Cette thèse traite du problème de la locomotion des robots humanoïdes dans le contexte du projet européen KoroiBot. En s'inspirant de l'être humain, l'objectif de ce projet est l'amélioration des capacités des robots humanoïdes à se mouvoir de façon dynamique et polyvalente. Le coeur de l'approche scientifique repose sur l'utilisation du controle optimal, à la fois pour l'identification des couts optimisés par l'être humain et pour leur mise en oeuvre sur les robots des partenaires roboticiens. Cette thèse s'illustre donc par une collaboration à la fois avec des mathématiciens du contrôle et des spécialistes de la modélisation des primitives motrices. Les contributions majeures de cette thèse reposent donc sur la conception de nouveaux algorithmes temps-réel de contrôle pour la locomotion des robots humanoïdes avec nos collégues de l'université d'Heidelberg et leur intégration sur le robot HRP-2. Deux contrôleurs seront présentés, le premier permettant la locomotion multi-contacts avec une connaissance a priori des futures positions des contacts. Le deuxième étant une extension d'un travail réalisé sur de la marche sur sol plat améliorant les performances et ajoutant des fonctionnalitées au précédent algorithme. En collaborant avec des spécialistes du mouvement humain nous avons implementé un contrôleur innovant permettant de suivre des trajectoires cycliques du centre de masse. Nous présenterons aussi un contrôleur corps-complet utilisant, pour le haut du corps, des primitives de mouvements extraites du mouvement humain et pour le bas du corps, un générateur de marche. Les résultats de cette thèse ont été intégrés dans la suite logicielle "Stack-of-Tasks" du LAAS-CNRS. / This thesis covers the topic of humanoid robot locomotion in the frame of the European project KoroiBot. The goal of this project is to enhance the ability of humanoid robots to walk in a dynamic and versatile fashion as humans do. Research and innovation studies in KoroiBot rely on optimal control methods both for the identification of cost functions used by human being and for their implementations on robots owned by roboticist partners. Hence, this thesis includes fruitful collaborations with both control mathematicians and experts in motion primitive modeling. The main contributions of this PhD thesis lies in the design of new real time controllers for humanoid robot locomotion with our partners from the University of Heidelberg and their integration on the HRP-2 robot. Two controllers will be shown, one allowing multi-contact locomotion with a prior knowledge of the future contacts. And the second is an extension of a previous work improving performance and providing additional functionalities. In a collaboration with experts in human motion we designed an innovating controller for tracking cyclic trajectories of the center of mass. We also show a whole body controller using upper body movement primitives extracted from human behavior and lower body movement computed by a walking pattern generator. The results of this thesis have been integrated into the LAAS-CNRS "Stack-of-Tasks" software suit.

This thesis presents the work of two significant projects. In the first project, a suite of benchmark problems for grid energy management are presented which demonstrate several issues characteristic to the dynamic optimization of these systems. These benchmark problems include load following, cogeneration, tri-generation, and energy storage, and each one assumes perfect foresight of the entire time horizon. The Gekko Python package for dynamic optimization is introduced and two different solution methods are discussed and applied to solving these benchmarks. The simultaneous solve mode out-performs the sequential solve mode in each benchmark problem across a wide range of time horizons with increasing resolution, demonstrating the ability of the simultaneous mode to handle many degrees of freedom across a range of problems of increasing difficulty. In the second project, combined optimization of propulsion system design, flight trajectory planning and battery mass optimization is applied to solar-regenerative high-altitude long-endurance (SR-HALE) aircraft through a sequential iterative approach. This combined optimization approach yields an increase of 20.2% in the end-of-day energy available on the winter solstice at 35°N latitude, resulting in an increase in flight time of 2.36 hours. The optimized flight path is obtained by using nonlinear model predictive control to solve flight and energy system dynamics over a 24 hour period with a 15 second time resolution. The optimization objective is to maximize the total energy in the system while flying a station-keeping mission, staying within a 3 km radius and above 60,000 ft. The propulsion system design optimization minimizes the total energy required to fly the optimal path. It uses a combination of blade element momentum theory, blade composite structures, empirical motor and motor controller mass data, as well as a first order motor performance model. The battery optimization seeks to optimally size the battery for a circular orbit. Fixed point iteration between these optimization frameworks yields a flight path and propulsion system that slightly decreases solar capture, but significantly decreases power expended. Fully coupling the trajectory and design optimizations with this level of accuracy is infeasible with current computing resources. These efforts show the benefits of combining design and trajectory optimization to enable the feasibility of SR-HALE flight.

