An optimal estimation strategy is developed for post-experiment estimation of discretely measured dynamic systems which accounts for system model errors in a much more rigorous manner than Kalman filter-smoother type methods. The Kalman filter-smoother type methods, which currently dominate post-experiment estimation practice, treat model errors via “process noise", which essentially shifts emphasis away from the model and onto the measurements. The usefulness of this approach is subject to the measurement frequency and accuracy. The current method treats model errors by use of an estimation strategy based on concepts from optimal control theory. Unknown model error terms are explicitly included in the formulation of the problem and estimated as a part of the solution. In this manner, the estimate is improved; the model is improved; and an estimate of the model error is obtained. Implementation of the current method is straightforward, and the resulting state trajectories do not contain jump discontinuities as do the Kalman filter-smoother type estimates. Results from a number of simple examples, plus some examples from spacecraft attitude estimation, are included. The current method is shown to obtain significantly more accurate estimates than the Kalman filter-smoother type methods in many of the examples. The difference in accuracy is accentuated when the assumed model is relatively poor and when the measurements are relatively sparse in time and/or of low accuracy. Even for some well-modeled, densely measured applications, the current method is shown to be competitive with the Kalman filter-smoother type methods. / Ph. D. / incomplete_metadata

Active control of large flexible space structures is typically implemented to control only a few known elastic modes. Linear Quadratic Regulators (LQR) and Kalman-Bucy Filter (KBF) observers are usually designed to control the desired modes of vibration. Higher modes, referred to as residual modes, are generally ignored in the analysis and may be excited by the controller to cause a net destabilizing effect on the system. This is referred to as the spillover phenomenon. This dissertation considers the stabilization of the neglected dynamics of the higher modes of vibration. It aims at designing modal controllers with improved spillover stability properties. It is based on the premise that the structural dynamicist will be able to predict more vibration modes than would be practical to include in the design of the controller. The proposed method calls for designing the observer so as to improve spillover stability with minimum loss in performance. Two formulations are pursued. The first is based on optimizing the noise statistics used in the design of the Kalman-Bucy Filter. The second optimizes directly the gain matrix of the observer. The influence of the structure of the plant noise intensity matrix of the Kalman-Bucy Filter on the stability margin of the residual modes is demonstrated. An optimization procedure is presented which uses information on the residual modes to minimize spillover (i.e., maximize the stability margin) of known residual modes while preserving robustness vis-à-vis the unknown dynamics. This procedure selects either the optimum plant noise intensity matrix or the optimum observer gain matrix directly to maximize the stability margins of the residual modes and properly place the observer poles. The proposed method is demonstrated for both centralized and decentralized modal control. / Ph. D.

his study explores clamp force estimation using Unscented Kalman Filtering (UKF) in torque-controlled tightening scenarios with various velocity profiles. Previous research has explored the impact of velocity levels on target torque and clamping force, but only using hand-held tools. Prior research is extended by implementing UKF in a fixed setup, using the QST42, to remove user errors. Four strategies, Continuous Drive, TurboTight, Accelerating Tightening, and Paused Tightening, are analyzed using error and quality factor metrics. In Continuous Drive, both hand-held and fixed rigshave mean errors of approximately 4.09% and 4.14%, with quality factors of 88.38% and 97.72%.UKF adapts well in TurboTight, with mean errors of 3.50% (hand-held) and 5.23% (fixed rigs), and quality factors of 93.02% and 94.44%, respectively. Dynamic strategies like Accelerating Tightening- yield higher mean errors (10.33%) and quality factors (94.86%), while Paused Tightening results in a mean error of 5.17% and a quality factor of 76.86%. Tailoring UKF calibration is crucial for accuracy. Overall, this research underscores the close correlation between UKF’s performance and the dynamics of the tightening strategy. The implications extend to industrial applications, advocating for strategy-specific adjustments to enhance clamp force estimation accuracy. This study contributes to advancing UKF’s applicability in real-world scenarios, providing a foundational framework to enhance the accuracy and reliability of clamp force estimations. / Denna studie utforskar kraftuppskattning för klammer i momentkontrollerade åtdragnings-scenarier med olika hastighetsprofiler med hjälp av Unscented Kalman Filtering (UKF). Tidigare forskning har utforskat påverkan av hastighetsnivåer på målmoment och klämkraft, men endast med användning av handhållna verktyg. Tidigare forskning utökas genom att implementera UKF i en fast inställning, med QST42 verktyget, för att eliminera användarfel. Fyra strategier, Continuous Drive, TurboTight, Accelerating Tightening och Paused Tight-ening, analyseras med hjälp av fel- och kvalitetsfaktormetoder. I Continuous Drive har både handhållna och fixta åtdragningar medelvärdesfel på cirka 4,09% och 4,14%, med kvalitetsfaktorer på 88,38% och 97,72%. UKF anpassar sig väl i TurboTight, med medelvärdesfel på 3,50% (handhållna) och 5,23%(fixt rig) och kvalitetsfaktorer på 93,02% och 94,44%, respektive. Dynamiska strategier som Accelerating Tightening ger högre medelvärdesfel (10,33%) och kvalitetsfaktorer (94,86%), medan Paused Tightening resulterar i ett medelvärdesfel på 5,17% och en kvalitetsfaktor på 76,86%. Sammanfattningsvis understryker denna forskning den nära korrelationen mellan UKF:s prestanda och dynamiken i åtdragningsstrategin. Implikationerna sträcker sig till industriella tillämpningar och förespråkar strategispecifika justeringar för att förbättra noggrannheten i klämkraftsuppskattningen. Denna studie bidrar till att främja användningen av UKF i verkliga scenarier och tillhandahåller en grundläggande ram för att förbättra noggrannheten och tillförlitligheten i klämkraftsuppskattning.

