In the wake of rising energy costs, there is a critical need for sustainable energy management of commercial and residential buildings. Buildings consume approximately 40% of total energy consumed in the US, and current methods to reduce this level of consumption include energy monitoring, smart sensing, and advanced integrated building control. However, the building industry has been slow to replace current PID and rule-based control strategies with more advanced strategies such as model-based building control. This is largely due to the additional cost of accurately modeling the dynamics of the building and the general uncertainty that model-based controllers can be reliably used in real conditions. The first half of this thesis addresses the challenge of constructing accurate grey-box building models for control using model identification. Current identification methods poorly estimate building model parameters because of the complexity of the building model structure, and fail to do so quickly because these methods are not scalable for large buildings. Therefore, we introduce the notion of parameter identifiability to determine those parameters in the building model that may not be accurately estimated and we use this information to strategically improve the identifiability of the building model. Finally, we present a decentralized identification scheme to reduce the computational effort and time needed to identify large buildings. The second half of this thesis discusses the challenge of using uncertain building models to reliably control building temperature. Under real conditions, building models may not match the dynamics of the building, which directly causes increased building energy consumption and poor thermal comfort. To reduce the impact of model uncertainty on building control, we pose the model-based building control problem as a robust control problem using well-known H1 control methods. Furthermore, we introduce a tuning law to reduce the conservativeness of a robust building control strategy in the presence of high model uncertainty, both in a centralized and decentralized building control framework.

The Hydro Muscle is a soft linear actuator which utilizes hydraulic pressure and elastic properties of its core for actuation. The Hydro Muscle has been recruited to actuate bio-inspired robot systems using a classic set point tracking feedback control system. A more efficient method is to develop a model-based control system which uses a dynamic model of the Hydro Muscle. The dynamic behavior of the Hydro Muscle which describes the relation between the forces exerted to the resultant motion can be studied with the help of a dynamic viscoelastic model. A dynamic viscoelastic model defines the force exerted by the Hydro Muscle as a function of the hydraulic pressure, the tensile expansion of the Hydro Muscle and the rate of its tensile expansion. Multivariable linear regression is employed to generate a model to relate fluid pressure, tensile expansion, and the rate of tensile expansion to the force exerted by the Hydro Muscle. The developed model can be utilized to implement a model-based control algorithm for the force control of individual joints. This model-based control design could be extended to systems involving multiple Hydro Muscles to allow for a modular control system. The design and test of multi-fiber Hydro Muscle actuated biologically inspired ankle is considered to study control strategies for multi-fiber system. A set-point tracking control algorithm with a proportional differential controller is used to minimize the tracking error. Modular force variation with sequential recruitment of Hydro Muscle is studied.

