Approved for public release; distribution is unlimited / The development of a dynamic computational model capable of predicting, with the requisite design certainty, the transient thermal response of jet vane thrust control systems has been undertaken. The modeling and simulation procedures utilized are based on the concept that the thermal processes associated with jet vane operation can be put into a transfer function form commonly found in the discipline of automatic controls. Well established system identification methods are employed to formulate and verify the relationships between the various gains and frequencies of the transfer function model and experimental data provided by Naval Weapons Center, China Lake. / http://archive.org/details/modelingofjetvan00hatz / Lieutenant, United States Navy

The plasma vertical position system on the COMPASS–D tokamak is studied in this thesis. An analogue P+D controller is used to regulate the plasma vertical position which is open loop unstable. Measurements from inside the vessel are used for the derivative component of the control signal and external measurements for the proportional component. Two main sources of disturbances are observed on COMPASS–D. One source is 600Hz noise from thyristor power supplies which cause large oscillations at the control amplifier output. Another source is impulse–like disturbances due to ELMs (Edge Localized Modes) and this can occasionally lead to loss of control when the control amplifier saturates. Models of the plasma open loop dynamics were obtained using the process of system identification. Experimental data is used to fit the coefficients of a mathematical model. The frequency response of the model is strongly dependent on the shape of the plasma. The effect of shielding by the vessel wall on external measurements when compared with internal measurements is also observed. The models were used to predict values of gain margins and phase crossover frequencies which were found to be in good agreement with measured values. The harsh reactor conditions on the proposed ITER tokamak preclude the use of internal measurements. On COMPASS–D the stability margins of the loop decrease when using only external flux loops. High order controllers were designed to stabilize the system using only external measurements and to reduce the effect of 600Hz noise on the control amplifier voltage. The controllers were tested on COMPASS–D and demonstrated the improved performance of high order controllers over the simple P+D controller. ELMs cause impulse–like disturbances on the plasma position. The optimal controller minimizing the peak of the impulse response can be calculated analytically for COMPASS–D. A multiobjective controller which combines a small peak impulse response with robust stability and noise attenuation can be obtained using a numerical search.

Heat and electricity production along with waste management are two modern day challenges for society. One of the possible solution to both of them is the incineration of household waste to produce heat and electricity. Incineration is a waste-to-energy treatment process, which can reduce the need for landfills and save the use of more valuable fuels, thereby conserving natural resources. This report/paper investigates the performance and emissions of a municipal solid waste (MSW) fueled industrial boiler by performing a system identification analysis using Principle Component Analysis (PCA) and Partial Least Squares Regression (PLS-R) modeling. The boiler is located in Västerås, Sweden and has a maximum capacity of 167MW. It produces heat and electricity for the city of Västerås and is operated by Mälarenergi AB. A dataset containing 148 different boilers variables, measured with a one hour interval over 2 years, was used for the system identification analysis. The dataset was visually inspected to remove obvious outliers before beginning the analysis using a multivariate data analysis software called The Unscrambler X (Version 10.3, CAMO Software, Norway). Correlations found using PCA was taken in account during the PLSR modelling where models were created for one response each. Some variables had an unexpected impact on the models while others were fully logical regarding combustion theory. Results found during the system analysis process are regarded as reliable. Any errors may be due to outlier data points and model inadequacies.

Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.

The need for mechanisms that assist human movements has been increasing due to the rising number of people that has some kind of movement disability. In this scenario, it is of great importance the development of control methods that assist the interface between a motor assistive device and its user. This work proposes a controller for an exoskeleton with one degree of freedom, using surface electromyography signals from the user as the input signal. An exoskeleton was adapted to serve as platform for the developed control method. To create an EMG-to-Angle model, a set of experiments were carried out with six subjects. The experiment consisted of a series of continuous and discrete elbow flexion and extension movements with different load levels. Using the experimental data, linear (ARIMAX) and non linear (Hammerstein-Wiener) system identification methods were evaluated to determine which is the best candidate for the estimation of the EMG-to-Angle model, based on its accuracy and ease of implementation. A new experiment wasconducted to develop a real-time controller, based on FIR model and tested in a real-timeapplication. Tests showed that the controller is capable of estimating the elbow joint angle with correlation above 70% and root-mean-square error below 25° when compared to the measured elbow joint angles. / A necessidade por mecanismos que auxiliam os movimentos do ser humano vem crescendo devido ao aumento do número de pessoas portadores de deficiências que afetam a capacidade motora. Nesse cenário, é de grande importância o desenvolvimento de métodos de controle que auxiliem a interface entre o dispositivo de assistência motora e o seu usuário. Esse trabalho propõe um controlador para um exoesqueleto com um grau de liberdade, usando sinais de eletromiografia de superfície do usuário como sinal de entrada. Um exoesqueleto foi adaptado para servir de plataforma para o método de controle desenvolvido. Para criar um modelo EMG-ângulo, um conjunto de experimentos foi conduzido com seis voluntários. O experimento consistiu em uma série de movimentos de flexo-extensão do cotovelo contínuos e discretos com diferentes níveis de carga. Utilizando os dados do experimento, métodos de identificação de sistemas linear (ARIMAX) e não linear (Hammerstein-Wiener) foram avaliados para determinar qual o melhor candidato para a estimação do modelo EMG-ângulo, baseado em sua acurácia e facilidade de implementação. Um novo experimento foi conduzido para desenvolver um controlador em tempo real, baseado no modelo FIR e testado em uma aplicação em tempo real. Testes indicaram que o controlador é capaz de estimar o ângulo da junta do cotovelo com valores de correlação acima de 70% e raiz do erro quadrático médio menor que 25º, quando comparados aos valores medidos de ângulo da junta do cotovelo.

