robustes vis-à-vis des incertitudes. Selon les applications et les objectifs fixés, telle ou telle famille de méthodes sera privilégiée. Cependant, dans un souci de robustesse, il est souvent utile de fournir les estimations sous forme d’intervalles auxquels est associé un niveau de confiance lié aux conditions de l’estimation. C’est pourquoi, une des méthodes les plus adaptées aux objectifs de robustesse est représentée par les méthodes par intervalles de confiance et leur développement constituera un point de recherche futur. __________________________________________ Abstract : This thesis work is about the synthesis of robust state estimation methods applied to two different domains. The first area is dedicated to the safe positioning in transport. The objective is to compute the vehicle position and velocity by intervals with a great confidence level. The second area is devoted to the software sensor design in bioprocess applications. The component concentrations are estimated from a limited number of measurements and a mathematical model describing the dynamical behavior of the system. The main interest is to design algorithms which achieve estimation performance and take uncertainties into account coming from the model parameters and the measurement errors. In this context, several algorithms have been studied and designed. Concerning the vehicle positioning, the research activities have led to robust Hinfinity methods and interval estimation methods. The robust Hinfinity methods use a linear model taking model uncertainty into account and perform a min-max optimization, minimizing a cost function which describes the worst-case configuration. The contribution in this domain is an extension to some systems with a nonlinear model and the use of a receding time window facing with asynchronous data. The developed interval algorithms compute confidence intervals of the vehicle velocity and position. They use interval combinations by union and intersection operations obtained from sensors along with kinematic and dynamic models. In the context of bioprocesses, three families of state estimation methods have been investigated: particle filtering, interval methods and moving-horizon filtering. The particle filtering is based on Monte-Carlo drawings to estimate the posterior probability density function of the state variables knowing the measurements. A major drawback is its sensitivity to model uncertainties. The proposed algorithm is dedicated to bioprocess applications and takes advantage of the characteristic structure of the models to design an alternative version of the particle filter which is robust to uncertainties in the kinetic terms. Moreover, interval observers are designed in the context of bioprocesses. The objective is to extend the existing methods to discrete-time measurements by developing interval predictors. The use of a bundle of interval predictors thanks to state transformations and the use of the predictor coupling with reinitializations improve significantly the estimation performance. Finally, a moving-horizon filter is designed, based on a min-max optimization problem. The best initial conditions are generated from the model using the worst parameter configuration. Furthermore, additional solutions have been provided to reduce the computational cost. To conclude, the developed algorithms and related results can be seen as improvements in the design of estimation methods which are robust to uncertainties. According to the application and the objectives, a family may be favored. However, in order to satisfy some robustness criteria, an interval is preferred along with a measure of the confidence level describing the conditions of the estimation. That is why, the development of confidence interval observers represents an important topic in the future fields of investigation.

No description available.

This dissertation consists of two topics: (1) structure-inspired disturbance observer design and (2) disturbance observer-based control/estimation. The disturbance is defined as the discrepancy between a model and the system the model represents. A disturbance observer is an algorithm that generates an estimate of the disturbance. The first topic illustrates a disturbance observer that provides a big class of nonlinear systems with a large basin of attraction, even ensuring global convergence. Such robustness is achieved by leveraging particular system nonlinearities in the observer design. The second topic discusses the usage of disturbance estimates to counteract or capture the effects of disturbances to recover the nominal controller/estimator performance. The main research results are theorems concerning stability analysis of the disturbance observer and the disturbance observer-based systems, whose practical aspects are supported by three application examples---a fixed-wing aircraft, an underwater vehicle, and a Furuta pendulum. / Doctor of Philosophy / This dissertation consists of two topics: (1) structure-inspired disturbance observer design and (2) disturbance observer-based control/estimation. Disturbances are the unknown signals entering the system; an external force, for example, such as the additional lift force due to turbulence surrounding an aircraft is a disturbance. A disturbance observer is an algorithm that estimates the mathematical value of disturbances. The first topic illustrates a disturbance observer whose convergence is guaranteed regardless of the initial condition. Such robustness is achieved by leveraging the system's special properties in the observer design. The second topic discusses the usage of disturbance observers to recover the nominal controller/estimator performance. Control is a study of how make systems behave ideally by properly designing the inputs, while estimation is about how to infer quantities that cannot be directly measured using the measurements that really are available; the solutions are correspondingly called controller and estimator. Disturbance estimates can be exploited by existing controllers and estimators as extra information to counteract or capture the effects of disturbances. The main research results are theorems about the conditions under which these algorithms perform as desired. Practical aspects are supported by three application examples---a fixed-wing aircraft, an underwater vehicle, and a Furuta pendulum.

