Biorreatores, que são explorados desde a antiguidade, são sistemas capazes de realizar a fermentação de compostos orgânicos, continuam sendo amplamente utilizados atualmente devido à diversidade de aplicações. Esses sistemas podem operar em diferentes modos de fermentação, entretanto, os mais utilizados são: fermentação contínua, semicontínua e descontínua. Esse último, juntamente com o processo de digestão anaeróbia (ausência de oxigênio), permitem que uma determinada matéria orgânica seja degradada e transformada em biogás, um dos fatores chave para geração de energia limpa. Percebe-se, portanto, que o estudo de biorreatores em modo de operação descontínuo e em processo de digestão anaeróbia é fundamental para o desenvolvimento de pesquisas relacionadas à geração de energia renovável. Para facilitar o entendimento desse processo, alguns autores propuseram estudos baseados na identificação de parâmetros em modelos não-lineares descritivos, do tipo caixa-branca, que hoje são vastamente utilizados na modelagem de biorreatores. A grande limitação dessa abordagem é que o processo de identificação de sistemas utilizando esses modelos pode ser complexo e demorado, ou, ainda, os parâmetros dos sistemas representados podem não ser identificáveis, inviabilizando o procedimento. Tentando amenizar essas dificuldades, propomos neste trabalho a utilização de modelos polinomiais NAR e NARMA do tipo caixa-preta para a modelagem de biorreatores em modo de fermentação descontínua. Modelos caixa-preta representam sistemas reais por meio de sua saída, sem informação sobre os mecanismos internos desse sistema, simplificando a identificação. Frente a esse contexto, o objetivo deste estudo é investigar a predição e, por consequência, realizar o monitoramento da produção de metano utilizando os modelos caixa-preta propostos em sistemas de biorreatores em modo descontínuo e em processo de digestão anaeróbia. Realizamos estudos que abarcam a investigação de dados simulados e de dados reais. Num primeiro momento são propostos modelos polinomiais dos tipos NAR e NARMA. A partir desses modelos são estimados os parâmetros dos sistemas simulados, com e sem ruído na saída, baseados em condições iniciais propostas na literatura, que denominamos Grupo de Controle. Posteriormente realizamos as validações desses modelos. Em seguida, passamos à etapa de investigação do domínio de validade dos modelos caixa-preta propostos, realizando um estudo em que modificamos as condições iniciais do sistema que representa biorreatores em modo de fermentação descontínua. Por fim, utilizamos dados de um experimento real para realizar o processo de estimação de parâmetros e de validação dos modelos. Os resultados mostraram que os modelos polinomiais NAR e NARMA são bastante adequados para predição de metano em biorreatores em modo de fermentação descontínua em processo de digestão anaeróbia, tanto para os dados simulados quanto para os dados reais. / Bioreactors, which are explored since antiquity, are systems that are capable of performing the fermentation of organic compounds. Nowadays, they are widely applied due to its diversity of applications. These systems can operate in different fermentation modes: continuous, fed-batch and batch. This last fermentation method along with the process of anaerobic digestion allow organic matter to be degraded and converted into biogas, which is a key factor for clean energy generation. It is thus realized that the study of bioreactors in batch mode and anaerobic digestion process is crucial to the development of research related to renewable energy generation. For a better understanding of the process, some authors have proposed studies based on parameters identification in descriptive nonlinear models, white-box models, which are widely used in bioreactors modeling. The main limitation of this approach is that the system identification procedure using these models can be complex and time-consuming, or even the parameters of the systems may not be identifiable. In order to overcome these difficulties, we propose in this work the use of black-box polynomial models for bioreactor modeling in batch mode, with NAR and NARMA model structures. Black-box models represent real systems using its output, without explicitly considering the inner mechanisms of the system, simplifying the identification procedure. Thus, the aim of this work is to investigate the prediction and monitoring methane production using the black-box models proposed using bioreactor systems in batch and anaerobic digestion process. The investigation uses numerical simulation and experimental data. At first, polynomial models of the types NAR and NARMA are proposed. The parameters from these models using simulation data with and without noise at the output, based on initial conditions proposed in the literature, are estimated. Subsequently we perform validations of these models. The next step is the study of the validity domain of the proposed black-box models, which is performed by testing many different initial conditions of the system that represents bioreactors in batch fermentation mode. Finally, we used real experimental data to perform the estimation of the parameters from the process and validation of models. The results, both simulated and experimental, indicate that the polynomial models NAR and NARMA are appropriate for prediction of methane fermentation in batch bioreactors.

