Cette thèse porte sur l'optimisation et la commande prédictive des centrales de production d'énergie en utilisant des modèles physiques des installations. Les modèles sont réalisés à l'aide du langage Modelica, un langage équationnel adapté à la modélisation de systèmes multi-physiques. La modélisation de systèmes physiques dans ce langage est présentée dans une première partie, ainsi que les traitements symboliques réalisés par les compilateurs Modelica pour mettre les modèles sous une forme adaptée à l'optimisation. On présente dans une seconde partie le développement d'une méthode d'optimisation dynamique hybride pour les centrales de production d'énergie, qui fournit une trajectoire optimisée de l'installation sur un horizon long. Les trajectoires calculées incluent les trajectoires des commandes continues ainsi que les décisions d'engagement des différents équipements. L'algorithme d'optimisation combine la méthode de collocation et une méthode nommée Sum Up Rounding (SUR) pour la prise en compte des décisions d'engagement. Un algorithme de commande prédictive (MPC) est enfin introduit afin de garantir le suivi des trajectoires optimales et de prendre en compte en temps réel la présence de perturbations et les erreurs du modèle d'optimisation. L'algorithme MPC utilise des modèles linéarisés tangents générés automatiquement à partir du modèle non linéaire. / This thesis deals with hybrid optimal control and Model Predictive Control (MPC) of power plants by use of physical models. Models of the facilities are developped with Modelica, an equation based language tailored for modelling multi-physics systems. Modeling of physical systems with Modelica is introduced in a first part, as well as some of the symbolic processing done by Modelica compilers that transform the original model to a form suited for optimization. Then, a method to solve optimal control problems on hybrid systems (such as power plants) is presented. This methods provides an optimal trajectory for the power plant on a long horizon. The optimal trajectory computed by the method includes the trajectories of continuous inputs as well as switching decisions for components in the plant. The optimization algorithm combines the collocation method and a method named Sum Up Rounding (SUR) for dealing with switches. Finally, a Model Predictive Controller is developped in order to follow this optimal trajectory in real time, and to cope with disturbances on the actual system and modelling errors. The proposed MPC uses tangent linear models of the plant that are derived automatically from the nonlinear model.

[ES] En muchos campos de la ingeniería los modelos matemáticos son utilizados para describir el comportamiento de los sistemas, procesos o fenómenos. Hoy en día, existen varias técnicas o métodos que pueden ser usadas para obtener estos modelos. Debido a su versatilidad y simplicidad, a menudo se prefieren los métodos de identificación de sistemas. Por lo general, estos métodos requieren la definición de una estructura y la estimación computacional de los parámetros que la componen utilizando un conjunto de procedimientos y mediciones de las señales de entrada y salida del sistema. En el contexto de la identificación de sistemas no lineales, un desafío importante es la selección de la estructura. En el caso de que el sistema a identificar presente una no linealidad de tipo estático, los modelos orientados a bloques, pueden ser útiles para definir adecuadamente una estructura. Sin embargo, el diseñador puede enfrentarse a cierto grado de incertidumbre al seleccionar el modelo orientado a bloques adecuado en concordancia con el sistema real. Además de este inconveniente, se debe tener en cuenta que la estimación de algunos modelos orientados a bloques no es sencilla, como es el caso de los modelos de Wiener-Hammerstein que consisten en un bloque NL en medio de dos subsistemas LTI. La presencia de dos subsistemas LTI en los modelos de Wiener-Hammerstein es lo que principalmente dificulta su estimación. Generalmente, el procedimiento de identificación comienza con la estimación de la dinámica lineal, y el principal desafío es dividir esta dinámica entre los dos bloques LTI. Por lo general, esto implica una alta interacción del usuario para desarrollar varios procedimientos, y el modelo final estimado depende principalmente de estas etapas previas. El objetivo de esta tesis es contribuir a la identificación de los modelos de Wiener-Hammerstein. Esta contribución se basa en la presentación de dos nuevos algoritmos para atender