The problem of estimating latent or unobserved states of a dynamical system from observed data is studied in this thesis. Approximate filtering methods for discrete time series for a class of nonlinear systems are considered, which, in turn, require sampling from a partially specified discrete distribution. A new algorithm is proposed to sample from partially specified discrete distribution, where the specification is in terms of the first few moments of the distribution. This algorithm generates deterministic sigma points and corresponding probability weights, which match exactly a specified mean vector, a specified covariance matrix, the average of specified marginal skewness and the average of specified marginal kurtosis. Both the deterministic particles and the probability weights are given in closed form and no numerical optimization is required. This algorithm is then used in approximate Bayesian filtering for generation of particles and the associated probability weights which propagate higher order moment information about latent states. This method is extended to generate random sigma points (or particles) and corresponding probability weights that match the same moments. The algorithm is also shown to be useful in scenario generation for financial optimization. For a variety of important distributions, the proposed moment-matching algorithm for generating particles is shown to lead to approximation which is very close to maximum entropy approximation. In a separate, but related contribution to the field of nonlinear state estimation, a closed-form linear minimum variance filter is derived for the systems with stochastic parameter uncertainties. The expressions for eigenvalues of the perturbed filter are derived for comparison with eigenvalues of the unperturbed Kalman filter. Moment-matching approximation is proposed for the nonlinear systems with multiplicative stochastic noise.

The estimation of the gas concentration (process-state) associated with a stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gaseous source into the ambient atmosphere is representative of an accidental or deliberate release of chemicals, or a release of gases from biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework presented couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the advection-diffusion equation with variable eddy diffusivities and ambient winds. Cases are considered for a 2D and 3D domain. The state estimator is a modified Luenberger observer with a €�collocated€� filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2D and 3D adaptive, multigrid, multi-step finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation in order to improve accuracy and efficiency. The motion dynamics of the SAV are incorporated into the spatial process and the SAV€™s guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid-refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid-refinement. Extensive numerical experiments serve to demonstrate the effectiveness of the coupled approach.

As the largest unexplored area on earth, the underwater world has unlimited at traction to marine scientists. Due to the complexity of the underwater environment and the limitations of human divers, underwater exploration has been facilitated by the use of submarines, Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). In recent years, use of autonomous control systems being integrated with visual sensors has increased substantially, especially in marine applications involving guidance of AUVs. In this work, autonomous fish-tracking via AUV with vision servoing control system is studied with the purpose of assisting marine biologists in gathering detailed information about the behaviors, habits, mobility, and local and global distributions of particular fish species. The main goal of this work in this thesis is to develop an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass. A key in enabling fish-tracking involves the development of a vision-processing algorithm to measure the position of the vehicle relative to the fish. It is challenging because of the complex nature of the underwater environment including dynamic and varied lighting conditions, turbulent water, suspended organic particles and various underwater plants and animals, and the deformable body of fish while swimming. These issues cause target fish identification by computer vision processing extremely difficult. In automated fish-tracking work, we provide two valid and efficient segmentation and recognition vision algorithms to identify a fish from the natural underwater environment: one is a feature extraction algorithm based on Gabor filter texture segmentation and a new approach that we call projection curve recognition. It is able to extract the feature on the fish tail and body and successfully describe the fish as two straight line segments. The second algorithm is SIFT based fish recognition algorithm. The SIFT approach introduced by David Lowe in 1999 extracts distinctive invariant features to scaling, illumination, rotation or translation of the image. The reliable keypoints matching in the database of keypoints from target fish is implemented by Best-Bin-First (BBF) algorithm. Clustering keypoints that agree on the possible object with Hough transform are identified as the object fish, reliable recognition is possible with as few as 3 features. Finally, a dynamic recognition process was designed using continuously updated fish model to match and recognize the target fish from a series of video frames. The SIFT Based recognition algorithm is effective and efficient in identifying Large Mouth Bass in a natural cluttering underwater environment. For a monocular camera system, the depth of field is extremely hard to obtain by vision processing. Hence, the system is augmented with a forward-looking digital image micro sonar. With the sonar image processing algorithm, the target fish is recognized. Sonar can not only provide the relative range between the fish and AUV, but also assist in identifying the target. Finally, the relative position and orientation of the fish in the image plane is estimated using an image processing method, transforming the coordinates between camera, sonar and AUV, and applying the estimation algorithm. The results of o off-line data processing taken from a natural Lake environment shows these computer vision algorithms for identifying fish and state estimation are efficient and successful. The proposed system has potential to enable a vision servo control system of AUV to reliably track a target fish in natural underwater environment.

