<div><div><div><p>Structural identification is a useful tool for detecting damage and damage evolution in a structure. The initiation of damage in a structure and its subsequent growth are mainly associated with nonlinear behaviors. While linear dynamics of a structure are easy to simulate, nonlinear structural dynamics have more complex dynamics and amplitude dependence that do require more sophisticated simulation tools and identification methods compared to linear systems. Additionally, there are generally many more parameters in nonlinear models and the responses may not be sensitive to all of them for all inputs. To develop model selection methods, an experiment is conducted that uses an existing device with repeatable behavior and having an expected model from the literature. In this case, an MR damper is selected as the experimental device. The objective of this research is to develop and demonstrate a method to select the most appropriate model from a set of identified stochastic models of a nonlinear device. The method is developed using numerical example of a common nonlinear system, and is then implemented on an experimental structural system with unknown nonlinear properties. Bayesian methods are used because they provide a distinct advantage over many other existing methods due to their ability to provide confidence on answers given the observed data and initial uncertainty. These methods generate a description of the parameters of the system given a set of observations. First, the selected model of the MR damper is simulated and used for demonstrating the results on a numerical example. Second, the model selection process is demonstrated on an experimental structure based on experimental data. This study explores the use of the Bayesian approach for nonlinear structural identification and identifies a number of lessons for others aiming to employ Bayesian inference.</p></div></div></div>

Aircraft in the US are equipped with Emergency Locator Transmitters (ELTs). In emergency situations these beacons are activated, providing a radio signal that can be used to locate the aircraft. Recent developments in UAV technologies have enabled mini-UAVs (5-foot wingspan) to possess a high level of autonomy. Due to the small size of these aircraft they are human-packable and can be easily transported and deployed in the field. Using a custom-built Radio Direction Finder, we gathered readings from a known transmitter and used them to compare various Bayesian reasoning-based filtering algorithms. Using a custom-developed simulator, we were able to test and evaluate filtering and control methods. In most non-trivial conditions we found that the Sequential Importance Resampling (SIR) Particle Filter worked best. The filtering and control algorithms presented can be extended to other problems that involve UAV control and tracking with noisy non-linear sensor behavior.

Planetary rovers are robots that need to perform autonomous navigation, because of the long delay communication and no human assistance. Furthermore, they need to perform the optimal estimation of its position in order to have a good performance on its navigation system. The need for good performance filters for estimating the actual position of mobile robots of this kind is needed, due to the fact that sensors are noisy and that information is of vital importance for a planetary rover’s mission. Besides, good accurate sensors for the matter, are not easy to find for space application. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to analyze a data set of a 4-wheel robot, and later used for comparison on accuracy in the estimation of its pose. The analysis will give the possibility to know the right combination of sensors, recognize some issues during the trajectory. Furthermore, this study has been made with aims to give the reader knowledge of state of the art in planetary rovers, their constraints and consideration while developing them. The robot used for the research has been developed for an international competition of field robot automation. The main goal is to navigate autonomously through flowerpots performing different tasks as flowerpot collection, distance traveled and robustness on localization and navigation algorithms. / <p>Validerat; 20120822 (anonymous)</p>

This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downward-facing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system's effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications. / Master of Science

Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-10-04T14:03:27Z No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:18:34Z (GMT) No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:18:43Z (GMT) No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) / Made available in DSpace on 2016-10-14T14:18:52Z (GMT). No. of bitstreams: 1 DissDPL.pdf: 3012901 bytes, checksum: 29c2df84e5e59e8e598fae154f0983d2 (MD5) Previous issue date: 2016-03-04 / Não recebi financiamento / Kalman filters are rooted in the technical literature, as a way of predicting new states in nonlinear systems providing a recursive solution to the problem of linear optimal filtering. Therefore, 56 years after its discovery, many modifications have been proposed in order to obtain better accuracy and speed. Some of these changes are used in this work; these being the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Kalman Filter Cubature (CKF). This work , divided into three distinct parts: Implementation / Comparative analysis of prediction of Kalman filters in complex systems (Series), qualitative analysis of the possible uses of the Kalman filter variants for neural network training and position and velocity determination a displaced object on a simulated plane with some trajectories Having these analyzes key role in fostering the studies cited in the scientific literature , proving the possibility of such algorithms and methods are used for positioning in unstructured environments / Filtros de Kalman estão consagrados na literatura técnica, como uma das formas de prever novos estados em sistemas não-lineares, fornecendo uma solução recursiva para o problema da filtragem ideal linear. Após 56 anos de sua descoberta, muitas modificações e melhorias foram propostas, procurando obter uma maior precisão e velocidade na predição de novos estados. Algumas dessas mudanças são utilizadas neste trabalho; sendo elas o Filtro de Kalman Estendido (EKF), Unscented Kalman Filter (UKF) e Filtro de Kalman de Cubagem Esférica Radial (CKF).O objetivo deste trabalho, divido em três partes distintas, porém complementares: Implementação/Análise comparativa da predição dos Filtros de Kalman em sistemas complexos (Series), Análise qualitativa das possíveis utilizações das variantes do Filtro de Kalman para treinamento de Redes Neurais e Determinação de posição e velocidade de um objeto deslocado sobre um plano simulado. Possuindo essas análises papel fundamental na fomentação dos estudos citados na literatura científica durante o trabalho, e comprovando a possibilidade desses algoritmos/ métodos serem utilizados em tarefas de posicionamento em ambientes não estruturados.

The master’s thesis deals with a design of the computational method for the analysis of swimmers training with the use of an inertial measurement unit. The developed algorithm uses quaternion-based Unscented Kalman filter and merges accelerometer and gyroscope measurements. The proposed method enables analysis of velocity, acceleration and inclination of a swimmer. Verification of the method was based on an underwater video camera capturing and a tethered velocity meter.

This thesis investigates using adaptive estimation techniques to determine unknown model parameters such as size and surface material reflectivity, while estimating position, velocity, attitude, and attitude rates of a resident space object. This work focuses on the application of these methods to the space situational awareness problem. This thesis proposes a unique method of implementing a top-level gating network in a dual-layer hierarchical mixture of experts. In addition it proposes a decaying learning parameter for use in both the single layer mixture of experts and the dual-layer hierarchical mixture of experts. Both a single layer mixture of experts and dual-layer hierarchical mixture of experts are compared to the multiple model adaptive estimation in estimating resident space object parameters such as size and reflectivity. The hierarchical mixture of experts consists of macromodes. Each macromode can estimate a different parameter in parallel. Each macromode is a single layer mixture of experts with unscented Kalman filters used as the experts. A gating network in each macromode determines a gating weight which is used as a hypothesis tester. Then the output of the macromode gating weights go to a top level gating weight to determine which macromode contains the most probable model. The measurements consist of astrometric and photometric data from non-resolved observations of the target gathered via a telescope with a charge coupled device camera. Each filter receives the same measurement sequence. The apparent magnitude measurement model consists of the Ashikhmin Shirley bidirectional reflectance distribution function. The measurements, process models, and the additional shape, mass, and inertia characteristics allow the algorithm to predict the state and select the most probable fit to the size and reflectance characteristics based on the statistics of the measurement residuals and innovation covariance. A simulation code is developed to test these adaptive estimation techniques. The feasibility of these methods will be demonstrated in this thesis.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

