The ability to construct accurate mathematical models of real systems is an important part of control systems design. A block oriented systems identification approach models the unknown system as interconnected linear and nonlinear blocks. The subject of this thesis is a particular configuration of these blocks referred to as a Wiener model. The Wiener model studied here is a cascade of a one input linear block followed by a nonlinear block which then provides one output. We assume that the signal between the linear and nonlinear block is always unknown, only the Wiener model input and output can be sampled. This thesis investigates identification of the linear transfer function in a Wiener model. The question examined throughout the thesis is: given some small amount of a priori information on the nonlinear part, what can we determine about the linear part? Examples of minimal a priori information are knowledge of only one point on the nonlinear transfer characteristic, or simply that the transfer characteristic is monotonic over a certain range. Nonlinear blocks with and without memory are discussed. Several algorithms for identifying the linear transfer function of a block oriented Wiener system are presented and analyzed in detail. Linear blocks identified have both finite and infinite impulse response (i.e. FIR and IIR). Each algorithm has a carefully defined set of minimal a priori information on the nonlinearity. Also, each approach has a minimally restrictive set of assumptions on the input excitation. The universal applicability of each algorithm is established by providing rigorous proofs of identifiability and in some cases convergence. Extensive simulation testing of each algorithm has been performed. Simulation techniques and results are discussed in detail.

For surface-water resource management studies, it is important to quantify all of the mechanisms that contribute to water quantity and influence water quality. In this regard, various methods have been used to ground-water fluxes in lake systems. These have included physical measurements (e.g., seepage meters), flow-net analyses, water budgets, chemical tracers, ground-water flow models, and statistical analyses. The method developed for this study for calculating ground-water inflow uses a simplified, 1-layer (surficial aquifer) ground-water flow model. The test area was on a set of lakes known as the Winter Haven Chain of Lakes in Polk County, Florida. The technique combines the use of a numerical model (MODFLOW) with an inverse prediction technique (PEST) to determine net surficial recharge rates. Within the model, the lakes were represented as constant-head boundaries. A general, surficial ground water no-flow boundary was delineated around the entire lake system based on the topographic boundaries. The model used annual average lake elevations to create a constant-head boundary for each lake for each year. Annual average elevations of surficial well heads were used as target well data. Model results generally support previous studies in the region, concluding that the lake chain receives significant inflow from the surficial aquifer and leaks to the Floridan aquifer. As a consequence, ground-water quality constituency was found to be of critical importance. One of the most important observations from this study is the need for accurate ground-water concentrations for ridge lake water quality management. The initial measured values used in this study were highly variable, uncertain, and likely underestimated the effect that ground water has on nutrient loading to the Winter Haven Chain of Lakes.

A novel application is presented for a fault-tolerant adaptive model predictive control system for small-scale helicopters. The use of a joint Extended Kalman Filter, (EKF), for the estimation of the states and parameters of the UAV, provided the advantage of implementation simplicity and accuracy. A linear model of a small-scale helicopter was utilized for testing the proposed control system. The results, obtained through the simulation of different fault scenarios, demonstrated that the proposed scheme was able to handle different types of actuator and system faults effectively. The types of faults considered were represented in the parameters of the mathematical representation of the helicopter. Benefits provided by the proposed fault-tolerant adaptive model predictive control systems include: The use of the joint Kalman filter provided a straightforward approach to detect and handle different types of actuator and system faults, which were represented as changes of the system and input matrices. The built-in adaptability provided for the handling of slow time-varying faults, which are difficult to detect using the standard residual approach. The successful inclusion of fault tolerance yielded a significant increase in the reliability of the UAV under study. A byproduct of this research is an original comparison between the EKF and the Unscented Kalman Filter, (UKF). This comparison attempted to settle the conflicting claims found in the research literature concerning the performance improvements provided by the UKF. The results of the comparison indicated that the performance of the filters depends on the approximation used for the nonlinear model of the system. Noise sensitivity was found to be higher for the UKF, than the EKF. An advantage of the UKF appears to be a slightly faster convergence.

