Many ecological populations can be interpreted as response surfaces; the spatial patterns of the population vary in response to changes in the spatial patterns of environmental explanatory variables. Collection of a probability sample from the population provides unbiased estimates of the population parameters using design based estimation. When information is available for the environmental explanatory variables, model based procedures are available that provide more precise estimates of population parameters in some cases. In practice, not all of these environmental explanatory variables will be known. When the spatial coordinates of the population units are available, a spatial model can be used as a surrogate for the unknown, spatially patterned explanatory variables. Design based and model based procedures will be compared for estimating parameters of the population of Acid Neutralizing Capacity (ANC) of lakes in the Adirondack Mountains in New York. Results from the analysis of this population will be used to elucidate some general principles for model based estimation of parameters of spatial populations. Results indicate that using model based estimates of population parameters provide more precise estimates than design based estimates in some cases. In addition, including spatial information as a surrogate for spatially patterned missing covariates improves the precision of the estimates in some cases, the degree to which depends upon the model chosen to represent the spatial pattern. When the probability sample is selected from the spatial population is a stratified sample, differences in stratum variances need to be accounted for when residual spatial covariance estimation is desired for the entire population. This can be accomplished by scaling residuals by their estimated stratum standard deviation functions, and calculating the residual covariance using these scaled residuals. Results here demonstrate that the form of scaling influences the estimated strength of the residual correlation and the estimated correlation range. / Graduation date: 1997

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

This thesis adresses the problem of estimating the parameters in a permanent magnet synchronous motor (PMSM). There is an uncertainty about the parameters, due to age and tolerances in the manufacturing process. Parameters such as the resistance and the current to torque factor Kt, changes with respect to temperature as well. The temperature in the motor varies in normal motor operation, due to variations in angular velocity and torques. Online estimation methods with the model reference adaptive systems technique (MRAS) and offline methods are presented. The estimation algorithms are validated in simulations with Matlab/Simulink and also evaluated with experimental data. Experiments were performed on a range of different motors, in realistic scenarios. Relevant factors such as the angular velocity of the rotor and the impact of the gravity force are investigated. The results show that it is possible to estimate the motor factor $K_t$, with an accuracy of two percentage from its reference value in normal industry conditions. The estimated value of the motor inductance is within 25 percentage of the calculated reference value. The resistance however is affected by the resistance in the cables from the motor to the measurement device. With the cable resistance included in the calculations, the estimate still often exceeds double the value of the reference value.

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 164-170).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2003. / Thesis advisor(s): Roberto Cristi, Fotis A. Papoulias. Includes bibliographical references (p. 155). Also available online.

Thesis (Ph. D.)--University of Toronto, 2004. / Adviser: Ruth Childs. Includes bibliographical references.

Univ., Diss.--Tübingen, 2002. / Includes bibliographical references and index.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 103-113) Also available in print.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 101-106) Also available in print.