This thesis presents the design of Kalman filter based attitude determination algorithms for a hypothetical LEO satellite and for a satellite attitude control test setup. For the hypothetical LEO satellite, an Extended Kalman Filter based attitude determination algorithms are formed with a multi-mode structure that employs the different sensor combinations and as well as online switching between these combinations depending on the sensor availability. The performance of these different attitude determination modes are investigated through Monte Carlo simulations. New attitude determination algorithms are prepared for the satellite attitude control test setup by considering the constraints on the selection of the suitable sensors. Here, performances of the Extended Kalman Filter and Unscented Kalman Filter are investigated. It is shown that robust and sufficiently accurate attitude estimation for the test setup is achievable by using the Unscented Kalman Filter.

In this thesis study, a valveless hydraulic servo system controlled by two pumps is investigated and its performance characteristics are compared with a conventional valve controlled system both experimentally and analytically. The two control techniques are applied on the position control of a single rod linear actuator. In the valve controlled system, the flow rate through the actuator is regulated with a servovalve / whereas in the pump controlled system, two variable speed pumps driven by servomotors regulate the flow rate according to the needs of the system, thus eliminating the valve losses. To understand the dynamic behaviors of two systems, the order of the differential equations defining the system dynamics of the both systems are reduced by using the fact that the dynamic pressure changes in the hydraulic cylinder chambers become linearly dependent on leakage coefficients and cylinder chamber volumes above and below some prescribed cut off frequencies. Thus the open loop speed response of the pump controlled and valve controlled systems are defined by v second order transfer functions. The two systems are modeled in MATLAB Simulink environment and the assumptions are validated. For the position control of the single rod hydraulic actuator, a linear state feedback control scheme is applied. Its state feedback gains are determined by using the linear and linearized reduced order dynamic system equations. A linear Kalman filter for pump controlled system and an unscented Kalman filter for valve controlled system are designed for estimation and filtering purposes. The dynamic performances of both systems are investigated on an experimental test set up developed by conducting open loop and closed loop frequency response and step response tests. MATLAB Real Time Windows Target (RTWT) module is used in the tests for application purposes.

Nowadays, high portion of tactical missiles use gimbaled seeker. For accurate target tracking, the platform where the gimbal is mounted must be stabilized with respect to the motion of the missile body. Line of sight stabilization is critical for fast and precise tracking and alignment. Although, conventional PID framework solves many stabilization problems, it is reported that many PID feedback loops are poorly tuned. In this thesis, recently introduced robot control method, proxy based sliding mode control, is adopted for the line of sight (LOS) stabilization. Before selecting the proposed method, adaptive neural network sliding mode control and fuzzy control are also implemented for comparative purposes. Experimental and simulation results show a satisfactory response of the proxy based sliding mode controller.

Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

Made available in DSpace on 2014-12-17T14:54:51Z (GMT). No. of bitstreams: 1 DiegoRCS.pdf: 1286138 bytes, checksum: 890aca771a97105bf912985058e417c2 (MD5) Previous issue date: 2008-12-11 / The present research aims at contributing to the area of detection and diagnosis of failure through the proposal of a new system architecture of detection and isolation of failures (FDI, Fault Detection and Isolation). The proposed architecture presents innovations related to the way the physical values monitored are linked to the FDI system and, as a consequence, the way the failures are detected, isolated and classified. A search for mathematical tools able to satisfy the objectives of the proposed architecture has pointed at the use of the Kalman Filter and its derivatives EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter). The use of the first one is efficient when the monitored process presents a linear relation among its physical values to be monitored and its out-put. The other two are proficient in case this dynamics is no-linear. After that, a short comparative of features and abilities in the context of failure detection concludes that the UFK system is a better alternative than the EKF one to compose the architecture of the FDI system proposed in case of processes of no-linear dynamics. The results shown in the end of the research refer to the linear and no-linear industrial processes. The efficiency of the proposed architecture may be observed since it has been applied to simulated and real processes. To conclude, the contributions of this thesis are found in the end of the text / O presente trabalho visa contribuir com a ?rea de detec??o e diagn?stico de falhas em sistemas din?micos atrav?s da proposta de uma nova arquitetura de sistemas de detec??o e isolamento de falhas (FDI, Fault Detection and Isolation). A arquitetura proposta traz inova??es no que se refere ? maneira como as grandezas f?sicas do processo monitorado s?o relacionadas ao sistema FDI e, em conseq??ncia disso, ? maneira como as falhas s?o detectadas, isoladas e classificadas. Uma busca por ferramentas matem?ticas capazes de satisfazer os objetivos da arquitetura proposta apontou para o uso do filtro de Kalman e seus derivados EKF (Extended Kalman Filter) e UKF (Unscented Kalman Filter). O uso do primeiro algoritmo mostra-se eficaz no caso em que o processo monitorado apresenta uma rela??o linear entre suas grandezas f?sicas a serem monitoradas e sua sa?da. Os outros dois, caso a din?mica seja n?o linear. Posteriormente, um comparativo entre o EKF e o UKF mostra que o segundo se adequa melhor ?s necessidades da arquitetura proposta. Os resultados mostrados no final da tese s?o referentes a plantas lineares e n?o-lineares, onde se pode observar a efic?cia da arquitetura proposta quando a mesma foi aplicada a processos simulados e reais

Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

Threaded fasteners tend to break and loosen when exposed to cyclic loads or potent temperature variations. Additionally, if the joint is held tightly to the structure, distortion will occur under thermal expansion issues. These complications can be prevented by identifying and regulating the clamping force to an appropriate degree – adapted to the properties of the joint. Torque-controlled tightening is a way of monitoring the clamping force, but it assumes constant friction and therefore has low accuracy, with an error of around 17% - 43%.This thesis investigates if the friction and clamping force can be estimated using the Extended and Unscented Kalman filters to increase the precision of the torque-controlled methodology. Before the investigation, data were collected for two widely used tightening strategies. The first tightening strategy is called Continuous Drive, where the angular velocity is kept at a constant speed while torque is increased. The second strategy is TurboTight, where the angular velocity starts at a very high speed and decreases with increased torque. The collected data were noisy and had to be filtered. A hybrid between a Butterworth lowpass filter and a Sliding Window was developed and exploited for noise cancellation.The investigations revealed that it was possible to use both the Extended and Unscented Kalman filers to estimate friction and clamping force in threaded fasteners. In Continuous Drive tightening, both the EKF and UKF performed well - with an averagequality factor of 81.87% and 88.38%, and with an average error (at max torque) of 3.54% and 4.09%, respectively. However, the TurboTight strategy was much more complex and had a higher order of statistical moments to account for. Thus, the UKF outperformed the EKF with an average quality factor of 93.02% relative to 24.49%, and with an average error (at max torque) of 3.50% compared to 4.19%

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The ability to rapidly assess the condition of a structure in a manner which enables the accurate prediction of its remaining capacity has long been viewed as a crucial step in allowing communities to make safe and efficient use of their public infrastructure. This objective has become even more relevant in recent years as both the interdependency and state of deterioration in infrastructure systems throughout the world have increased. Current practice for structural condition assessment emphasizes visual inspection, in which trained professionals will routinely survey a structure to estimate its remaining capacity. Though these methods have the ability to monitor gross structural changes, their ability to rapidly and cost-effectively assess the detailed condition of the structure with respect to its future behavior is limited.<div>Vibration-based monitoring techniques offer a promising alternative to this approach. As opposed to visually observing the surface of the structure, these methods judge its condition and infer its future performance by generating and updating models calibrated to its dynamic behavior. Bayesian inference approaches are particularly well suited to this model updating problem as they are able to identify the structure using sparse observations while simultaneously assessing the uncertainty in the identified parameters. However, a lack of consensus on efficient methods for their implementation to full-scale structural systems has led to a diverse set of Bayesian approaches, from which no clear method can be selected for full-scale implementation. The objective of this work is therefore to assess and enhance those techniques currently used for structural identification and make strides toward developing unified strategies for robustly implementing them on full-scale structures. This is accomplished by addressing several key research questions regarding the ability of these methods to overcome issues in identifiability, sensitivity to uncertain experimental conditions, and scalability. These questions are investigated by applying novel adaptations of several prominent Bayesian identification strategies to small-scale experimental systems equipped with nonlinear devices. Through these illustrative examples I explore the robustness and practicality of these algorithms, while also considering their extensibility to higher-dimensional systems. Addressing these core concerns underlying full-scale structural identification will enable the practical application of Bayesian inference techniques and thereby enhance the ability of communities to detect and respond to the condition of their infrastructure.<br></div>