<p>An intrinsic mathematical model for simulation of fixed bed chromatography was demonstrated and compared to more simplified models. The former model was shown to describe variations in the physical, kinetic, and operating parameters better than the latter ones. This resulted in a more reliable prediction of the chromatography process as well as a better understanding of the underlying mechanisms responsible for the separation. A procedure based on frontal liquid chromatography and a detailed mathematical model was developed to determine effective diffusion coefficients of proteins in chromatographic gels. The procedure was applied to lysozyme, bovine serum albumin, and immunoglobulin γ in Sepharose™ CL-4B. The effective diffusion coefficients were comparable to those determined by other methods.</p><p>Molecularly imprinted polymers (MIPs) are traditionally prepared as irregular particles by grinding monoliths. In this thesis, a suspension polymerization providing spherical MIP beads is presented. Droplets of pre-polymerization solution were formed in mineral oil with no need of stabilizers by vigorous stirring. The droplets were transformed into solid spherical beads by free-radical polymerization. The method is fast and the performance of the beads comparable to that of irregular particles. Optimizing a MIP formulation requires a large number of experiments since the possible combinations of the components are huge. To facilitate the optimization, chemometrics was applied. The amounts of monomer, cross-linker, and porogen were chosen as the factors in the model. Multivariate data analysis indicated the influence of the factors on the binding and an optimized MIP composition was identified. The combined use of the suspension polymerization method to produce spherical beads with the application of chemometrics was shown in this thesis to drastically reduce the number of experiments and the time needed to design and optimize a new MIP.</p>

<p>In several projects within ABB there is a need of state and parameter estimation for nonlinear dynamic systems. One example is a project investigating optimisation of gas turbine operation. In a gas turbine there are several parameters and states which are not measured, but are crucial for the performance. Such parameters are polytropic efficiencies in compressor and turbine stages, cooling mass flows, friction coefficients and temperatures. Different methods are being tested to solve this problem of system identification or parameter estimation. This thesis describes the implementation of such a method and compares it with previously implemented identification methods. The comparison is carried out in the context of parameter estimation in gas turbine models, a dynamic load model used in power systems as well as models of other dynamic systems. Both simulated and real plant measurements are used in the study.</p>

<p>This masters thesis concerns a modeling project performed at Volvo Technology in Gothenburg, Sweden. The main purpose of the project has been to develop a physical model of the climate in construction vehicles that later on can be used in the development of an electronic climate controller. The focus of the work has been on one type of wheel loader and one type of excavator. The temperature inside the compartment has been set equal to the notion climate. </p><p>With physical theories about air flow and heat transfer in respect, relations between the components in the climate unit and the compartment has been calculated. Parameters that has had unknown values has been estimated. The relations have then been implemented in the modeling tool Simulink. </p><p>The validation of the model has been carried out by comparison between measured data and modeled values by calculation of Root Mean Square and correlation. Varying the estimated parameters and identifying the change in the output signal, i.e the temperature of the compartment, have performed a sensitivity analysis. </p><p>The result of the validation has shown that the factor with the greatest influence on the temperature in the vehicle is the airflow through the climate unit and the outlets. Minor changes of airflow have resulted in major changes in temperature. The validation principally shows that the model gives a good estimation of the temperature in the compartment. The static values of the model differs from the values of the measured data but is regarded being as within an acceptable margin of error. The weakness of the model is mainly its predictions of the dynamics, which does not correlate satisfyingly with the data.</p>

In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field equations lead to parallel and iterative algorithms. One of the considered models for image reconstruction is shown to give in a natural way the graduate non-convexity algorithm proposed by Blake and Zisserman.