aspectos específicos que no han sido abordados en la identificación de este tipo de modelos. El primer algoritmo, denominado WH-EA, permite estimar todos los parámetros de un modelo de Wiener-Hammerstein con un solo procedimiento a partir de un modelo dinámico lineal. Con WH-EA, una buena estimación no depende de procedimientos intermedios ya que el algoritmo evolutivo simultáneamente busca la mejor distribución de la dinámica, ajusta con precisión la ubicación de los polos y los ceros y captura la no linealidad estática. Otra ventaja importante de este algoritmo es que bajo consideraciones específicas y utilizando una señal de excitación adecuada, es posible crear un enfoque unificado que permite también la identificación de los modelos de Wiener y Hammerstein, que son casos particulares del modelo de Wiener-Hammerstein cuando uno de sus bloques LTI carece de dinámica. Lo interesante de este enfoque unificado es que con un mismo algoritmo es posible identificar los modelos de Wiener, Hammerstein y Wiener-Hammerstein sin que el usuario especifique de antemano el tipo de estructura a identificar. El segundo algoritmo llamado WH-MOEA, permite abordar el problema de identificación como un Problema de Optimización Multiobjetivo (MOOP). Sobre la base de este algoritmo se presenta un nuevo enfoque para la identificación de los modelos de Wiener-Hammerstein considerando un compromiso entre la precisión alcanzada y la complejidad del modelo. Con este enfoque es posible comparar varios modelos con diferentes prestaciones incluyendo como un objetivo de identificación el número de parámetros que puede tener el modelo estimado. El aporte de este enfoque se sustenta en el hecho de que en muchos problemas de ingeniería los requisitos de diseño y las preferencias del usuario no siempre apuntan a la precisión del modelo como un único objetivo, sino que muchas veces la complejidad es también un factor predominante en la toma de decisiones. / [CA] En molts camps de l'enginyeria els models matemàtics són utilitzats per a descriure el comportament dels sistemes, processos o fenòmens. Hui dia, existeixen diverses tècniques o mètodes que poden ser usades per a obtindre aquests models. A causa de la seua versatilitat i simplicitat, sovint es prefereixen els mètodes d'identificació de sistemes. En general, aquests mètodes requereixen la definició d'una estructura i l'estimació computacional dels paràmetres que la componen utilitzant un conjunt de procediments i mesuraments dels senyals d'entrada i eixida del sistema. En el context de la identificació de sistemes no lineals, un desafiament important és la selecció de l'estructura. En el cas que el sistema a identificar presente una no linealitat de tipus estàtic, els models orientats a blocs, poden ser útils per a definir adequadament una estructura. No obstant això, el dissenyador pot enfrontar-se a cert grau d'incertesa en seleccionar el model orientat a blocs adequat en concordança amb el sistema real. A més d'aquest inconvenient, s'ha de tindre en compte que l'estimació d'alguns models orientats a blocs no és senzilla, com és el cas dels models de Wiener-Hammerstein que consisteixen en un bloc NL enmig de dos subsistemes LTI. La presència de dos subsistemes LTI en els models de Wiener-Hammerstein és el que principalment dificulta la seua estimació. Generalment, el procediment d'identificació comença amb l'estimació de la dinàmica lineal, i el principal desafiament és dividir aquesta dinàmica entre els dos blocs LTI. En general, això implica una alta interacció de l'usuari per a desenvolupar diversos procediments, i el model final estimat depén principalment d'aquestes etapes prèvies. L'objectiu d'aquesta tesi és contribuir a la identificació dels models de Wiener-Hammerstein. Aquesta contribució es basa en la presentació de dos nous algorismes per a atendre aspectes específics que no han sigut adreçats en la identificació d'aquesta mena de models. El primer algorisme, denominat WH-EA (Algorisme Evolutiu per a la identificació de sistemes de Wiener-Hammerstein), permet estimar tots els paràmetres d'un model de Wiener-Hammerstein amb un sol procediment a partir d'un model dinàmic lineal. Amb WH-EA, una bona estimació no depén de procediments intermedis ja que l'algorisme evolutiu simultàniament busca la millor distribució de la dinàmica, afina la ubicació dels pols i els zeros i captura la no linealitat estàtica. Un altre avantatge important d'aquest algorisme és que sota consideracions específiques i utilitzant un senyal d'excitació adequada, és possible crear