Avec le développement de l'industrie électronique, on constate un nombre croissant de projets avec des contraintes matérielles et temporelles de plus en plus élevées, ce qui conduit à l'utilisation de FPGA (Field Programmable Gate Arrays). Pour cela, le concepteur doit avoir une bonne connaissance de la programmation VHDL car cela nécessite beaucoup de formation et de pratique pour maîtriser ces architectures. Mais même pour les spécialistes, le processus de développement prend beaucoup de temps. Par conséquent, le développement d'un outil pour aider les utilisateurs non experts à travailler sur FPGA est nécessaire.Des outils tels que Simulink+HDL coder proposent une interface graphique pour créer un design en posant des blocs sur un tableau et en les connectant. Malheureusement, ce type d’outil souffre de deux défauts. Le premier est qu'il ne prend pas en compte les caractéristiques physiques de l'architecture cible. L'autre est qu'il ne vérifie pas si les flux de données entrant sont traités correctement par le design. Cela oblige le développeur à créer de nombreux tests, ce qui est fastidieux et consommateur en temps. Par conséquent, ce n’est pas une solution adaptée pour produire des applications dans un environnement en temps réel et des contraintes matérielles strictes.Pour gérer la complexité et la taille croissante des designs, l’abstraction est devenue graduellement essentielle. Des modèles ont émergé afin de représenter un design comme un graphe d’acteurs (c.a.d. de blocs), avec une analyse statique de l’exécution du graphe. Néanmoins, ces modèles sont basés sur une description plus ou moins fidèle du comportement d’architecture réelles telles que les FPGAs.Dans cette thèse, nous nous concentrons sur l'étude d’un nouveau modèle et d’un nouvel outil logiciel pour aider les utilisateurs non experts à concevoir automatiquement des implémentations correctes de FPGA. Les principales contributions sont résumées comme suit:1. Les limitations des modèles SDF existants, en particulier ceux du modèle SDF-AP, sont décrites et illustrées par l'analyse d'exemples caractéristiques. Les deux problèmes les plus courants rencontrés dans les implémentations d'assemblages de blocs sont la production de résultats incorrects et la croissance infinie de la taille du tampon.2. Nous proposons un nouveau modèle appelé "Actors with Stretchable Access Patterns" (ASAP) qui décrit le comportement matériel de façon mins limitée que les approches antérieures. Il s'agit d'une manière originale de résoudre le problème d'ordonnancement des acteurs, adaptée aux FPGAs. Il permet de déterminer l'exactitude mathématique d'une exécution sans lancer de simulations complexes. Il peut non seulement modéliser correctement les comportements des acteurs, mais aussi éviter les inconvénients mentionnés ci-dessus. Des algorithmes implémentant ces principes sont également fournis.3. Nous avons étudié des stratégies et des algorithmes connexes pour analyser un graphe représentant un design. L’exactitude du traitement peut être analysée par une série d'algorithmes permettant par exemple la vérification de la vitesse des flux et la vérification de la compatibilité des patterns. Il est ainsi possible de calculer la vitesse de décimation ou la longueur de délais à appliquer sur les entrées lorsqu'une erreur de correction est détectée.4. Un logiciel d’aide à la création de design est également développé. Il est appelé BlAsT (Block Assembly Tool) et vise à compenser les inconvénients des outils similaires tels que Simulink + HDL. Dans BlAsT, les algorithmes du modèle ASAP sont utilisés pour vérifier que pour un flux d'entrée donné, le système peut produire un résultat correct et finalement générer des codes VHDL directement utilisables sur une carte FPGA réelle. De plus, l'outil détermine automatiquement les décimations et les modifications requises. Ainsi, un utilisateur sans aucune compétence de programmation, est capable créer un design pour FPGA. / With the development of electronic industry, a growing number of projects require real-time streaming applications on embedded platforms. These comprise increasingly high hardware and timing constraints, which leads to the use of FPGAs (Field Programmable Gate Arrays). Usually, the designer should have a good knowledge of programming with VHDL or Verilog HDL. Unfortunately, only specialists can do it, because this needs a lot of training and practices to master these architectures. Furthermore, even for specialists, the process of development is quite time consuming. Therefore, how to develop a tool to help non-expert users working on FPGA is a promising but challenging work.Tools like Simulink+HDL coder provide a graphical interface to create a design, by putting functional blocks on a layer and to connect them. Nevertheless, such tools are generally suffering from two flaws. One is that they do not take the physical characteristics of the target architecture of the application into account, including that of the selected FPGA. The other one is that they do not check whether a data stream is processed correctly by the design, besides creating many test-benches, which is tedious and time consuming for the developer. Therefore, they are not suitable to produce applications in real-time environment and high hardware constraints.In order to manage the ever-increasing size and complexity of designs, the abstraction is gradually more and more essential. Some models have emerged to represent a design as a graph of actors (i.e. blocks), with a static analysis of the graph execution. Nevertheless, they have an unfaithful description of the behavior real architectures like an FPGA.In this dissertation, we concentrate on the study of a novel model and software tool that can help non-expert users for automatic design of FPGA implementations correctly. The main contributions are summarized as follows:1. The limits of existing SDF models, in particular those of the SDF-AP model, are described and illustrated by the analysis of characteristic examples. The two most common problems encountered in block assembly implementations are the production of incorrect results and the infinite growth of buffer size.2. We propose a new model called Actors Stretchable Access Patterns (ASAP) that describes the hardware behaviors as efficiently and precisely as possible. This is a novel way to address the scheduling problem of actors, adapted to FPGA architectures. It opens the possibility to determine the execution correctness mathematically without launching complex simulations. It can not only model actors' behaviors properly, but also avoid the above mentioned drawbacks. Algorithms that implement these principles also provided.3. We investigate strategies and related algorithms to analyze a graph representing a designed system. Its correctness can be analyzed by a series of algorithms, such as sample rate checking and pattern compatibility checking. The decimation rate or the delay length to be applied on actor's input can be computed when a correctness failure is detected. This increases the number of possible real FPGA implementations covered by the block assembly method.4. A software tool based on the concept of functional block graph is also developed. It is called BlAsT (Block Assembly Tool) and aims to compensate the drawbacks of other tools based on the same concepts, as for example Simulink + HDL coder. In BlAsT, the proposed ASAP model and related algorithms are used to check that for a given input stream, whether the system can produce a correct result and finally generate VHDL code directly usable on a real FPGA-based board. Otherwise, the tool determines the required decimations and modifications on the graph automatically. It makes a user without any programming skills to make designs on FPGAs thanks to the friendly graphic interface.