Natural attenuation refers to the observed reduction in contaminant concentration via natural processes as contaminants migrate from the source into environmental media. Assessment of the dimensions of contaminant plumes and prediction of their fate requires predictions of the rate of dissolution of contaminants from residual non-aqueous-phase liquids (NAPLs) into the aquifer and the rate of contaminant removal through biodegradation. The available techniques to estimate these parameters do not characterize their confidence intervals by accounting for their relationships to uncertainty in source geometry and hydraulic conductivity distribution. The central idea in this thesis is to develop a flexible modeling approach for characterization of uncertainty in residual NAPL dissolution rate and first-order biodegradation rate by tailoring the estimation of these parameters to distributions of uncertainty in source size and hydraulic conductivity field. The first development in this thesis is related to a distance function approach that characterizes the uncertainty in the areal limits of the source zones. Implementation of the approach for a given monitoring well arrangement results in a unique uncertainty band that meets the requirements of unbiasedness and fairness of the calibrated probabilities. The second development in this thesis is related to a probabilistic model for characterization of uncertainty in the 3D localized distribution of residual NAPL in a real site. A categorical variable is defined based on the available CPT-UVIF data, while secondary data based on soil texture and groundwater table elevation are also incorporated into the model. A cross-validation study shows the importance of incorporation of secondary data in improving the prediction of contaminated and uncontaminated locations. The third development in this thesis is related to the implementation of a Monte Carlo type inverse modeling to develop a screening model used to characterize the confidence intervals in the NAPL dissolution rate and first-order biodegradation rate. The development of the model is based on sequential self-calibration approach, distance-function approach and a gradient-based optimization. It is shown that tailoring the estimation of the transport parameters to joint realizations of source geometry and transmissivity field can effectively reduce the uncertainties in the predicted state variables.

The biodegradation kinetics of Geropon TC-42 (trademark) by an acclimated culture was investigated in anoxic batch reactors to determine biokinetic coefficients to be implemented in two biofilm mathematical models. Geropon TC-42 (trademark) is the surfactant commonly used in space habitation. The two biofilm models differ in that one assumes a constant biofilm density and the other allows biofilm density changes based on space occupancy theory. Extant kinetic analysis of a mixed microbial culture using Geropon TC-42 (trademark) as sole carbon source was used to determine cell yield, specific growth rate, and the half-saturation constant for S0/X0 ratios of 4, 12.5, and 34.5. To estimate cell yield, linear regression analysis was performed on data obtained from three sets of simultaneous batch experiments for three S0/X0 ratios. The regressions showed non-zero intercepts, suggesting that cell multiplication is not possible at low substrate concentrations. Non-linear least-squares analysis of the integrated equation was used to estimate the specific growth rate and the half-saturation constant. Net specific growth rate dependence on substrate concentration indicates a self-inhibitory effect of Geropon TC-42 (trademark). The flow rate and the ratio of the concentrations of surfactant to nitrate were the factors that most affected the simulations. Higher flow rates resulted in a shorter hydraulic retention time, shorter startup periods, and faster approach to a steady-state biofilm. At steady-state, higher flow resulted in lower surfactant removal. Higher influent surfactant/nitrate concentration ratios caused a longer startup period, supported more surfactant utilization, and biofilm growth. Both models correlate to the empirical data. A model assuming constant biofilm density is computationally simpler and easier to implement. Therefore, a suitable anoxic packed bed reactor for the removal of the surfactant Geropon TC-42 (trademark) can be designed by using the estimated kinetic values and a model assuming constant biofilm density.

While the characteristic dimensions of electronic systems scale down to micro- or nano-world, their performance is greatly influenced. Micro-fabrication process or variations of the operating situation such as temperature, humidity or pressure are usual cause of dispersion. Therefore, it seems essential to co-integrate self-testing or self-adjustment routines for these microdevices. For this feature, most existing system parameter estimation methods are based on the implementation of high-resolution digital measurements of the system's output. Thus, long design time and large silicon areas are needed, which increases the cost of the micro-fabricated devices. The parameter estimation problems based on binary outputs can be introduced as alternative self-test identification methods, requiring only a 1-bit Analog-to-Digital Converter (ADC) and a 1-bit Digital-to-Analog Converter (DAC).In this thesis, we propose a novel recursive identification method to the problem of system parameter estimation from binary observations. An online identification algorithm with low-storage requirements and small computational complexity is derived. We prove the asymptotic convergence of this method under some assumptions. We show by Monte Carlo simulations that these assumptions do not necessarily have to be met in practice in order to obtain an appropriate performance of the method. Furthermore, we present the first experimental application of this method dedicated to the self-test of integrated micro-electro-mechanical systems (MEMS). The proposed online Built-In Self-Test method is very amenable to integration for the self-testing of systems relying on resistive sensors and actuators, because it requires low memory storage, only a 1-bit ADC and a 1-bit DAC which can be easily implemented in a small silicon area with minimal energy consumption.