un enfocament unificat que permet també la identificació dels models de Wiener i Hammerstein, que són casos particulars del model de Wiener-Hammerstein quan un dels seus blocs LTI manca de dinàmica. L'interessant d'aquest enfocament unificat és que amb un mateix algorisme és possible identificar els models de Wiener, Hammerstein i Wiener-Hammerstein sense que l'usuari especifique per endavant el tipus d'estructura a identificar. El segon algorisme anomenat WH-MOEA (Algorisme evolutiu multi-objectiu per a la identificació de models de Wiener-Hammerstein), permet abordar el problema d'identificació com un Problema d'Optimització Multiobjectiu (MOOP). Sobre la base d'aquest algorisme es presenta un nou enfocament per a la identificació dels models de Wiener-Hammerstein considerant un compromís entre la precisió aconseguida i la complexitat del model. Amb aquest enfocament és possible comparar diversos models amb diferents prestacions incloent com un objectiu d'identificació el nombre de paràmetres que pot tindre el model estimat. L'aportació d'aquest enfocament se sustenta en el fet que en molts problemes d'enginyeria els requisits de disseny i les preferències de l'usuari no sempre apunten a la precisió del model com un únic objectiu, sinó que moltes vegades la complexitat és també un factor predominant en la presa de decisions. / [EN] In several engineering fields, mathematical models are used to describe the behaviour of systems, processes or phenomena. Nowadays, there are several techniques or methods for obtaining mathematical models. Because of their versatility and simplicity, system identification methods are often preferred. Generally, systems identification methods require defining a structure and estimating computationally the parameters that make it up, using a set of procedures y measurements of the system's input and output signals. In the context of nonlinear system identification, a significant challenge is the structure selection. In the case that the system to be identified presents a static type of nonlinearity, block-oriented models can be useful to define a suitable structure. However, the designer may face a certain degree of uncertainty when selecting the block-oriented model in accordance with the real system. In addition to this inconvenience, the estimation of some block-oriented models is not an easy task, as is the case with the Wiener-Hammerstein models consisting of a NL block in the middle of two LTI subsystems. The presence of two LTI subsystems in the Wiener-Hammerstein models is what mainly makes their estimation difficult. Generally, the identification procedure begins with the estimation of the linear dynamics, and the main challenge is to split this dynamic between the two LTI block. Usually, this implies a high user interaction to develop several procedures, and the final model estimated mostly depends on these previous stages. The aim of this thesis is to contribute to the identification of the Wiener-Hammerstein models. This contribution is based on the presentation of two new algorithms to address specific aspects that have not been addressed in the identification of this type of model. The first algorithm, called WH-EA (An Evolutionary Algorithm for Wiener-Hammerstein System Identification), allows estimating all the parameters of a Wiener-Hammerstein model with a single procedure from a linear dynamic model. With WH-EA, a good estimate does not depend on intermediate procedures since the evolutionary algorithm looks for the best dynamic division, while the locations of the poles and zeros are fine-tuned, and nonlinearity is captured simultaneously. Another significant advantage of this algorithm is that under specific considerations and using a suitable excitation signal; it is possible to create a unified approach that also allows the identification of Wiener and Hammerstein models which are particular cases of the Wiener-Hammerstein model when one of its LTI blocks lacks dynamics. What is interesting about this unified approach is that with the same algorithm, it is possible to identify Wiener, Hammerstein, and Wiener-Hammerstein models without the user specifying in advance the type of structure to be identified. The second algorithm called WH-MOEA (Multi-objective Evolutionary Algorithm for Wiener-Hammerstein identification), allows to address the identification problem as a Multi-Objective Optimisation Problem (MOOP). Based on this algorithm, a new approach for the identification of Wiener-Hammerstein models is presented considering a compromise