Thesis (PhD (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006. / This thesis addresses the problem of bulding a video-based traffic monitoring system. We employ clustering, trackiing and three-dimensional reconstruction of moving objects over a long image sequence. We present an algorithms that robustly recovers the motion and reconstructs three-dimensional shapes from a sequence of video images, Magaia et al [91]. The problem ...

<p>The presence of vibration is an important problem in many engineering applications. Various passive techniques have traditionally been used in order to reduce waves and vibrations, and their harmful effects. Passive techniques are, however, difficult to apply in the low frequency region. In addition, the use of passive techniques often involve adding mass to the system, which is undesirable in many applications.</p><p>As an alternative, active techniques can be used to manipulate system dynamics and to control the propagation of waves and vibrations. This thesis deals with modeling, estimation and active control of systems that have resonant dynamics. The systems are exposed to stochastic disturbances. Some of them excite the system and generate vibrational responses and other corrupt measured signals. </p><p>Feedback control of a beam with attached piezoelectrical elements is studied. A detailed modeling approach is described and system identification techniques are employed for model order reduction. Disturbance attenuation of a non-measured variable shows to be difficult. This issue is further analyzed and the problems are shown to depend on fundamental design limitations.</p><p>Feedforward control of traveling waves is also considered. A device with properties analogous to those of an electrical diode is introduced. An `ideal´ feedforward controller based on the mechanical properties of the system is derived. It has, however, poor noise rejection properties and it therefore needs to be modified. A number of feedforward controllers that treat the measurement noise in a statistically sound way are derived.</p><p>Separation of overlapping traveling waves is another topic under investigation. This operation also is sensitive to measurement noise. The problem is thoroughly analyzed and Kalman filtering techniques are employed to derive wave estimators with high statistical performance. </p><p>Finally, a nonlinear regression problem with close connections to unbalance estimation of rotating machinery is treated. Different estimation techniques are derived and analyzed with respect to their statistical accuracy. The estimators are evaluated using the example of separator balancing. </p>

This research focuses on the pathogenic pollution of coastal recreational waters. Pollution of this resource can have serious social and economic implications. The health of the public could be compromised and there may be associated adverse impacts on the tourism industry. A section of coastline along the Durban Bight and including some of the nation's premier bathing beaches, was used for a case study. The water quality condition of the beaches was evaluated against both local and international marine recreational water quality standards. Most of Durban's bathing beaches were found to have good water quality. However beaches situated close to stormwater drains regularly experience poor water quality conditions. The relationships between beach water quality, the pollution sources and environmental factors such as rainfall were quantified. A weak correlation was found between rainfall and beach pathogenic pollution levels. No correlation was found between successive fortnightly beach samples indicating that the time scales of coastal dispersion processes are significantly shorter than the beach monitoring period. The research also indicates a need to update the SA marine water quality standards. The exclusive use of Escherichia coli (E.coli) as the indicator of faecal pollution is inconsistent with international trends towards the use of Enterococcus, which is a more robust pathogen indicator for marine environments. The main aim of the research was to develop a model to predict the water quality conditions of beaches. The Coastal Water Quality Model (CWQM) is intended to serve two functions: firstly to provide daily estimates of pathogenic pollution levels for beach management (e.g. closure under poor water quality conditions), and secondly to provide decision-makers with a tool for predicting the effects of changes on future water quality conditions. The CWQM was formulated as a stochastic state-space lumped advection diffusion model. A Kalman Filter was used for state estimation. Parameter estimation using the Extended Kalman filter was investigated but found to be unsatisfactory due to large input uncertainties and sparse measurements. An alternative statistical fitting procedure was therefore used for parameter estimation. The model was shown to produce accurate predictions of pathogenic pollution for the case study site. To further demonstrate it's utility. it was used to evaluate options for improving the poor water quality at Battery Beach. The results show that a constructed wetland could be effective in this case. / Thesis (M.Sc.)-University of Natal, Natal, 2003.