<p>Stronger environmental awareness as well as actual and future legislations increase</p><p>the demands on diagnosis and supervision of any vehicle with a combustion engine.</p><p>Particularly this concerns heavy duty trucks, where it is common with long driving</p><p>distances and large engines. Model based diagnosis is an often used method in</p><p>these applications, since it does not require any hardware redundancy.</p><p>Undesired changes in the intake manifold pressure can cause increased emissions.</p><p>In this thesis a diagnosis system for supervision of the intake manifold</p><p>pressure is constructed and evaluated. The diagnosis system is based on a Mean</p><p>Value Engine Model (MVEM) of the intake manifold pressure in a diesel engine</p><p>with Exhaust Gas Recirculation (EGR) and Variable Geometry Turbine (VGT).</p><p>The observer-based residual generator is a comparison between the measured intake</p><p>manifold pressure and the observer based estimation of this pressure. The</p><p>generated residual is then post treated in the CUSUM algorithm based diagnosis</p><p>test.</p><p>When constructing the diagnosis system, robustness is an important aspect. To</p><p>achieve a robust system design, four different observer approaches are evaluated.</p><p>The four approaches are extended Kalman filter, high-gain, sliding mode and an</p><p>adaption of the open model. The conclusion of this evaluation is that a sliding</p><p>mode approach is the best alternative to get a robust diagnosis system in this</p><p>application. The CUSUM algorithm in the diagnosis test improves the properties</p><p>of the diagnosis system further.</p>

<p>Technical systems of today are often complex and integrated. If a fault occurs, the consequences can be disastrous both for the system itself and its surroundings. To maintain the operation and the security it is necessary to have a surveillance system which can detect a fault in an early stage.</p><p>In this thesis two structural methods for fault isolation analysis are discussed. The result from the studied algorithms shows what fault isolation properties a diagnostic model is expected to have. If the isolability is not good enough, it also gives information on where further modelling needs to be done. </p><p>To base a comparison of the two structural analysis algorithms on, four criteria are defined concerning for example realizability of residuals and time complexity. One interesting part of the methods is how dynamic models are handled. It is shown how differential constraints can end up in differential cycles which implies calculatory problems and what effects structural differentiation has on a system. </p><p>The algorithms have been tested on an application from the research training network DAMADICS. The result shows how different types of input models in this case give the same result.</p>

<p>When developing products, reliability is an important factor that has to be considered. For safety critical systems it is important to know the probability that an item will perform a required function without failure under stated conditions for a stated period of time. The main goal of a reliability prediction analysis is to predict the rate at which the product of a system will fail. To perform this prediction there are a number of methodologies available.</p><p>This Master Thesis proposes a model-based approach for reliability prediction calculations based on the physics of failure and supported by analysis of test-data field returns and physical models provided by the FIDES methodology. FIDES based reliability models have been integrated into a model-based diagnosis environment for seamless integration with other safety assessment analysis.</p><p>The model-based diagnosis environment used in this thesis is model-based reasoner RODON developed by Uptime Solutions AB. Components that uses the FIDES methodology have been developed in RODON, where components can be combined to systems by drag and drop method. Usage profiles that are defined according to the FIDES methodology in RODON are not system specific, which makes them reusable in other systems. The developed library of components and usage profiles makes it easy to model complex systems and perform reliability predictions according to the FIDES methodology.</p>

<p>New emission legislations introduced in the European Union and the U.S. have made truck manufacturers face stricter requirements for low emissions and on-board diagnostic systems. The on-board diagnostic system typically consists of several tests that are run when the truck is driving. One way to construct such tests is to use so called consistency relations. A consistency relation is a relation with known variables that in the fault free case always holds. Calculation of a consistency relation typically involves eliminating unknown variables from a set of equations.</p><p>To eliminate variables from a differential polynomial system, methods from differential algebra can be used. In this thesis, the purely algebraic Gröbner basis algorithm and the differential Rosenfeld-Gröbner algorithm implemented in the Maple package Diffalg have been compared and evaluated. The conclusion drawn is that there are no significant differences between the methods. However, since using Gröbner basis requires differentiations to be made in advance, the recommendation is to use the Rosenfeld-Gröbner algorithm.</p><p>Further, attempts to calculate consistency relations using the Rosenfeld-Gröbner algorithm have been made to a real application, a model of a Scania diesel engine. These attempts did not yield any successful results. It was only possible to calculate one consistency relation. This can be explained by the high complexity of the model.</p>