between the accuracy achieved and the model complexity. With this approach, it is possible to compare several models with different performances, including as an identification target the number of parameters that the estimated model may have. The contribution of this approach is based on the fact that in many engineering problems the design requirements and user's preferences do not always point to the accuracy of the model as a single objective, but many times the complexity is also a predominant factor in decision-making. / Zambrano Abad, JC. (2021). Identification of nonlinear processes based on Wiener-Hammerstein models and heuristic optimization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171739 / TESIS

Les personnes vivant avec une lésion de la moelle épinière (LMÉ) subissent une réduction drastique de leurs capacités sensori-motrices en dessous du niveau de la lésion. Cette pathologie influence négativement leur contrôle postural, vu comme le résultat de contributions actives, passives et neuronales. Une approche par modèle est choisie afin de représenter ces différentes contributions et mieux comprendre le contrôle postural en position assise. Deux problèmes apparaissent. Tout d’abord les modèles existants de la position assise sont très rares et inapplicables à l’étude des personnes vivant avec une LMÉ qui, dans l’absence de contrôle musculaire au niveau du tronc, vont utiliser leurs membres supérieurs pour s’équilibrer. De plus, les équations non linéaires sous forme descripteur des modèles biomécaniques existant sont linéarisées dans la majorité des cas afin de simplifier les équations obtenues ce qui n’est plus valable dans le cas de mouvements de grande amplitude. Or des travaux récents fournissent des outils pour analyser ces modèles en utilisant le formalisme de Takagi-Sugeno issu de la logique floue. Ce travail est focalisé sur l’étude du contrôle postural des personnes vivant avec une LMÉ par des outils de l’automatique non linéaire, trois objectifs sont définis : 1. Proposer des modèles biomécaniques non linéaires de la stabilité en position assise pour les personnes vivant avec une LMÉ. 2. Utiliser des méthodes d’écriture de modèle du domaine de l’automatique non linéaire afin de créer les outils nécessaires à l’estimation des variables internes (non mesurées) au modèle. 3. Améliorer la connaissance de la stabilité chez les personnes vivant avec une LMÉ par l’estimation expérimentale d’efforts internes en fonction de paramètres personnels et des conditions expérimentales. Les modèles créés dans ce manuscrit sont, à notre connaissance, les premiers à représenter la stabilité assise en considérant le déplacement des membres supérieurs. Il s’agit également des premiers modèles biomécaniques à utiliser les avancées récentes de l’automatique non linéaire pour l’estimation d’efforts internes non mesurables. / The ability to maintain stability in an unsupported sitting position often becomes challenging after an individuals is affected by a spinal cord injury (SCI). In order to better understand sitting postural control as a combination of active, passive and neuronal contribution, a modelling approach is chosen. Two problems arise; first of all, existing models of sitting control are very scarse and inapplicable to people living with a SCI who will use their upper in a compensatory strategy to balance their trunk. Moreover, the majority of biomechanical models nonlinear equations are linearized with small angle hypothesis for an easier processing which is not acceptable when movements become large. Recent advances in nonlinear control theory provide the theoretical tools for the creation of nonlinear observers using the Takagi-Sugeno fuzzy formalism. This work aims at the study of postural control for people living with a SCI by use of nonlinear tools from control theory; three main goals are formulated: 1. Creating new nonlinear biomechanical models aiming at the study of sitting stability of people living with SCI. 2. Using tools coming from nonlinear control theory to estimate model unmeasured variable 3. Improve the current knowledge on stability for people living with a SCI by estimating internal efforts depending on personal parameters in experimental conditions. The models created in this manuscript are, to the best of our knowledge, the first to consider the upper limbs displacement for the control of sitting stability and the first biomechanical models to use the recent advances in nonlinear control theory for the estimation of unmeasured variables.