The problem of system identification for dynamic effects on spacecraft has become increasingly relevant with the surge of agile spacecraft, which must perform large amplitude maneuvers at high rates. Precise knowledge of the state of the spacecraft, as well as of the parameters characterizing its motion, is vital for the design of control algorithms enabling stabilization and pointing accuracy. Traditional rigid body models and estimation methods are no longer sufficient to provide this knowledge. This thesis focuses on estimation of flexibility effects and spacecraft parameters through methods based on the unscented Kalman filter, an estimator for nonlinear dynamic systems. A spacecraft model consisting of a rigid central body and a flexible appendage described as an Euler-Bernoulli beam in pure bending is built, and equations for its translational and rotational motion, as well as the deflection of the beam, are derived in the Newton-Euler framework considering the first bending mode of the flexible deformation. Observability tests are successfully conducted to ensure that estimation of the relevant states and parameters can be performed exclusively from linear and angular velocity measurements. A total of eight filters, estimating the spacecraft’s state along with different combinations of parameters, are developed, implemented, and tested on simulated data. Grouped under the common denomination “UFFE” (Unscented Filter for Flexibility Effects), they are made available as Simulink library blocks. State estimation is performed for the linear and angular velocities of the spacecraft and the modal coordinate and velocity of the appendage, with estimates following closely the truth model of the state variables and estimation errors at least an order of magnitude lower than true state values. Simultaneous state and parameter estimation is implemented from two approaches, joint estimation and dual estimation, whose performance and applications are compared. Estimated parameters include the moments of inertia of the system and natural frequency, damping ratio, and modal participation factors of the flexible appendage. Convergence to true parameter values is reached in the first 100s of the estimation for inertia terms and natural frequency, while the estimation for modal participation factors is conditioned to precise tuning of the filter. Estimates of the damping ratio are biased, most likely due to the control input not being optimal for observation of this parameter. The dual approach to parameter estimation is found to be advantageous when proper filter tuning is possible, as it enables the continuous operation of a state filter combined with short runs of the parameter filter activated at will; this configuration could be employed to track the variation of spacecraft parameters along space missions.  The causes of estimation error are identified and methods for automatic tuning of the process noise and process noise covariance are researched. Five such tuning techniques are implemented and tested, with promising results found for online sampling of the process noise covariance through Monte Carlo methods. A discussion on the limitations of the chosen dynamic model and estimator, along with recommendations for extensions and future applications, concludes this work.

A novel structural health assessment (SHA) technique is proposed. It is a finite element-based time domain nonlinear system identification technique. The procedure is developed in two stages to incorporate several desirable features and increase its implementation potential. First, a weighted global iteration with an objective function is introduced in the unscented Kalman filter (UKF) procedure in order to obtain stable, convergent, and optimal solution. Furthermore, it also improves the capability of the UKF procedure to identify a large structural system using only a short duration of responses measured at a limited number of dynamic degrees of freedom (DDOFs). The combined procedure is denoted as unscented Kalman filter with weighted global iteration (UKF-WGI). Then, UKF-WGI is integrated with iterative least-squares with unknown input (ILS-UI) in order to increase its implementation potential. The substructure concept is also incorporated in the procedure. The integrated procedure is denoted as unscented Kalman filter with unknown input and weighted global iteration (UKF-UI-WGI). The two most important features of the method are that it does not need information on input excitation and uses only limited number of noise-contaminated response information to identify structural systems. Also, the method is able to identify the defects at the local element level by tracking the changes in the stiffness of the structural elements in the finite element representation. The UKF-UI-WGI procedure is implemented in two stages. In Stage 1, based on the location of input excitation, the substructure is selected. Using only responses at all DDOFs in the substructure, ILS-UI can identify the input excitation time-histories, stiffness parameters of all the elements in the substructure, and two Rayleigh damping coefficients. The outcomes of the first stage are necessary to initiate UKF-WGI. Using the information from Stage 1, the stiffness parameters of all the elements in the structure are identified using UKF-WGI in Stage 2. To demonstrate the effectiveness of the procedure, health assessment of relatively large structural systems is presented. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. The optimum number and location of measured responses are also investigated. It is demonstrated that the method is capable of identifying defect-free and defective states of the structures using minimum information. Furthermore, it can locate defect spot within a defective element accurately. The comparative studies are also conducted between the proposed methods and available methods in the literature. First, it is between the UKF-WGI and extended Kalman filter with weighted global iteration (EKF-WGI) procedure. Then, it is between UKF-UI-WGI and generalized iterative least-squares extended Kalman filter with unknown input (GILS-EKF-UI) procedure, developed earlier by the research team. It is demonstrated that the proposed UKF-based procedures are superior to the EKF-based procedures for SHA.