Mineral processing plan ts, such as LONMIN's Eastern Platinum B-stream, typically have few on-line measurements, and key measures of performance such as grade only become available after samples have been analysed in the laboratory. More immediate feedback from a dynamic observer model promises enhanced understanding of the process, and facilitates prompt corrective actions, whether in open or closed loop . Such plant s easily enter sub-optimal modes such as large , uselessly re-circulating loads as the feed conditions change. Interpretation of such modes from key combinations of the variables deduced by an observer model , using a type of expert system, would add another level of intelligence to benefit operation. The aim of this thesis was to develop and implement a dynamic observer model of the LONMIN Eastern Platinum B-Stream into one of the existing control platforms available at the plant , known as PlantStar®, developed by MINTEK. The solution of the system of differential and algebraic equations resulting from this type of flowsheet modelling is based on an extended Kalman filter, which is able to dynamically reconcile any measurements which are presented to it, in real time. These measurement selections may also vary in real time, which provides flexibility of the model solution and the model 's uses. PlantStar passes the measurements that are available at the plant, to the dynamic observer model through a "plugin" module, which has been developed to incorporate the observer model and utilise the PlantStar control platform. In an on-line situation, the model will track the plant's behaviour and continuously update its position in real-time to ensure it follows the plant closely. This model would then be able to run simulations of the plant in parallel and could be used as a training facility for new operators, while in a real-time situation it could provide estimates of unmeasurable variables throughout the plant. An example of some of these variables are the flotation rate constants of minerals throughout the plant, which can be estimated in real time by the extended Kalman filter. The model could also be used to predict future plant conditions based on the current plant state , allowing for case scenarios to be performed without affecting the actual plant's performance. Once the dynamic observer model and "plugin" module were completed, case scenario simulations were performed using a measured data set from the plant as a starting point because real-time data were unavailable as the model was developed off-site . / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2004.

Este trabalho dedica-se ao projeto e desenvolvimento de um veículo aéreo não tripulado. Tais veículos podem ser utilizados em diversas aplicações como monitoramento, vigilância, transporte, resgate, entre outros. Dentre os diversos tipos de veículos aéreos, este trabalho irá focar no modelo do quadrirotor, composto por quatro hélices contra-rotoras que estabilizam e movimentam o veículo. Para alcançar o objetivo de controlar este tipo de veículo, várias propostas e metodologias podem ser aplicadas, todas buscando contemplar o controle de todas ou parte das variáveis de estado presentes nesta planta. Neste texto serão descritas: as equações cinemáticas e dinâmicas que regem este sistema; o projeto e composição mecânica da aeronave; definição de sensores e atuadores juntamente com seus métodos de utilização; implementação de controlador linear por alocação direta de polos e Regulador Linear Quadrático juntamente com observador de estados de ordem plena e filtro de Kalman, para recuperação de estados não mensurados e filtragem de ruídos. Serão apresentados resultados em simulações para cada método de controle selecionado visando optar pelo melhor controlador para a aplicação da aeronave. O método selecionado será implementado para controlar a aeronave com os sensores e atuadores selecionados. Esta implementação será realizada a partir da técnica HIL Hardware in The Loop juntamente com o software MATLAB/Simulink visando validar o controlador em conjunto com a planta real, bem como o modelo dinâmico construído. / This work is dedicated to the project and development of an unmanned aerial vehicle. Such vehicles can be employed in various applications such as monitoring, surveillance, transportation, rescue and others. Among the types of aerial vehicles, this work is focused on the quadrotor, composed by four counter-rotating propellers which stabilize and displace the vehicle. In order to fulfill the objective of controlling this vehicle, many methodologies and propositions can be applied, seeking the control of all or a snippet of the state variables present in the system. There will be described in this work: the cinematic and dynamic equations that govern this system; the mechanical project and construction of the aircraft; sensors and actuators definition, along with its usage methods; linear control implementation of the pole placement and Linear Quadratic Regulator techniques along full order state observer and Kalman filtering in order to recover and filter non-measured states. Performance results in simulations will be presented on each control implementation to validate the best controller for the application and this implementation will be applied on the projected aircraft using the sensors and actuators selected. This implementation will be given through the HIL - Hardware in the Loop method using MATLAB/Simulink software to validate the control technique applied and the constructed dynamic model.

