O objetivo desse trabalho foi investigar o modelo matemático para baterias de chumbo-ácido, usada em aplicações veiculares, mais adequado para a condição de descarga, que ocorre durante o teste de Cold Crank, e propor uma metodologia para identificar os parâmetros da bateria, a partir de ensaios experimentais. A simulação do teste de Cold Crank foi a motivação para o início da pesquisa. Dentre os diversos modelos pesquisados, foram selecionados aqueles que representam a dinâmica da bateria durante a descarga e que são baseados em circuitos elétricos. O modelo de Jackey foi escolhido, por possuir um circuito equivalente com adequada complexidade para o objetivo estudado. Após algumas simplificações e usando a 1ª Lei de Kirchhoff, definiu-se a equação da bateria, que calcula a tensão nos terminais para um dado valor de corrente de descarga constante. Adotaram-se ainda algumas leis de formação propostas por Jackey e uma forma alternativa para a descrição de R1. Alguns parâmetros da equação da bateria foram calculados usando a curva de tensão em aberto (OCV) em função do estado de carga (SOC), a equação da variação do estado de carga em função do tempo (SOC(t)) e o circuito simplificado para o instante inicial. Para os demais parâmetros, uma metodologia de resolução foi apresentada e implementada em ambiente MatLab®. Através da utilização de curvas de descarga experimentais e com o auxílio dos algoritmos de otimização genético e de busca local, os parâmetros desconhecidos foram estimados de forma a minimizar o erro entre os valores calculados e os valores experimentais. Por fim, foi apresentada a variação dos parâmetros em função da corrente de descarga. Com o uso das curvas que aproximam essa variação, alguns exemplos foram gerados para mostrar que os valores calculados continuam coerentes, tanto em forma quanto em escala, quando comparados com valores experimentais para outros níveis de corrente. Dessa forma, o objetivo do trabalho foi alcançado uma vez que a metodologia aplicada apresentou bons resultados mesmo com o número limitado de curvas de descarga experimentais. / The aim of this study was to investigate the most suitable lead-acid battery model, used in vehicular application, to the discharge condition which occurs during a Cold Crank test, and to propose a methodology to identify the battery parameters from experimental tests. The Cold Crank simulation was the motivation for this research. Among the various studied models, were selected those that describe the battery dynamic during a discharge process and that are based on electrical circuits. Jackey model was chosen because it has an equivalent circuit with suitable complexity to the aim. After some simplifications and using 1st Kirchhoffs Law, the battery equation was defined, which calculates the terminal voltage for a given constant discharge current. Also, it was adopted some laws proposed by Jackey and an alternative way to describe R1. Some parameters from battery equation were defined using the open circuit voltage (OCV) as function of state of charge (SOC), the equation of SOC variation as function of time and simplified circuit for the initial time. For the others parameters, a solving methodology was introduced and implemented in Matlab® environment. Usage of experimental discharge curves and with the help of genetic and local search algorithms, the unknown parameters were estimated in order to minimize the error between calculated and experimental values. Finally, it was presented the parameters variation as function of discharge current. With the use of curves that approximate this variation, some examples were generated to show that the calculated values remain consistent in both shape and range when compared to experimental values for others current levels. In this way, the aim was reached since methodology produced good results even with limited number of experimental discharge curves.