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.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2016-02-16T13:26:42Z No. of bitstreams: 1 arquivototal.pdf: 3970645 bytes, checksum: d514b848324ac20a549db632034383d7 (MD5) / Made available in DSpace on 2016-02-16T13:26:42Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3970645 bytes, checksum: d514b848324ac20a549db632034383d7 (MD5) Previous issue date: 2015-10-10 / The purpose of this study is to adapt and embed a navigation system and control of mobile robots, based on NMPC, in a low-cost board existent on the market, to provide sufficient com-putational resources so that the robot is able to converge, without losing performance, using the same horizons applied in a Laptop. The obtained results demonstrate the proposed scenario according with the experiments, proving that it is possible to use low cost boards, to a navigation system and control of mobile robots, based on NMPC, using the same predictive and control horizons applied in a Laptop. / A proposta desse trabalho é adaptar e embarcar um sistema de navegação e controle de robôs móveis, baseado em NMPC, em uma placa de baixo custo já existente no mercado, que dispo-nibilize recursos computacionais suficientes para que o Robô seja capaz de convergir, sem perda de desempenho e utilizando os mesmos horizontes aplicados em um Laptop. Os Resulta-dos obtidos demonstram todo o cenário proposto e de acordo com os experimentos realizados, comprovou-se que é possível o uso de placas de baixo custo, para controle de robôs móveis, baseado em NMPC, utilizando os mesmos horizontes de predição e controle aplicados em uma Laptop.

In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty. In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC. Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data. In another direction, the stability of data driven predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. Finally, I address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The effectiveness of all of the proposed methods are illustrated through simulations on nonlinear systems. / Dissertation / Doctor of Philosophy (PhD)

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.

Made available in DSpace on 2014-12-17T14:56:17Z (GMT). No. of bitstreams: 1 GasparFDJ_DISSERT.pdf: 3388304 bytes, checksum: 086a8f61099f69978a8b9f477f351d24 (MD5) Previous issue date: 2014-01-23 / Petr?leo Brasileiro SA - PETROBRAS / Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC / A golfada ? um regime inst?vel do fluxo multif?sico, com oscila??es de press?o e vaz?o abruptas no processo de produ??o de petr?leo, podendo ocasionar problemas tais como vibra??o na tubula??o e alto n?vel de l?quido nos separadores. Pode ser classificada de acordo com seu local de ocorr?ncia. A mais severa destas, conhecida como golfada no riser, ocorre na tubula??o vertical que alimenta a plataforma. Conhecida tamb?m como golfada severa, ela ? capaz de causar bruscas oscila??es na press?o, nas vaz?es do processo, vibra??o excessiva, inunda??o dos tanques separadores, produ??o limitada, parada n?o programada da plataforma, entre outros aspectos negativos que motivaram a produ??o deste trabalho. Uma solu??o vi?vel para lidar com tal problema seria projetar um m?todo efetivo para a remo??o ou diminui??o deste regime, como um controlador. De acordo com a literatura, o controlador convencional PID n?o apresenta bons resultados devido ao alto grau de n?o linearidade do processo, o que impulsionou o desenvolvimento de t?cnicas avan?adas de controle. Dentre estas, o controlador preditivo, cuja a??o de controle resulta da solu??o de um problema de otimiza??o, al?m de ser uma t?cnica que apresenta robustez e pode incorporar restri??es f?sicas e/ou de seguran?a. O objetivo deste trabalho ? estudar a aplica??o de uma t?cnica de controle preditivo n?o linear ao controle de golfada severa, visando controlar a quantidade de massa l?quida no riser atuando na v?lvula de produ??o e, indiretamente, suprimir as oscila??es de vaz?o e press?o. Com a finalidade de obter benef?cios ambientais e econ?micos. A t?cnica de controle preditivo proposta baseia-se no uso de aproxima??es lineares do modelo e na resolu??o repetida de um problema de otimiza??o quadr?tica que proporciona solu??es que melhoram a cada itera??o. No caso em que a converg?ncia desse algoritmo ? satisfeita, os valores preditos das vari?veis do processo s?o iguais ?queles que seriam obtidos pelo modelo n?o linear original, garantindo que as restri??es nessas vari?veis sejam satisfeitas ao longo do horizonte de predi??o. Um modelo matem?tico publicado recentemente na literatura, capaz de representar caracter?sticas da golfada severa em um po?o real, ? utilizado tanto para a simula??o, quanto para projeto do controlador proposto, cujo desempenho ? comparado ao de um controlador preditivo linear

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.

[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.