Compared to conventional machine tools, industrial robots offer great advantages such as multitasking, larger workspace, and lower price. However, these advantages of robots are undermined by their high structural flexibility leading to excessive deflections, severe vibrations, and ultimately violating dimensional tolerances and poor surface finish. Modeling the dynamics of robots under machining (e.g. milling and drilling) forces is essential for reducing deflections and vibrations during the process. Although modeling the dynamics of traditional machining systems is a well-studied subject, the existing modeling approaches are not applicable to robotic manipulators because of the posture-dependent dynamics of industrial robots. Within this context, the presented thesis aims to predict the stability of vibrations during robotic machining operations through prediction of posture dependent dynamic behavior of robots. A rigid-body modeling approach is used to identify the dynamic parameters of the robotic manipulator based on least squares estimation method. Next, by adopting a rigid link flexible joint model and employing experimental modal analysis to identify the joint stiffness and damping parameters, posture dependent dynamic response prediction of the robot is achieved. Finally, the posture-dependent milling stability is presented as a function of the predicted tool center point transfer function, spindle speed, and axial depth of cut. A Staubli TX200 robot and a Kuka KR90 robot are used as experimental case studies. / Graduate

The development of Advanced Driving Assistance Systems (ADAS) produces comfort and safety through the application of several control theories. One of these systems is the Adaptive Cruise Control (ACC). In this work, a distribution of two control loops of such system is developed for an embedded application to a vehicle. The vehicle model was estimated using the system identification theory. An outer loop control manages the radar data to compute a suitable cruise speed, and an inner loop control aims for the vehicle to reach the cruise speed given a desired performance. For the inner loop, it is used two different approaches of model predictive control: a finite horizon prediction control, known as MPC, and an infinite horizon prediction control, known as IHMPC. Both controllers were embedded in a microcontroller able to communicate directly with the electronic unit of the vehicle. This work validates its controllers using simulations with varying systems and practical experiments with the aid of a dynamometer. Both predictive controllers had a satisfactory performance, providing safety to the passengers. / A inclusão de sistemas avançados para assistência de direção (ADAS) tem beneficiado o conforto e segurança através da aplicação de diversas teorias de controle. Um destes sistemas é o Sistema de Controle de Cruzeiro Adaptativo. Neste trabalho, é usado uma distribuição de duas malhas de controle para uma implementação embarcada em um carro de um Controle de Cruzeiro Adaptativo. O modelo do veículo foi estimado usando a teoria de identificação de sistemas. O controle da malha externa utiliza dados de um radar para calcular uma velocidade de cruzeiro apropriada, enquanto o controle da malha interna busca o acionamento do veículo para atingir a velocidade de cruzeiro com um desempenho desejado. Para a malha interna, é utilizado duas abordagens do controle preditivo baseado em modelo: um controle com horizonte de predição finito, e um controle com horizonte de predição infinito, conhecido como IHMPC. Ambos controladores foram embarcados em um microcontrolador capaz de comunicar diretamente com a unidade eletrônica do veículo. Este trabalho valida estes controladores através de simulações com sistemas variantes e experimentos práticos com o auxílio de um dinamômetro. Ambos controladores preditivos apresentaram desempenho satisfatório, fornecendo segurança para os passageiros.

O trabalho tem por objetivo avaliar diferentes métodos para identificar zeros na matriz de transferência de sistemas MIMO e propor um método novo baseado em análise de correlação. Estes métodos são utilizados durante a etapa de pré-identificação, a fim de se obter informações relevantes que possam ser utilizadas para se reduzir o tempo dos experimentos, diminuir a variabilidade dos parâmetros dos modelos e melhorar a eficácia dos modelos remanescentes. Estes métodos são aplicados a sistemas MIMO lineares, com dados coletados em malha aberta e em malha fechada. É avaliado o ganho obtido em relação à capacidade de predição dos modelos, a redução do tempo de identificação e o ganho de desempenho do controlador MPC que utiliza estes modelos. O trabalho conclui que a informação de zeros resulta em melhorias no tempo de identificação e no desempenho do controlador MPC. / This work aims to evaluate different methods to identify zeros in the transfer matrix of MIMO systems and to propose a new method based on correlation analysis. These methods are used during the pre-identification stage in order to identify relevant information that can be used to reduce the duration of the experiment, decrease model parameter variability and improve the accuracy of the remaining models. These methods are applied to MIMO linear systems, with data collected in open and closed-loop. The gains obtained in relation to the predictive ability of the models, the reduction of identification time and the performance gain of the MPC using these models are evaluated. This work concludes that the zero information results in improvements in identification time and in performance gain of the MPC controller.