Monitoramento da integridade estrutural (Structural health monitoring - SHM) está relacionado à implementação de alguma estratégia para a detecção de dano em estruturas de engenharia. Este estudo geralmente envolve a observação do sistema no tempo, utilizando amostras periódicas de medições da resposta dinâmica, a partir de um grupo de sensores, a fim de verificar alterações nos parâmetros modais, que podem indicar a presença do dano. Entretanto, especialmente para estruturas treliçadas, este processo tornase difícil principalmente porque nem todos os deslocamentos ou rotações nodais modelados numericamente podem ser medidos experimentalmente. Desta forma, o presente estudo tem por objetivo tratar algumas das ainda correntes questões dos sistemas de monitoramento da integridade estrutural baseados em registros de vibração. Primeiramente aborda-se um tema que, apesar de recentemente ter se mostrado importante, ainda apresenta muito poucos estudos: a influência da variação dos efeitos ambientais, especialmente a temperatura, sobre as características dinâmicas de estruturas. Com o intuito de verificar tal influência em pontes metálicas, os resultados apresentados por Ni et al. (2005) são utilizados para a realização de estudos de correlação, através de uma comparação entre equações de regressão linear e um modelo, proposto no presente trabalho, em Redes Neurais Artificiais (RNA). A seguir são estudados procedimentos de identificação estocástica de sistemas, passo fundamental para o monitoramento da integridade estrutural. Realiza-se uma revisão bibliográfica nesta área abordando a evolução dos métodos que utilizam apenas dados de resposta para a identificação. Enfoque principal é dado nos métodos de identificação estocástica de subespaço (SSI), pois se mostram os mais práticos e robustos para a determinação dos parâmetros modais da estrutura.Finalmente, o método dos vetores de localização de dano (Damage locating vector method- DLV), introduzido por Bernal (2002), é extensivamente discutido. Esta é umatécnica eficaz quando operando com um número arbitrário de sensores, modos truncados e em cenários de dano múltiplo, mantendo as operações numéricas simples. Além disto, a influência do ruído na precisão do método dos vetores de localização de dano é avaliada. Com o intuito de verificar o comportamento do método DLV perante diferentes intensidades de dano e, principalmente, na presença de ruído de medição, um estudo paramétrico é conduzido. Distintas excitações, como também diferentes cenários de dano, são numericamente testadas em uma treliça Warren contínua considerando um limitado conjunto de sensores, através de cinco níveis de ruído. Além disto, é proposto um caminho alternativo para determinar os vetores de localização de dano no procedimento do método DLV. A idéia é oferecer uma opção alternativa para a solução do problema utilizando um método algébrico amplamente difundido. A formulação original via decomposição em valores singulares é subsituída pela solução mais trivial de um problema de valores próprios. Isto é possível graças à relação algébrica entre a decomposição em valores singulares de uma matriz e a solução do problema de autovalores desta matriz pré-multiplicada por sua transposta. Os resultados finais mostraram que o método DLV, considerando a soluça alternativa, foi capaz de corretamente localizar as barras danificadas, utilizando dados somente de resposta da estrutura, mesmo considerando pequenas intesidades de dano e moderados níveis de ruído. / Structural health monitoring (SHM) refers to the implementation of some strategy for damage detection in engineering structures. This study generally involves the observation of a system over time using periodically sampled dynamic response measurements from a set of sensors in order to verify changes in modal parameters, which may indicate damage or degradation. However, especially for truss structures this process sounds difficulty mainly because not all nodal displacements or rotations in the numerical model can be experimentally measured. In this context, the present thesis aims to address some still current issues of the vibration-based structural health monitoring systems. Firstly it is introduced a subject that, although has recently shown important, still presents very few studies: the environmental effects, mainly temperature, on the structural modal properties. Seeking to address this influence on steel bridges, the results presented by Ni et al. (2005) are used to conduct correlations studies, comparing linear equation regression with an artificial neural network model (ANN), proposed in the present thesis. Procedures for stochastic systems identification are studied next, which is a fundamental phase for the SHM systems. A literature review in this field addressing the evolution of the methods that just use response data for identification is carried out. Main focus is given in the stochastic subspace identification methods (SSI), because they have been known as the most practical and robust methods to determine the structure’s modal parameters. Finally, the damage locating vector (DLV) method, introduced by Bernal (2002), is extensively discussed. This is a useful approach because is effective when operating with an arbitrary number of sensors, a truncated modal basis and multiple damage scenarios, while keeping the calculation at a low level. In addition, the noise influence on the accuracy of the damage locating vector method is evaluated. In order to verify the DLV behavior in front of different damages intensities and, mainly, in presence of measurement noise, a parametric study had been carried out. Different excitations as well as damagescenarios are numerically tested in a continuous Warren truss structure with a set of limited measurement sensors through five noise levels. Besides this, it is proposed another way to determine the damage locating vectors in the DLV procedure. The idea is to offer an alternative option to solve the problem with a more widespread algebraic method. The original formulation via singular value decomposition (SVD) is replaced by a common solution of an eigenvector and eigenvalue problem. This is possible thanks to the algebraic relationship between the singular value decomposition of a matrix and the eigenproblem solution of this matrix pre-multiplied by its transpose. The final results show that the DLV method, adopting the alternative, was able to correct locate the damaged bars, using an output-only system identification procedure, even considering small intensities of damage and moderate noise levels.