No description available.

Linear switched reluctance motor (LSRM) drives are investigated and proved as an alternative actuator for vertical linear transportation applications such as a linear elevator. A one-tenth scaled prototype elevator focused on a home elevator with LSRMs is designed and extensive experimental correlation is presented for the first time. The proposed LSRM has twin stators and a set of translator poles without back-iron. The translators are placed between the two stators. The design procedures and features of the LSRM and the prototype elevator are described. The designed LSRM is validated through a finite element analysis (FEA) and experimental measurements. Furthermore, a control strategy for the prototype elevator is introduced consisting of four control loops, viz., current, force, velocity, and position feedback control loops. For force control, a novel force distribution function (FDF) is proposed and compared with conventional FDFs. A trapezoidal velocity profile is introduced to control vertical travel position smoothly during the elevator's ascent, descent, and halt operations. Conventional proportional plus integral (PI) controller is used for the current and velocity control loops and their designs are described. The proposed control strategy is dynamically simulated and experimentally correlated. Analytical and experimental results of this research prove that LSRMs are one of the strong candidates for ropeless linear elevator applications. However, the proposed FDF is assuming that the feedback current signals are ideal currents indicating actual phase currents without any measurement disturbances mainly arising from sensor noise, DC-link voltage ripple, measurement offset, and variations in the plant model. Meanwhile, real control systems in industry have measurement disturbance problems. Phase current corrupted by measurement disturbances increases torque or force ripple, acoustic noise and EMI. Therefore, this dissertation also presents a novel current control method to suppress measurement disturbances without extra hardware. The controller is based on an extended state observer (ESO) and a nonlinear P controller (NLP). The proposed method does not require an accurate mathematical model of system and can be implemented on a low-cost DSP controller. The proposed ESO is exploited to estimate the measurement disturbances on measured phase currents, and the proposed NLP compensates for the measurement disturbances estimated by the ESO. The performance of the proposed current control is validated through extensive dynamic simulations and experiments. Moreover, this rejection of measurement disturbances results in a reduction of force ripple and acoustic noise. Due to superior and robust current control performance, it is believed that the proposed method can be successfully applied into other motor drive systems to suppress measurement disturbances with the same promising results without extra hardware. / Ph. D.

In pneumatic pressure control applications the influence of tubes that connect the valve with the control volume ist mainly neglected. This can lead to stability and robustness issues and limit either control performance or tube length. Modeling and considering tube behavior in controller design procedure allows longer tubes while maintaining the required performance and robustness properties without need for manual tuning. The author\'s previously published Simplified Fluid Transmission Line Model and the proposed model-based controller design enable the specification of a desired pressure trajectory in the control volume while the pressure sensor is mounted directly at the valve. Thus wiring effort is reduced as well as cost and the chance of cable break or sensor disturbance. In order to validate the simulated results the proposed control scheme is implemented on a real-time system and compared to a state-of-the-art pressure regulating valve