Diese Dissertation beschäftigt sich mit drei relevanten Aufgabebereichen einer Zentralbank und untersucht die makroökonomische Prognose, die Analyse der Geldpolitik in einem makroökonomischen Modell und die Analyse des Währungssystems. Jedes dieser Phänomene wird mit Hilfe des passenden Modells nach Nichtlinearitäten untersucht. Der erste Teil der Dissertation zeigt, dass nichtlineare rekurrente neuronale Netze, eine Methode aus dem Bereich Maschinelles Lernen, die Standard-Methoden übertreffen können und präzise Vorhersagen der Inflation in 1 bis 12 Monaten liefern können. Der zweiter Teil analysiert eine nichtlineare Formulierung der monetären Taylor-Regel. Anhand der Schätzung eines nichtlinearen DSGE Modells wird gezeigt, dass die Taylor-Regel in den USA asymmetrisch ist. Die Zentralbank ergreift stärkere Maßnahmen, wenn die Inflation höher ist als die Zielinflation, und reagiert weniger wenn die Inflation niedriger als die Zielinflation ist. Gleicherweise ist die Reaktion der monetären Politik stärker bei zu geringem Produktionswachstum als bei zu hohem. Der dritte Teil der Dissertation formuliert ein theoretisches Modell, das für eine Analyse der digitalen dezentralen Währungen verwendet werden kann. Es werden die Bedingungen bestimmt, unter denen der Wettbewerb zwischen der Währung der Zentralbank und den digitalen Währungen einige Beschränkungen für die Geldpolitik darstellt. / This thesis addresses three topics that are relevant for the central bank policy design. It analyzes forecasting of the macroeconomic time series, accurate monetary policy formulation in a general equilibrium macroeconomic model and monitoring of the novel developments in the monetary system. All these issues are analyzed in a nonlinear framework with the help of a macroeconomic model. The first part of the thesis shows that nonlinear recurrent neural networks – a method from the machine learning literature – outperforms the usual benchmark forecasting models and delivers accurate inflation predictions for 1 to 12 months ahead. The second part of the thesis analyzes a nonlinear formulation of the Taylor rule. With the help of the nonlinear Bayesian estimation of a DSGE model it shows that the Taylor rule in the US is asymmetric. The central bank reacts stronger to inflation when it is above the target than when it is below the target. Similarly, the reaction to the output growth rate is stronger when the output growth is too weak than when it is too strong. The last part of the thesis develops a theoretical model that is suitable for the analysis of decentralized digital currencies. The model is used to derive the conditions, under which the competition between digital and fiat currencies imposes restrictions on the monetary policy design.

We consider the Volter-Salnikov model describing the dynamics of the flow chemical reactor. In the work, a study of deterministic model was performed, deterministic system attractors were defined, bifurcation diagram was constructed. For stochastic model, we analyzed a response of attractors (equilibria and cycles) on sensitivity to noise. With the help of confidence domains, a geometrical description of the dispersion of random states was given, noise-induced transitions were investigated, and of critical values of the noise intensity were estimated. We have constructed a feedback regulator that allows to provide the desired stochastic sensitivity of the system. / В работе рассматривается модель Вольтера-Сальникова, описывающая динамику проточного химического реактора. В работе было проведено исследование детерминированной модели, определены детерминированные аттракторы системы, построена бифуркационная диаграмма. Для стохастической модели проведен анализ аттракторов системы (равновесий и циклов) на чувствительность к шуму. С помощью доверительных областей было дано геометрическое описание областей рассеивания случайных состояний. С помощью доверительных областей исследованы индуцированные шумом переходы, проведена оценка критических значений интенсивности шума. В работе построен управляющий регулятор, позволяющий обеспечить в системе желаемую стохастическую чувствительность.

[pt] Este trabalho tem como objetivo estudar estratégias de Controle Preditivo baseado em Modelo Não-Linear (NMPC) aplicadas a uma coluna de destilação despropanizadora simulada. Essas colunas são empregadas em unidades de processamento de gás natural (UPGNs) para a separação do produto propano do butano. Colunas de destilação possuem características particularmente desafiadoras sob o ponto de vista de controle, como: não-linearidades, grandes constantes de tempo, atraso, restrições de variáveis e inversão do sinal de ganho estático. Como as medidas de composição frequentemente possuem atrasos e dados esparsos, os sistemas de controle convencionais não são capazes de controlar a composição diretamente e possuem dificuldade em manter os produtos dentro das especificações. Contudo, controladores baseados em modelo possuem a habilidade de prever a composição através do modelo interno do processo, além de serem capazes de lidar com restrições. Na literatura, nenhuma aplicação do modelo de Hammerstein modificado para coluna de destilação ou para sistemas multivariáveis foi encontrada, sendo esta uma novidade. Desta forma, foram estudadas três estratégias de controle: controle PID tradicional, NMPC com modelo de Hammerstein modificado (H-NMPC) e NMPC com modelo por Redes Neurais (NN-NMPC). O sistema estudado foi identificado de forma a se obter valores numéricos adequados aos parâmetros dos modelos. A identificação dos parâmetros dos modelos e os algoritmos de NMPC foram implementados no ambiente MATLAB. A coluna de destilação foi simulada usando o Aspen Plus Dynamics. Como resultado, o H-NMPC teve o melhor desempenho de controle ao rastrear diferentes trajetórias de referência, a desacoplar as variáveis controladas e a rejeitar os distúrbios. Além disso, esta