O tópico de identificação de sistemas aparece em vários ramos da ciência, com especial importância ao campo de Controle Automático. Entretanto, os problemas encontrados na construção de uma representação precisa de um sistema, como a falta de informações prévias, e as diversas decisões de projeto que devem ser tomadas para a resolução de problemas de identificação de sistemas por meios mais tradicionais, podem ser solucionados através da análise empírica do sistema. Nesse sentido, os processos Gaussianos apresentam-se como uma alternativa viável para a modelagem não-paramétrica de sistemas, trazendo a vantagem da estimação da incerteza do modelo. Para verificar o potencial dos processos Gaussianos em problemas de identificação de sistemas, foi realizada a identificação do modelo longitudinal de um veículo de grande porte, tendo alcançado um desempenho satisfatório, mesmo quando se utilizou poucos dados de treinamento. A partir do modelo aprendido, foi projetado um controlador preditivo baseado em modelo para controlar a velocidade do veículo. O controlador levou em consideração a variância da predição do modelo GP (Gaussian Process - Processos Gaussianos) em consideração durante o processo de otimização do sinal de controle. O controlador proposto alcançou um baixo erro no seguimento da referência, mesmo em situações extremas, como estradas íngremes. Entretanto, em alguns tipos de problemas, o resultado só pode ser mensurado a partir da combinação de uma sequência de ações, ou sinais de controle, aplicados ao longo da execução do processo, como é o caso do problema de direção ecológica (eco-driving). Nesses casos, estratégias que otimizem sinais de controle instantâneos podem não ser viáveis, sendo necessária a utilização de estratégias em que toda a política de controle seja otimizada de uma vez. Além disso, a avaliação do custo, ou execução de todo um episódio do processo, pode ser dispendiosa, é desejável que uma solução seja encontrada com a menor quantidade de interações possíveis com o sistema real. Uma técnica apropriada para essa situação é a Otimização Bayesiana, um algoritmo de otimização caixa-preta bastante eficiente. Porém, um dos problemas dessa solução é a incapacidade de lidar com um grande número de dimensões. Sendo assim, nesse trabalho, foi proposto o Coordinate Descent Bayesian Optimisation, um algoritmo baseado na Otimização Bayesiana, que busca o ótimo em espaços de alta dmensionalidade de maneira mais eficiente pois otimiza cada dimensão individualmente, em um esquema de descida coordenada. / The system identification topic appears in various branches of science, with particular emphasis on Automatic Control field. However, problems encountered in building an accurate representation of a system, such as lack of prior information, and the various design decisions which have to be taken to deal with system identification problems by more traditional means, can be solved through the empirical analysis of the system. In this sense, the Gaussian processes are presented as a viable alternative for non-parametric modelling systems, bringing the advantage of estimating the uncertainty of the model. To investigate the potential of Gaussian processes of system identification problems, identifying the longitudinal model of a large vehicle was performed, achieving reasonable performance even when used little training data. From the obtained model, a Model Predictive Controller was designed to control the vehicle speed. The controller took into account the variance of the GP model prediction on the control signal optimization and achieved low reference tracking error, even on hard conditions, like steep roads. However, in some kinds of problems, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategies to optimise control policies for individual stages of the process might not be applicable, and instead the whole policy might have to be optimised at once. Also, the cost to evaluate the policy\'s performance might also be high, being desirable that a solution can be found with as few interactions with the real system as possible. One appropriate candidate is Bayesian Optimization, a very efficient black-box optimization algorithm. But one of the main problems of this solution is the inability of dealing with a large number of dimensions. For that reason, in this work it was proposed Coordinate Descent Bayesian Optimisation, an algorithm to search more efficiently over high-dimensional policy-parameter spaces with BO, by searching over each dimension individually, in a sequential coordinate descent-like scheme.

Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second step involves learning the precise mechanisms of how this regulation occurs. This step reveals the dynamics of the system. Applying this process to all species leads to the complete dynamical network. Systems biology is making considerable efforts to learn biological networks at low experimental costs. The main goal of this thesis is to develop advanced methods to build models for biological networks, taking the circadian system of Arabidopsis thaliana as a case study. A variety of network inference approaches have been proposed in the literature to study dynamic biological networks. However, many successful methods either require prior knowledge of the system or focus more on topology. This thesis presents novel methods that identify both network topology and dynamics, and do not depend on prior knowledge. Hence, the proposed methods are applicable to general biological networks. These methods are initially developed for linear systems, and, at the cost of higher computational complexity, can also be applied to nonlinear systems. Overall, we propose four methods with increasing computational complexity: one-to-one, combined group and element sparse Bayesian learning (GESBL), the kernel method and reversible jump Markov chain Monte Carlo method (RJMCMC). All methods are tested with challenging dynamical network simulations (including feedback, random networks, different levels of noise and number of samples), and realistic models of circadian system of Arabidopsis thaliana. These simulations show that, while the one-to-one method scales to the whole genome, the kernel method and RJMCMC method are superior for smaller networks. They are robust to tuning variables and able to provide stable performance. The simulations also imply the advantage of GESBL and RJMCMC over the state-of-the-art method. We envision that the estimated models can benefit a wide range of research. For example, they can locate biological compounds responsible for human disease through mathematical analysis and help predict the effectiveness of new treatments.