Este trabalho apresenta através de simulação um estudo comparativo de estimadores de velocidade de motor de indução trifásico por meio de observadores de estado e da técnica do filtro de Kalman. É realizada uma análise comparativa de desempenho das estratégias de estimação determinísticas e estocásticas, com observadores adaptativos e estimadores baseados na teoria do filtro de Kalman estendido, respectivamente. A realização do trabalho visa a constatação dos procedimentos de elaboração, de operação e de aplicação destas técnicas de estimação usando um exemplo real com fins de ilustrar o ensino de controle e acionamento de máquinas elétricas. As simulações foram realizadas através do Matlab/Simulink com a utilização das ferramentas do Power System Blockset (PSB) e o algoritmo dos estimadores é escrito em programa Matlab e executado por uma função S-Function. Os resultados de simulação demonstram a eficiência de cada um dos estimadores propostos, no que se refere ao comportamento transitório, robustez a ruídos e variações nos parâmetros do motor. / This works presents through of the simulation a comparative study of the sensorless of speed estimation of induction three-phase motor using state observer and Kalman filter. A comparative analysis of the performance of the deterministic and stochastic estimation strategies using adaptive observers and estimators based on extended Kalman filter was realized. The work aims to verify the procedure of the elaboration, operation and application of such estimation techniques using a real example to illustrate the teaching of the control and driving of electric machines. The simulations where performed using Matlab/Simulink with Power System Blockset (PSB) toolboxes and the estimators are programmed as S-Function Matlab. The results indicate the effectiveness of the proposed estimators, according to the transient behavior, robustness to noise and ability to handle parametric variations.

Este trabalho apresenta através de simulação um estudo comparativo de estimadores de velocidade de motor de indução trifásico por meio de observadores de estado e da técnica do filtro de Kalman. É realizada uma análise comparativa de desempenho das estratégias de estimação determinísticas e estocásticas, com observadores adaptativos e estimadores baseados na teoria do filtro de Kalman estendido, respectivamente. A realização do trabalho visa a constatação dos procedimentos de elaboração, de operação e de aplicação destas técnicas de estimação usando um exemplo real com fins de ilustrar o ensino de controle e acionamento de máquinas elétricas. As simulações foram realizadas através do Matlab/Simulink com a utilização das ferramentas do Power System Blockset (PSB) e o algoritmo dos estimadores é escrito em programa Matlab e executado por uma função S-Function. Os resultados de simulação demonstram a eficiência de cada um dos estimadores propostos, no que se refere ao comportamento transitório, robustez a ruídos e variações nos parâmetros do motor. / This works presents through of the simulation a comparative study of the sensorless of speed estimation of induction three-phase motor using state observer and Kalman filter. A comparative analysis of the performance of the deterministic and stochastic estimation strategies using adaptive observers and estimators based on extended Kalman filter was realized. The work aims to verify the procedure of the elaboration, operation and application of such estimation techniques using a real example to illustrate the teaching of the control and driving of electric machines. The simulations where performed using Matlab/Simulink with Power System Blockset (PSB) toolboxes and the estimators are programmed as S-Function Matlab. The results indicate the effectiveness of the proposed estimators, according to the transient behavior, robustness to noise and ability to handle parametric variations.

In the control of batch and continuous distillation columns, one of the most challenging problem is the difficulty in measuring compositions. This problem can be handled by estimating the compositions from readily available online temperature measurements using a state observer. The aim of this study is to design a state observer that estimates the product composition in a multicomponent batch distillation column (MBDC) from the temperature measurements and to test this observer using a batch column simulation. To achieve this, first a model for MBDC is prepared and compared with the data from literature where a case column is utilized. After checking the validity of the simulation package, it is used as a fictitious process for the performance evaluations. In the second phase of the study, an extended Kalman Filter (EKF) is designed by utilizing a simplified model of MBDC and it is implemented for performance investigation on the case column with 8 trays separating the mixture of cyclohexane, n-heptane and toluene. The simplified model utilized in EKF results in response, which have some deviation with rigorous model, mainly due to the simplification of vapor-liquid equilibrium relationship. In the performance evaluation, the tuning parameters of EKF / the diagonal terms of process noise covariance matrix and the diagonal terms of measurement model noise covariance matrix are changed in the range of 50&iexcl / 1x10&iexcl / 7 and 0:5&iexcl / 5x108 and the optimum values are found as 0:00001 and 5000, respectively. The effect of number of measurement points is also investigated with a result of number of component measurements. The effect of measurement period value is also studied and found that it has a major effect on the performance which has to be determined by the available computational facilities. The control of the column is done by utilizing the designed EKF estimator and the estimator is successfully used in controlling the product purities in MBDC under variable reflux-ratio operation.