apresentou maior rapidez computacional comparado com a estratégia NNNMPC. / [en] This work aims to study strategies of Nonlinear Model Predictive Control (NMPC) applied to a simulated depropanizer distillation column. These columns are used in natural gas processing units (NGPUs) for the separation of the product propane from butane. Distillation columns have particularly challenging features from the control point of view, such as: nonlinearities, large time constants, delay, variable constraints and static gain signal inversion. Because compositional measures often have delays and sparse data, conventional control systems are not able to control composition directly and have difficulty keeping products within specifications. However, model-based controllers predict composition through the internal process model, besides being able to handle constraints. In the literature, no applications of the modified Hammerstein model for distillation column or multivariable systems was found, so this is a novelty. Therefore, three control strategies were studied: traditional PID control, NMPC with modified Hammerstein model (H-NMPC) and NMPC with neural network model (NN-NMPC). The studied system was identified in order to obtain adequate numerical values of the model parameters. The model identification and the NMPC algorithms were implemented in the MATLAB environment. The distillation column was simulated using Aspen Plus Dynamics. As a result, the H-NMPC provided better control performance for different setpoint tracking, control variables decoupling, and disturbance rejection. Furthermore, it presented faster computational speed compared to NN-NMPC.

Cette thèse s'inscrit dans le cadre du projet collaboratif MICROSOL, mené par Schneider Electric, et qui oeuvre pour le développement de micros centrales solaires thermodynamiques destinées à la production d'électricité en sites isolés (non connectés au réseau électrique) en exploitant l'énergie thermique du soleil. Le but de cette thèse étant le développement de lois de commande innovantes et efficaces pour la gestion de l'énergie de deux types de micros centrales solaires thermodynamiques : à base de moteur à cycle de Stirling et à base de machines à Cycle de Rankine Organique (ORC). Dans une première partie, nous considérons une centrale solaire thermodynamique à base de machine à cycle de Stirling hybridée à un supercondensateur comme moyen de stockage d'énergie tampon. Dans ce cadre, nous proposons une première loi de commande validée expérimentalement, associée au système de conversion d'énergie du moteur Stirling, qui dote le système de performances quasi optimales en termes de temps de réponse ce qui permet de réduire la taille du supercondensateur utilisé. Une deuxième loi de commande qui gère explicitement les contraintes du système tout en dotant ce dernier de performances optimales en terme de temps de réponse, est également proposée. Cette dernière loi de commande est en réalité plus qu'un simple contrôleur, elle constitue une méthodologie de contrôle applicable pour une famille de systèmes de conversion de l'énergie.Dans une deuxième partie, nous considérons une centrale solaire thermodynamique à base de machine à cycle de Rankine Organique (ORC) hybridée à un banc de batteries comme moyen de stockage d'énergie tampon. Etant donné que ce système fonctionne à vitesse de rotation fixe pour la génératrice asynchrone qui est connectée à un système de conversion d'énergie commercial, nous proposons une loi de commande prédictive qui agit sur la partie thermodynamique de ce système afin de le faire passer d'un point de fonctionnement à un autre, lors des appels de puissance des charges électriques, le plus rapidement possible (pour réduire le dimensionnement des batteries) tout en respectant les contraintes physiques du système. La loi de commande prédictive développée se base sur un modèle dynamique de la machine ORC identifié expérimentalement grâce à un algorithme d'identification nonlinéaire adéquat. / This Ph.D thesis was prepared in the scope of the MICROSOL project, ledby Schneider Electric, that aims at developing Off-grid solar thermodynamic micro powerplants exploiting the solar thermal energy. The aim of this thesis being the development of innovative and efficient control strategies for the energy management of two kinds of solar thermodynamic micro power plants: based on Stirling engine and based and Organic RankineCycle (ORC) machines.In a first part, we consider the Stirling based solar thermodynamic micro power planthybridized with a supercapacitor as an energy buffer. Within this framework, we propose afirst experimentally validated control strategy, associated to the energy conversion system ofthe Stirling engine, that endows the system with quasi optimal performances in term of settlingtime enabling the size reduction of the supercapacitor. A second control strategy that handlesexplicitly the system constraints while providing the system with optimal performances interm of settling time , is also proposed. This control strategy is in fact more than a simplecontroller, it is a control framework that holds for a family of energy conversion systems.In a second part, we consider the Organic Rankine Cycle (ORC) based thermodynamicmicro power plant hybridized with a battery bank as an energy buffer. Since this system worksat constant speed for the asynchronous generator electrically connected to a commercial energyconversion system, we propose a model predictive controller that acts on the thermodynamicpart of this system to move from an operating point to another, during the load power demandtransients, as fast as possible (to reduce the size of the battery banks) while respecting thephysical system constraints. The developed predictive controller is based upon a dynamicmodel, for the ORC power plant, identified experimentally thanks to an adequate nonlinearidentification algorithm.