As the largest unexplored area on earth, the underwater world has unlimited at traction to marine scientists. Due to the complexity of the underwater environment and the limitations of human divers, underwater exploration has been facilitated by the use of submarines, Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). In recent years, use of autonomous control systems being integrated with visual sensors has increased substantially, especially in marine applications involving guidance of AUVs. In this work, autonomous fish-tracking via AUV with vision servoing control system is studied with the purpose of assisting marine biologists in gathering detailed information about the behaviors, habits, mobility, and local and global distributions of particular fish species. The main goal of this work in this thesis is to develop an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass. A key in enabling fish-tracking involves the development of a vision-processing algorithm to measure the position of the vehicle relative to the fish. It is challenging because of the complex nature of the underwater environment including dynamic and varied lighting conditions, turbulent water, suspended organic particles and various underwater plants and animals, and the deformable body of fish while swimming. These issues cause target fish identification by computer vision processing extremely difficult. In automated fish-tracking work, we provide two valid and efficient segmentation and recognition vision algorithms to identify a fish from the natural underwater environment: one is a feature extraction algorithm based on Gabor filter texture segmentation and a new approach that we call projection curve recognition. It is able to extract the feature on the fish tail and body and successfully describe the fish as two straight line segments. The second algorithm is SIFT based fish recognition algorithm. The SIFT approach introduced by David Lowe in 1999 extracts distinctive invariant features to scaling, illumination, rotation or translation of the image. The reliable keypoints matching in the database of keypoints from target fish is implemented by Best-Bin-First (BBF) algorithm. Clustering keypoints that agree on the possible object with Hough transform are identified as the object fish, reliable recognition is possible with as few as 3 features. Finally, a dynamic recognition process was designed using continuously updated fish model to match and recognize the target fish from a series of video frames. The SIFT Based recognition algorithm is effective and efficient in identifying Large Mouth Bass in a natural cluttering underwater environment. For a monocular camera system, the depth of field is extremely hard to obtain by vision processing. Hence, the system is augmented with a forward-looking digital image micro sonar. With the sonar image processing algorithm, the target fish is recognized. Sonar can not only provide the relative range between the fish and AUV, but also assist in identifying the target. Finally, the relative position and orientation of the fish in the image plane is estimated using an image processing method, transforming the coordinates between camera, sonar and AUV, and applying the estimation algorithm. The results of o off-line data processing taken from a natural Lake environment shows these computer vision algorithms for identifying fish and state estimation are efficient and successful. The proposed system has potential to enable a vision servo control system of AUV to reliably track a target fish in natural underwater environment.

Power system operation requires accurate monitoring of electrical quantities and a reliable database of the power system. As the power system operation becomes more competitive, the secure operation becomes highly important and the role of state estimation becomes more critical. Recently, due to the development of new technology in high power electronics, new control and monitoring devices are becoming more popular in power systems. It is therefore necessary to investigate their models and integrate them into the existing state estimation applications. This dissertation is dedicated to exploiting the newly appeared controlling and monitoring devices, such as Flexible AC Transmission System (FACTS) devices and (Phasor Measurement Units) PMUs, and developing new algorithms to include them into power system analysis applications. Another goal is to develop a 3D visualization tool to help power system operators gain an in-depth image of the system operation state and to identify limit violations in a quick and intuitive manner. An algorithm of state estimation of a power system with embedded FACTS devices is developed first. This estimator can be used to estimate the system state quantities and Unified Power Flow Controller (UPFC) controller parameters. Furthermore, it can also to be used to determine the required controller setting to maintain a desired power flow through a given line. In the second part of this dissertation, two methods to determine the optimal locations of PMUs are derived. One is numerical and the other one is topological. The numerical method is more effective when there are very few existing measurements while the topology-based method is more applicable for a system, which has lots of measurements forming several observable islands. To guard against unexpected failures of PMUs, the numerical method is extended to account for single PMU loss. In the last part of this dissertation, a 3D graphic user interface for power system analysis is developed. It supports two basic application functions, power flow analysis and state estimation. Different visualization techniques are used to represent different kinds of system information.