Modelos preditivos de mercado são ferramentas importantes para tomadores de decisões no âmbito público e privado. Devido à complexidade dinâmica do mercado imobiliário, composta pela interação de dois submercados distintos (mercado de ativos imobiliários e mercado de consumo de espaço) e pela limitação de dados disponíveis, o estudo analítico de mercados imobiliários requer a modelagem paramétrica de um sistema de equações que os descrevam, seguido pela identificação dos parâmetros deste sistema utilizando dados reais de uma região. Neste trabalho, estudamos o modelo dinâmico de mercado imobiliário proposto por Wheaton (1999), criado a partir do popular modelo de quatro quadrantes de autoria de DiPasquale e Wheaton (1996). Utilizamos técnicas de identificação de sistemas para elaborar um modelo de aprendizado para o estoque imobiliário, e o implementamos em Matlab. Aplicamos o método elaborado em dados simulados, para validá-lo, e então aplicamos o mesmo método, com adaptações, em dados reais do mercado imobiliário canadense. Os resultados obtidos validam o método de identificação de sistema dinâmico quando testado em dados simulados, e corroboram o modelo de Wheaton (1999) como modelo preditivo em dados reais. Ademais, os resultados indicam que um modelo que seja capaz de entender a evolução dinâmica dos parâmetros estáticos do modelo de Wheaton (1999), poderia melhorar os resultados deste como ferramenta preditiva. / Predictive market models are important tools for decision-makers in the public and private spheres. Due to the dynamic complexity of the real estate market, consisting of the interaction of two distinct submarkets (real estate asset market and space consumption market) and the lack of real estate data, the analytical study of real estate markets requires the parametric modeling of a system of equations describing them, followed by the identification of the parameters of this system using real data from a region. In this work, we study the dynamic real estate market model proposed by Wheaton (1999), created from the popular four-quadrant model of DiPasquale e Wheaton (1996). We use system identification techniques to develop a learning model for real estate inventory data, and implement it in Matlab. We apply the method devised in simulated data to validate it, and then apply the same method with adaptations in real data of the Canadian real estate market. The results validate the dynamic system identification method when tested in simulated data, and corroborate the Wheaton (1999) model as a predictive model in real data. In addition, the results indicate that a model that is able to understand the dynamic evolution of the static parameters of the Wheaton (1999) model, could improve its results as a predictive tool.

A Identificação de Sistemas é uma das técnicas utilizadas para se obter a representação matemática de um sistema. Diversos métodos podem ser aplicados para se obter um modelo matemático através da identificação de sistemas, entre eles o método de identificação assintótico, também chamado de ASYM (Zhu, 1998). Este trabalho propõe aplicar o método de identificação assintótico em sistemas SISO para a obtenção de modelo de sistemas ditos caixa-preta e avaliar o seu desempenho buscando também o melhor detalhamento do método. Os modelos obtidos foram avaliados de acordo com sua nota calculada através do método ASYM, através da comparação do índice de ajuste fit para autovalidação e validação cruzada e pela variância dos parâmetros dos modelos. O método ASYM é exaustivamente testado para sua avaliação. Entre os testes realizados neste trabalho destacam-se dois experimentos tipo Monte-Carlo com mais de quinhentas identificações e a aplicação do método em uma planta real. Os testes comprovaram a viabilidade da aplicação do método assintótico na identificação de sistemas SISO do tipo caixa-preta com excelente desempenho para estruturas ARMAX. / System Identification is one of the techniques used to obtain the mathematical representation of a system. Several methods can be applied to obtain a mathematical model by the system identification, including the asymptotic method, also called ASYM (Zhu, 1998). This work proposes to apply the ASYM method for SISO systems identification, then obtain models of black-box systems called \"black box\" and evaluate its performance and show details of the method. The models obtained were evaluated according to their grade calculated using the ASYM method, by comparing the fit adjustment index, self-validation and cross validation and the variance of model parameters. The asymptotic method has been extensively tested to be evaluated. Among the tests in this work, two stand out such Monte Carlo experiments with more than five hundred identifications and a real plant identification. The tests proved the feasibility of applying the asymptotic method in the \"black box\" SISO systems identification with excellent performance for ARMAX structures.