The demand for sophisticated wireless applications capable of conveying information content represented in various forms such as voice, data, audio and video is ever increasing. In order to support such applications, either additional wireless spectrum is needed or advanced signal processing techniques must be employed by the next-generation wireless communication systems. An immediate observation that can be made regarding the first option is that radio frequency spectrum is a limited natural resource. Moreover, since existing spectrum allocation policies of several national regulatory agencies such as the Federal Communications Commission (FCC) restrict spectrum access to licensed entities only, it has been identified that most of the licensed spectrum across time and frequency is inefficiently utilized. To facilitate greater spectral efficiency, many national regulatory agencies are considering a paradigm shift towards spectrum allocation by allowing unlicensed users to temporarily borrow unused spectral resources. This concept is referred to a dynamic spectrum access (DSA). Although, several spectrum measurement campaigns have been reported in the published literature for quantitatively assessing the available vacant spectrum, there are certain aspects of spectrum utilization that need a deeper understanding. First, we examine two complementary approaches to the problem of characterizing the usage of licensed bands. In the first approach, a linear mixed-effects based regression model is proposed, where the variations in percentage spectrum occupancy and activity period of the licensed user are described as a function of certain independent regressor variables. The second approach is based on the creation of a geo-location database consisting of the licensed transmitters in a specific geographical region and identifying the coverage areas that affect the available secondary channels. Both of these approaches are based on the energy spectral density data-samples collected across numerous frequency bands in several locations in the United States. We then study the mutual interference effects in a coexistence scenario consisting of licensed and unclicensed users. We numerically evaluate the impact of interference as a function of certain receiver characteristics. Specifically, we consider the unlicensed user to utilize OFDM or NOFDM symbols since the appropriate subcarriers can be turned off to facilitate non- contiguous spectrum utilization. Finally, it has been demonstrated that multiple-input and multiple-output (MIMO) antennas yield significant throughput while requiring no increase in transmit power or required bandwidth. However, the separation of spectrally overlapping signals is a challenging task that involves the estimation of the channel. We provide results concerning channel and symbol estimation in the scenario described above. In particular, we focus on the MIMO-OFDM transmission scheme and derive capacity lower bounds due to imperfect channel estimation.

Target tracking is one of the typical applications of wireless sensor networks: a large number of spatially deployed sensor nodes collaboratively sense, process and estimate the target state (e.g., position, velocity and heading). This thesis aimed to develop the collaborative information processing techniques that jointly address information processing and networking for the distributive estimation of target state in the highly dynamic and resources constrained wireless sensor networks. Taking into account the interplay between information processing and networking, this thesis proposed a collaborative information processing framework. The framework integrates the information processing which is responsible for the representation, fusion and processing of data and information with networking which caters for the formation of network, the delivery of information and the management of wireless channels. Within the proposed collaborative information processing framework, this thesis developed a suite of target tracking algorithms on the basis of the recursive Bayesian estimation method. For tracking a single target in wireless sensor networks, this thesis developed the sequential extended Kalman filter (S-EKF), the sequential unscented Kalman filter (S-UKF) and the Particle filter (PF). A novel extended Kalman filter and Particle filter hybrid algorithm, named as EKPF was also developed. The simulation results showed that the EKPF outperformed other three algorithms in terms of tracking accuracy and robustness. Moreover, to help evaluate the performance of the developed tracking algorithms, the posterior Cramer-Rao lower bound (PCRLB) which is the theoretical lower bound on the mean square error of the target state estimation was also computed. To tackle the measurement origin uncertainty in practical target tracking in wireless sensor networks, this thesis designed a Particle filter and probability density association filter (PDAF) hybrid algorithm, named as PF-PDAF for tracking a single target under the dual assumptions of clutter and missed detections. The PF-PDAF combines the advantages of PDAF algorithm in effectively solving the data association problem with the merits of PF that can accommodate the general non-Gaussian, nonlinear state space model. The PCRLB under measurement origin uncertainty was also derived and computed. For multiple target tracking in wireless sensor networks, this thesis designed a Particle filter and joint probabilistic data association filter (JPDAF) hybrid algorithm, named as PFJPDAF. The PF-JPDAF algorithm extends the traditional JPDAF to solve the general nonlinear non-Gaussian multiple targets tracking problems in wireless sensor networks. In the highly energy and communication bandwidth constrained wireless sensor networks, a critical consideration is that the information processing needs to be distributive. By adopting the hierarchical network architecture to achieve dynamic sensor nodes clustering and utilizing the Gaussian mixture model (GMM) to propagate estimation results amongst sensor clusters, this thesis developed the distributive PF, the distributive EKPF, the distributive PF-PDAF and the distributive PF-JPDAF tracking algorithms. Moreover, this thesis proposed a composite objective function incorporating both the information utility and the energy consumption measures to facilitate the sensing nodes selection in the distributive tracking algorithms. This composite objective function enables the distributive tracking algorithms to achieve the desirable tracking accuracy while still maintaining the lower energy consumption. / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008