For robotic deployments in planetary worksite environments, map construction and navigation are essential for tasks such as base construction, scientific investigation, and in-situ resource utilization. However, operation in a planetary environment imposes sensing restrictions, as well as challenges due to the terrain. In this thesis, we develop enabling technologies for autonomous mapping and navigation by employing a panning laser rangefinder as our primary sensor on a rover platform. The mapping task is addressed as a three-dimensional Simultaneous Localization and Mapping (3D SLAM) problem. During operation, long-range 360 degree scans are obtained at infrequent stops. These scans are aligned using a combination of sparse features and odometry measurements in a batch alignment framework, resulting in accurate maps of planetary worksite terrain. For navigation, the panning laser rangefinder is configured to perform short, continuous sweeps while the rover is in motion. An appearance-based approach is taken, where laser intensity images are used to compute Visual Odometry (VO) estimates. We overcome the motion distortion issues by formulating the estimation problem in continuous time. This is facilitated by the introduction of Gaussian Process Gauss-Newton (GPGN), a novel algorithm for nonparametric, continuous-time, nonlinear, batch state estimation. Extensive experimental validation is provided for both mapping and navigation components using data gathered at multiple planetary analogue test sites.

For robotic deployments in planetary worksite environments, map construction and navigation are essential for tasks such as base construction, scientific investigation, and in-situ resource utilization. However, operation in a planetary environment imposes sensing restrictions, as well as challenges due to the terrain. In this thesis, we develop enabling technologies for autonomous mapping and navigation by employing a panning laser rangefinder as our primary sensor on a rover platform. The mapping task is addressed as a three-dimensional Simultaneous Localization and Mapping (3D SLAM) problem. During operation, long-range 360 degree scans are obtained at infrequent stops. These scans are aligned using a combination of sparse features and odometry measurements in a batch alignment framework, resulting in accurate maps of planetary worksite terrain. For navigation, the panning laser rangefinder is configured to perform short, continuous sweeps while the rover is in motion. An appearance-based approach is taken, where laser intensity images are used to compute Visual Odometry (VO) estimates. We overcome the motion distortion issues by formulating the estimation problem in continuous time. This is facilitated by the introduction of Gaussian Process Gauss-Newton (GPGN), a novel algorithm for nonparametric, continuous-time, nonlinear, batch state estimation. Extensive experimental validation is provided for both mapping and navigation components using data gathered at multiple planetary analogue test sites.

The problem of parameter and state estimation of a class of nonlinear systems is addressed. An adaptive identifier and observer are used to estimate the parameters and the state variables simultaneously. The proposed method is derived using a new formulation. Uncertainty sets are defined for the parameters and a set of auxiliary variables for the state variables. An algorithm is developed to update these sets using the available information. The algorithm proposed guarantees the convergence of parameters and the state variables to their true value. In addition to its application in difficult estimation problems, the algorithm has also been adapted to handle fault detection problems. The technique of estimation is applied to two broad classes of systems. The first involves a class of continuous time nonlinear systems subject to bounded unknown exogenous disturbance with constant parameters. Using the proposed set-based adaptive estimation, the parameters are updated only when an improvement in the precision of the parameter estimates can be guaranteed. The formulation provides robustness to parameter estimation error and bounded disturbance. The parameter uncertainty set and the uncertainty associated with an auxiliary variable is updated such that the set is guaranteed to contain the unknown true values. The second class of system considered is a class of nonlinear systems with timevarying parameters. Using a generalization of the set-based adaptive estimation technique proposed, the estimates of the parameters and state are updated to guarantee convergence to a neighborhood of their true value. The algorithm proposed can also be extended to detect the fault in the system, injected by drastic change in the time-varying parameter values. To study the practical applicability of the developed method, the estimation of state variables and time-varying parameters of salt in a stirred tank process has been performed. The results of the experimental application demonstrate the ability of the proposed techniques to estimate the state variables and time-varying parameters of an uncertain practical system. / Thesis (Master, Chemical Engineering) -- Queen's University, 2011-10-31 22:04:58.762

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