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.

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation.

We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called prototypes) of an object class. The models consist of a linear combination ofsprototypes. The flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. A novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest modelsimage. Currently, the algorithm applies to line drawings of objects. An extension to real grey level images is discussed.

In unmanned aerial systems an autopilot controls the vehicle without human interference. Modern autopilots use an inertial navigation system, GPS, magnetometers and barometers to estimate the orientation, position, and velocity of the aircraft. In order to make correct decisions the autopilot must rely on correct information from the sensors. Fault diagnosis can be used to detect possible faults in the technical system when they occur. One way to perform fault diagnosis is model based diagnosis, where observations of the system are compared with a mathematical model of the system. Model based diagnosis is a common technique in many technical applications since it does not require any additional hardware. Another way to perform fault diagnosis is hardware diagnosis, which can be performed if there exists hardware redundancy, i.e. a set of identical sensors measuring the same quantity in the system. The main contribution of this master thesis is a model based diagnosis system for a fixed wing UAV autopilot. The diagnosis system can detect faults in all sensors on the autopilot and isolate faults in vital sensors as the GPS, magnetometer, and barometers. This thesis also provides a hardware diagnosis system based on the redundancy obtained with three autopilots on a single airframe. The use of several autopilots introduces hardware redundancy in the system, since every autopilot has its own set of sensors. The hardware diagnosis system handles faults in the sensors and actuators on the autopilots with full isolability, but demands additional hardware in the UAV.

Scania CV AB is a leading company within development and production of buses, trucks as well as industrial and marine engines. New environmental and safety legislations continuously demand higher quality from the products. An upcoming European legislation, Euro 6, implies that gas leakages from truck engines should be detected while driving. If the source of the leakage is not only detected, but also isolated, that is separated from other faults, the adjustments in the workshop goes faster since there is no need for leakage localisation. A faster reparation increases the up-time, i.e. the amount of time that the truck can be used. This master thesis work uses current methods developed at Scania for residual generation to perform model-based leakage diagnosis. In this work, measurements are gathered for dierent sensor faults and two leakages. The measurements are used to evaluate the actual performance of the resulting diagnosis system. The result, based on the residuals generated by the method, shows that leakages on the boost-side and the exhaust-side can be detected, and isolated from faults in the pressure sensors on the boost-side and the exhaust-side. The isolation of these four faults is considered the hardest to achieve among sensor faults and leakages why the full isolation performance is promising. Further measurements are needed to determine the full isolation performance of the diagnosis system. The resulting system is reasoned to be suitable for execution in real time on-board the truck. / Scania CV AB är en ledande koncern inom utveckling och produktion av bussar, lastbilar samt industri- och marinmotorer. Nya lagkrav för miljö och säkerhet ställer ständigt högre krav på de tillverkade produkterna. Ett nära förestående lagkrav för lastbilar, Euro 6, innebär att gasläckage från motorn ska detekteras under körning. Om läckaget förutom att detekteras också kan isoleras, det vill säga särskiljas från andra fel, går reparationen i verkstaden snabbare då man slipper lokalisera läckaget. En snabbare reparation ökar up-time, det vill säga tiden som lastbilen kan användas på åkeriet. I detta exjobb används befintliga metoder för residualgenerering framtagna på Scania för att åstadkomma modelbaserad läckagediagnos. Arbetet tar även fram mätdata för olika givarfel samt för två läckage i motorn. Denna mätdata används för att utvärdera det erhållna diagnossystemets faktiska prestanda. Resultatet, som bygger på residualerna som metoden genererat, visar att läckage går att detektera, och att läckagen går att isolera från fel på tryckgivarsensorer på laddluftssidan och avgassidan. Denna isolering anses vara den svåraste att uppnå av alla sensorfel samt läckage varvid övrig isoleringsprestande verkar lovande. Däremot behövs mer mätdata för att säkert kunna fastställa övrig isoleringsprestanda. Diagnosmetoden lämpar sig troligen för exekvering i realtid ombord på lastbilen.

Large-scale production of plant secondary metabolites is of interest because of their application in production of many valuable products. Recent advances in the area of DNA recombinant technology has made it possible to produce these valuable compounds using microbial routes. The objective of this work was, to design a platform strain of Saccharomyces cerevisiae with improved intracellular tyrosine pools using computational modeling. This engineered yeast could be used as a host for producing important plant secondary metabolites on an industrial-scale. In this study, a combination of steady-state and dynamic modeling methods were used for strain design. Initial strain design was performed using steady-state modeling, and the predictions from steady-state modeling were prioritized for experimental validation using dynamic modeling. The final strategy proposed included deletion of PDC1, ZWF1, ARO10; over-expression of ALD6, and alleviation of tyrosine feedback resistance in shikimate pathway. Initial experiments for validation of this strategy showed promising results.

Large-scale production of plant secondary metabolites is of interest because of their application in production of many valuable products. Recent advances in the area of DNA recombinant technology has made it possible to produce these valuable compounds using microbial routes. The objective of this work was, to design a platform strain of Saccharomyces cerevisiae with improved intracellular tyrosine pools using computational modeling. This engineered yeast could be used as a host for producing important plant secondary metabolites on an industrial-scale. In this study, a combination of steady-state and dynamic modeling methods were used for strain design. Initial strain design was performed using steady-state modeling, and the predictions from steady-state modeling were prioritized for experimental validation using dynamic modeling. The final strategy proposed included deletion of PDC1, ZWF1, ARO10; over-expression of ALD6, and alleviation of tyrosine feedback resistance in shikimate pathway. Initial experiments for validation of this strategy showed promising results.

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

If a fault occurs in a technical system, for example in an airplane, it is important to beable to detect that there is a fault and to find what in the system that is faulty. Theprocedure of determining, given certain observations, if faults are present and if so thelocation of faults is called a diagnosis. For achieving diagnosis we can use computersoftware that takes observations of a system as input and that generates a diagnosis asoutput. This is called a diagnostic system. To build a diagnostic system we needanother piece of computer software which is called a diagnostic tool. This thesis willpresent a market survey for diagnostic tools as well as an analysis of three of the toolsfound in the survey. The analysis can be seen as constituted by two different aspects,one focusing on the diagnostic methods with which each tool creates diagnosticsystems, the other focusing on practical details that determine the usability of eachtool. The analysis found that the largest differences were between the methods used increating the diagnostic systems.

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. 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. 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.