<div>The orbits of interest for potential missions are stable or nearly stable to maintain long term presence for conducting scientific studies and to reduce the possibility of rapid departure. Near Rectilinear Halo Orbits (NRHOs) offer such stable or nearly stable orbits that are defined as part of the L1 and L2 halo orbit families in the circular restricted three-body problem. Within the Earth-Moon regime, the L1 and L2 NRHOs are proposed as long horizon trajectories for cislunar exploration missions, including NASA's upcoming Gateway mission. These stable or nearly stable orbits do not possess well-distinguished unstable and stable manifold structures. As a consequence, existing tools for stationkeeping and transfer trajectory design that exploit such underlying manifold structures are not reliable for orbits that are linearly stable. The current investigation focuses on leveraging stretching direction as an alternative for visualizing the flow of perturbations in the neighborhood of a reference trajectory. The information supplemented by the stretching directions are utilized to investigate the impact of maneuvers for two contrasting applications; the stationkeeping problem, where the goal is to maintain a spacecraft near a reference trajectory for a long period of time, and the transfer trajectory design application, where rapid departure and/or insertion is of concern.</div><div><br></div><div>Particularly, for the stationkeeping problem, a spacecraft incurs continuous deviations due to unmodeled forces and orbit determination errors in the complex multi-body dynamical regime. The flow dynamics in the region, using stretching directions, are utilized to identify appropriate maneuver and target locations to support a long lasting presence for the spacecraft near the desired path. The investigation reflects the impact of various factors on maneuver cost and boundedness. For orbits that are particularly sensitive to epoch time and possess distinct characteristics in the higher-fidelity ephemeris model compared to their CR3BP counterpart, an additional feedback control is applied for appropriate phasing. The effect of constraining maneuvers in a particular direction is also investigated for the 9:2 synodic resonant southern L2 NRHO, the current baseline for the Gateway mission. The stationkeeping strategy is applied to a range of L1 and L2 NRHOs, and validated in the higher-fidelity ephemeris model.</div><div><br></div><div>For missions with potential human presence, a rapid transfer between orbits of interest is a priority. The magnitude of the state variations along the maximum stretching direction is expected to grow rapidly and, therefore, offers information to depart from the orbit. Similarly, the maximum stretching in reverse time, enables arrival with a minimal maneuver magnitude. The impact of maneuvers in such sensitive directions is investigated. Further, enabling transfer design options to connect between two stable orbits. The transfer design strategy developed in this investigation is not restricted to a particular orbit but applicable to a broad range of stable and nearly stable orbits in the cislunar space, including the Distant Retrograde Orbit (DROs) and the Low Lunar Orbits (LLO) that are considered for potential missions. Examples for transfers linking a southern and a northern NRHO, a southern NRHO to a planar DRO, and a southern NRHO to a planar LLO are demonstrated.</div>

Aim of this thesis is building of a simulator model of a rotary (Furuta) pendulum and design of appropriate regulators. This paper describes assembly of a nonlinear simulator model, using Matlab–Simulink and its library Simscape–Simmechanics. Furthermore the paper discuss linear discrete model obtained from the system response, using least squares method. This linear model serves as aproximation of the system for designing of two linear discrete state space regulators with sumator. These regulators are supported by a simple swing–up regulator and logics managing cooperation.