<p>As machines are increasingly used to fulfill even more needs of mankind, the dependence upon those machines increase. To prevent catastrophic failure and to facilitate maintenance a diagnostic system can be used. A diagnostic system supervises the system and can alarm the operator when a fault has occurred, and possibly determine what the cause may be. One architecture of a diagnostic system is a number of tests run by an on-board computer checking certain combinations of sensor values and control signals chosen in advance. To design these tests is a difficult task, which leads to the desire to automate the test construction. A part of this task can be performed using structural methods.</p><p>In this thesis model based diagnosis is considered. This means that a formal mathematical model is used. The models typically consist of a number of equations describing the behavior of the system. In structural methods it is only considered if a variable exists in an equation or not. The goal of this master thesis project has been to apply structural methods to RODON models. RODON is a software diagnostics tool brought to market by Sörman Information & Media, which can perform various diagnostic-related tasks based on a single model. This model is defined in an object oriented fashion using a Modelica-like language called Rodelica. A prototype implementation of a structural algorithm plug-in has been developed and integrated into RODON. An additional part of the project has been to investigate further possible uses of structural algorithms in RODON, apart from diagnostic test construction. This has been performed as a series of interviews with Sörman and university employees.</p><p>The work performed in this thesis has shown that it is possible to apply structural methods to RODON models. It has also shown that even a prototype implementation can handle quite large systems. Some problems have been found as well, most notably in extracting a structural model from a RODON model. A consequence is that the developed structural plug-in only works for a subset of RODON models. It might be possible to deal with these problems if more time would be spent on the task. Finally, the interview survey revealed other possible uses of structural methods in RODON, including optimal sensor placement analysis and isolability and detectability analysis.</p>

<p>Diagnosis is the task of finding faults or malfunctioning components in a technical system, e.g a car. When doing diagnosis on cars with combustion engines, a computer program can be used. The computer program, also called diagnosis system, needs information about the car. This information could be data sheets of all the electronic components in the car. It could also be a description of how the engine behaves in a nominal and a non-nominal case. This information is contained in a model of the engine. RODON, a diagnostic tool developed by Sörman Information and Media AB, uses models of systems for conflict detection diagnosis. RODON needs fault models of the components to do diagnosis. The diagnosis system is then used in workshops, factories, or other places where cars need to be surveyed.</p><p>In this thesis, a Simulink model of the nominal behaviour of a combustion engine is given. The problem is how to make use of the model as well as the diagnostic tool RODON for combustion engine diagnosis. To solve this, the Simulink model is translated into a RODON model. Translating a Simulink model into a RODON model requires a new library in RODON. The library developed in this thesis is called AdvancedBlocks library.</p><p>The Simulink model describes the nominal behaviour of a combustion engine but for diagnosis with RODON, fault models are needed as well. Several types of faults that can occur in an engine have been studied and fault models have been implemented in RODON. The conclusion is that diagnosis in RODON with a translated engine model is possible.</p>

As exploration of our solar system and outerspace move into the future, spacecraft are being developed to venture on increasingly challenging missions with bold objectives. The spacecraft tasked with completing these missions are becoming progressively more complex. This increases the potential for mission failure due to hardware malfunctions and unexpected spacecraft behavior. A solution to this problem lies in the development of an advanced fault management system. Fault management enables spacecraft to respond to failures and take repair actions so that it may continue its mission. The two main approaches developed for spacecraft fault management have been rule-based and model-based systems. Rules map sensor information to system behaviors, thus achieving fast response times, and making the actions of the fault management system explicit. These rules are developed by having a human reason through the interactions between spacecraft components. This process is limited by the number of interactions a human can reason about correctly. In the model-based approach, the human provides component models, and the fault management system reasons automatically about system wide interactions and complex fault combinations. This approach improves correctness, and makes explicit the underlying system models, whereas these are implicit in the rule-based approach. We propose a fault detection engine, Compiled Mode Estimation (CME) that unifies the strengths of the rule-based and model-based approaches. CME uses a compiled model to determine spacecraft behavior more accurately. Reasoning related to fault detection is compiled in an off-line process into a set of concurrent, localized diagnostic rules. These are then combined on-line along with sensor information to reconstruct the diagnosis of the system. These rules enable a human to inspect the diagnostic consequences of CME. Additionally, CME is capable of reasoning through component interactions automatically and still provide fast and correct responses. The implementation of this engine has been tested against the NEAR spacecraft advanced rule-based system, resulting in detection of failures beyond that of the rules. This evolution in fault detection will enable future missions to explore the furthest reaches of the solar system without the burden of human intervention to repair failed components.