Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs. / text

L’identité de Ward est une relation qui permet d’identifier unmilieu de propagation linéaire dissipatif, c'est-à-dire d'estimer des paramètres qui le caractérisent. Dans les travaux exposés, cette identité est utilisée pour proposer de nouveaux modèles d’observation caractérisant un contexte d’estimation qualifié de passif : les sources qui excitent le système ne sont pas contrôlées par l’utilisateur. La théorie de l’estimation/détection dans ce contexte est étudiée et des analyses de performances sont menées sur divers estimateurs. La portée applicative des méthodes proposées concerne le domaine du Structural Health Monitoring (SHM), c’est-à-dire le suivi de l’état de santé desbâtiment, des ponts... L'approche est développée pour la modalité acoustique aux fréquences audibles, cette dernière s'avérant complémentaire des techniques de l’état de l’art du SHM et permettant entre autre, d’accéder à des paramètres structuraux et géométriques. Divers scénarios sont illustrés par la mise en oeuvre expérimentale des algorithmes développés et adaptés à des contraintes de calculs embarqués sur un réseau de capteurs autonome. / Ward identity is a relationship that enables damped linear system identification, ie the estimation its caracteristic properties. This identity is used to provide new observation models that are available in an estimation context where sources are uncontrolled by the user. An estimation and detection theory is derived from these models and various performances studies areconducted for several estimators. The reach of the proposed methods is extended to Structural Health Monitoring (SHM), that aims at measuring and tracking the health of buildings, such as a bridge or a sky-scraper for instance. The acoustic modality is chosen as it provides complementary parameters estimation to the state of the art in SHM, such as structural and geometrical parameters recovery. Some scenarios are experimentally illustrated by using the developed algorithms, adapted to fit the constrains set by embedded computation on anautonomous sensor network.

[pt] Atuadores baseados em materiais piezelétricos apresentam características ideais para aplicações como transmissão acústica e micromanipulação. No entanto, não-linearidades inerentes a estes atuadores, como histerese e fluência, aumentam o desafio de controla-los. Além disso, a crescente necessidade de atuadores mais precisos e rápidos aliada a frequentes mudanças nas condições ambientais e operacionais agravam ainda mais o problema. Modelagens analíticas são específicas ao sistema ao qual foram feitas, o que significa que elas não são facilmente escalonáveis e eficientes para todos os tipos de sistemas. Adicionalmente, com o aumento da complexidade, os fenômenos que regem a física do sistema não são totalmente conhecidos, tornando difícil o desenvolvimento destes modelos. Este trabalho investiga esses desafios do ponto de vista da metodologia de identificação de sistemas e modelos baseados em dados para atuadores piezelétricos. A abordagem de modelagem caixa preta foi testada com dados experimentais adquiridos em um ambiente de laboratório para os estudos de caso de micromanipulação e transmissão acústica. Sinais de uso geral foram empregados como entrada de excitação do sistema de modo a acelerar a aquisição e estimação dos parâmetros. Parte dos modelos desenvolvidos foram validados com um conjunto de dados separado. Em ambos os casos foi necessário pré-processamento para otimização da quantidade de dados. Os modelos testados incluem a Média Móvel AutoRegressiva com entradas eXógenas (ARMAX), AutoRegressiva Não Linear com entradas eXógenas (NARX) com uma estrutura de rede neural artificial e Média Móvel AutoRegressiva Não Linear com entradas eXógenas (NARMAX). Os resultados mostram uma boa capacidade de prever as não-linearidades do micro manipulador e, portanto, a histerese em diferentes frequências de entrada. O sistema de transmissão acústica foi modelado com sucesso. Embora os resultados mostrem que ainda há espaço para melhorias, eles fornecem informações importantes sobre possíveis otimizações para o sistema uma vez que os modelos apresentados são uteis para janelas de predição curtas. / [en] Actuators based on piezoelectric materials have ideal characteristics for applications such as acoustic transmission and micromanipulation. However, the inherent nonlinearities of those actuators, such as hysteresis and creep, greatly increase the challenge to control such devices. Furthermore, the increasing need for more precise and faster actuators, allied with frequent changes in the environmental and operational conditions, further worsens the problem. Analytical models are application-specific, meaning that they are not easily and efficiently scalable to all systems. Also, with increased complexity, the understating of underlying phenomena is not fully documented, making it difficult to develop such models. This work investigates those challenges from the perspective of the system identification methodology and data-driven models for piezoelectric actuators. The black-box approach is tested with experimental data acquired in a laboratory setting for micromanipulator and acoustic transmission case studies. In some datasets, general-purpose signals were employed as the excitation input of the system to accelerate the data acquisition of the whole system dynamic and estimation process. Additionally, some models were validated on a separate dataset. In both cases, preprocessing was employed to optimize the amount of data. The tested models include the AutoRegressive Moving Average with eXogenous inputs (ARMAX), Nonlinear AutoRegressive with eXogenous inputs (NARX) with an artificial neural network structure, and Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX). The results show a good ability to predict the nonlinearities of the micromanipulator and, therefore, the hysteresis at different input frequencies. The acoustic transmission system was successfully modeled. Although the results show that there is still room for improvements, it provides insights into possible optimizations for the setup as the models here devised are useful for short prediction windows.