Applied System Identification for a Four Wheel Reaction Wheel Platform By Seth Franklyn Silva At the California Polytechnic State University, San Luis Obispo there is a four-wheel reaction wheel pyramidal simulator platform supported by an air-bearing. This simulator has the current capability to measure the wheel speeds and angular velocity of the platform, and with these measurements, the system identification process was used to obtain the mass properties of this simulator. A handling algorithm was developed to allow wireless data acquisition and command to the spacecraft simulator from a “ground” computer allowing the simulator to be free of induced torques due to wiring. The system identification algorithm using a least squares estimation scheme was tested on this simulator and compared to theoretical analysis. The resultant principle inertia about the z-axis from the experimental analysis was 3.5 percent off the theoretical, while the other inertias had an error of up to 187 percent. The error is explained as noise attributed to noise in the measurement, averaging inconsistencies, low bandwidth, and derivation of accelerations from measured data.

Driver distraction contributes to approximately 43% of motor-vehicle crashes and 27% of near-crashes. Rapidly developing in-vehicle technology and electronic devices place additional demands on drivers, which might lead to distraction and diminished capacity to perform driving tasks. This situation threatens safe driving. Technology that can detect and mitigate distraction by alerting drivers could play a central role in maintaining safety. Correctly identifying driver distraction in real time is a critical challenge in developing distraction mitigation systems, and this function has not been well developed. Moreover, the greatest benefit may be from real-time distraction detection in advance of dangerous breakdowns in driver performance. Based on driver performance, two types of distraction - visual and cognitive - are identified. These types of distraction have very different effects on visual behavior and driving performance; therefore, they require different algorithms for detection. Distraction detection algorithms typically rely on either eye measures or driver performance measures because the effect of distraction on the coordination of measures has not been established. Combining both eye glance and vehicle data could enhance the ability of algorithms to detect and differentiate visual and cognitive distraction. The goal of this research is to examine whether poor coordination between visual behavior and vehicle control can identify diminished attention to driving in advance of breakdowns in lane keeping. The primary hypothesis of this dissertation is that detection of changes in eye-steering relationship caused by distraction could provide a prospective indication of vehicle state changes. Three specific aims are pursued to test this hypothesis. The first aim examines the effect of distracting activity on eye and steering movements to assess the degree to which the correlation parameters are indicative of distraction. The second aim applies a control-theoretic system identification approach to the eye movement and steering data to distinguish between distracted and non-distracted conditions. The third aim examines whether changes of eye-steering coordination associated with distraction provide a prospective indication of breakdowns in driver performance, i.e., lane departures. Together, the three aims show how that a combination of visual and steering behavior, i.e., eye-steering model, can differentiate between non-distracted and distracted state. This model revealed sensitivity to distraction associated with off-road glances. The models derived for different drivers have similar structure and fit to data from other drivers reasonably well. In addition, the differences in model order and model coefficients indicate the variability in driving behavior: some people generate more complex behavior than others. As was expected, eye-steering correlation on straight roads is not as strong as observed on curvy roads. However, eye-steering correlation measured through correlation coefficient and time delay between two movements is sensitive to different types of distraction. Time delay mediates changes in lane position and the eye-steering system predicts breakdowns in lane keeping. This dissertation contributes to developing a distraction detection system that integrates visual and steering behavior. More broadly, these results suggest that integrating eye and steering data can be helpful in detecting and mitigating impairments beyond distraction, such as those associated with alcohol, fatigue, and aging.

This thesis centers around an electric arc furnace (EAF) at a steel mini-mill in Wilton, IA. First, the thesis replicates previous optimization attempts. Next, the modeling is greatly altered to produce a much improved steel-melting model. Then, a new optimal control system is created and used to reduce energy and fuel costs over the melting process. Finally, results are presented. This thesis shows that when the new optimal control is simulated, the system shows significant energy and fuel savings.

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.

<p>The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series. </p><p>Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.</p>

<p>When a helicopter is flying, the dynamics vary depending on, for example, speed and position. Hence, a time-invariant linear model cannot describe its properties under all flight conditions. It is therefore desirable to update the linear helicopter model continuously during the flight. In this thesis, two different recursive estimation methods are presented, LMS (Least Mean Square) and adaptation with a Kalman filter. The main purpose of the system estimation is to get a model which can be used for feedback control. In this report, the estimated model will be used to create a LQ controller with the task of keeping the output signal as close to the reference signal as possible.Simulations in this report show that adaptive feedback control can be used to control a helicopter's angular velocities and that the possibility to use an adaptive control algorithm in a real future helicopter is good.</p>