Kalman filtering is a well-established method for fusing sensor data in order to accuratelyestimate unknown variables. Recently, the unscented Kalman filter (UKF) has beenused due to its ability to propagate the first and second moments of the probability distribution of an estimated state through a non-linear transformation. The design of ageneric algorithm which implements this filter occupies the first part of this thesis. The generality and functionality of the filter were tested on a toy example and the results are within machine accuracy when compared to those of an equivalent C++ implementation.Application of this filter to the attitude navigation problem becomes non-trivial when coupled to quaternions. Challenges present include the non-commutation of rotations and the dimensionality difference between quaternions and the degrees of freedom of the motion. The second part of this thesis deals with the formulation of the UKF in the quaternion space. This was achieved by implementing an error-state formulation of the process model, bounding estimation in the infinitesimal space and thus de-coupling rotations from non-commutation and bridging the dimensionality discrepancy of quaternions and their respective covariances.The attitude navigation algorithm was then tested using an IMU and a magnetometer.Results show a bounded estimation error which settles to around 1 degree. A detailed look of the filter mechanization process was also presented showing expected behavior for estimation of the initial attitude with error tolerance of 1 mdeg. The structure and design of the proposed formulation allows for trivially incorporating other sensors inthe estimation process and more intricate modelling of the stochastic processes present,potentially leading to greater estimation accuracy. / Kalman filtrering är en vältablerad metod for att sammanväga sensordata för att erhålla noggranna estimat av okända variabler. Nyligen har den typ av kalman filter som kallas unscented Kalman filter (UKF) ökat i populäritet pa grund av dess förmåga att propagera de första och andra momenten för sannolikhetsfördelningen för ett estimera tillstånd genom en ickelinjär transformation. Designen av en generisk algoritm som implementerar denna typ av filter upptar den första delen av denna avhandling. Generaliteten och funktionaliteten för detta filter testades på ett minimalt exempel och resultaten var identiska med de för en ekvivalent C++-implementation till den noggrannhet som tillåts av den nita maskinprecisionen. Användandet av detta filter för attitydnavigering blir icke-trivialt när det anvands forkvaternioner. De utmaningar som uppstar inkluderar att rotationer inte kommuterar och att de finns en skillnad i dimensionalitet mellan kvaternioner och antalet frihetsgrader i rörelsen. Den andra delen av denna avhandling behandlar formuleringen av ett UKF för ett tillstånd som inkluderar en kvaternion. Detta gjordes genom att implementera en så kallad error state-formulering av processmodellen, vilken begränsar estimeringen till ett innitesimalt tillstånd och därigenom undviker problemen med att kvaternionmultiplikation inte kommuterar och överbryggar skillnaden i dimensionalitet hos kvaternioner och deras motsvarande vinkelosäkerheter.Attitydnavigeringen testades sedan med hjälp av en IMU och en magnetometer.Resultaten visade ett begränsat estimeringsfel som ställer in sig kring 1 grad. Strukturen och designen av den föreslagna formuleringen möjliggör på ett rattframt satt tillägg av andra sensorer i estimeringsprocessen och mer detaljerad modellering av de stokastiska processerna, vilket potentiellt leder till högre estimering noggrannhet.