The purpose of this thesis was to implement an Model Predictive Control based system identification method on a micro-aerial vehicle (DJI Matrice 100) as outlined in a study performed by ETH Zurich. Through limited test flights, data was obtained that allowed for the generation of first and second order system models. The first order models were robust, but the second order model fell short due to the fact that the data used for the model was not sufficient.

<p>Inom processindustrin finns en etablerad reglerhierarki där basreglering sker med PID-regulatorer och där avancerad, multivariabel styrning sköts av MPC-programvara. Steget mellan dessa två nivåer kan upplevas som stort. För mindre och snabba multivariabla processer undvikes helst en multivariabel ansats, med försämrad reglering som följd. På Preem AB har detta upplevts som ett problem. Syftet med examensarbetet har varit att utveckla en alternativ, multivariabel styrstrategi för en process med ett mindre antal reglerstorheter som interagerar. Detta har gjorts genom en utveckling av en LQG-regulator i styrsystemet DeltaV.</p><p>För att implementera en regulator i ett styrsystem måste hänsyn tas till en rad faktorer, såsom hantering av olika körlägen, bortfall av signaler, integratoruppvridning, kommunikation med slavregulatorer och inte minst operatörernas gränssnitt för hantering av regulatorn. Att sedan utveckla en regulator för en process kräver bland annat stegförsök, analys och anpassning av stegtestdata, modellidentifiering, framtagning av trimningskonstanter, testning av styrstrategi i simulerad miljö och idrifttagning. Den typen av frågeställningar addresseras i rapporten.</p><p>Examensarbetet visar att det finns en plats för LQG-regulatorn i processindustrin för en viss typ av problem. Den utvecklade regulatorn har implementerats på en avsvavlingsprocess på Preems oljeraffenaderi i Lysekil med lyckat resultat. Oscillationer i processen, som tidvis påverkat produktionen av propen, har kunnat reduceras.</p> / <p>Process control in process industry is done in a hierarchy in which PID controllers are used for basic control and MPC software is used for advanced, multivariable process control. The implementation of multivariable control using MPC software is a major undertaking and development of such controllers for small and fast multivariable processes is therefore avoided. To achieve better control for such processes, a simpler approach to multivariable control is often sought. The purpose of this masters thesis is to develop an alternative, multivariable control strategy for processes with a smaller number of interacting control variables. This is achieved by developing an LQG-controller in the DCS DeltaV at Preem AB.</p><p>Implementation of such a controller in a DCS requires that consideration is given to a number of factors, including handling of different modes, loss of signals, reset windup, communication with slave controllers and construction of operator interface. To develop a controller for a specific process also requires step testing, model identification, tuning of the controller parameters, simulation of the control strategy and commissioning. Solutions to such issues are addressed in this report.</p><p>The thesis shows that  LQG-controllers can be useful in process industry for some niche applications. The LQG-controller has successfully been applied to a desulphurisation process at Preem's oil refinery in Lysekil, where oscillations affecting the production of propylene have been reduced.</p>

<p>Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications. </p><p>System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear systems exists. </p><p>This is a master thesis in systems biology treating identification of biochemical systems. Methods based on both local parameter perturbation data and time series data have been tested and evaluated in silico. </p><p>The advantage of local parameter perturbation data methods proved to be that they demand less complex data, but the drawbacks are the reduced information content of this data and sensitivity to noise. Methods employing time series data are generally more robust to noise but the lack of available data limits the use of these methods. </p><p>The work has been conducted at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics in Göteborg, and at the division of Computational Biology at the Department of Physics and Measurement Technology, Biology, and Chemistry at Linköping University during the autumn of 2004.</p>

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

Tuned mass dampers (TMDs) are auxiliary damping devices installed within tall structures to reduce undesirable wind-induced vibrations and to enhance the overall system damping and hence, the dissipative capacity. The design of TMDs involves the selection of optimal auxiliary mass, frequency, and damping, based on the main structure's mass, natural frequency and damping properties. TMDs are inherently susceptible to detuning, where the auxiliary parameters are no longer optimal due to deterioration or changes within the system, resulting in a degradation in their performance. In order to correct for this detuning, it is necessary to perform a condition assessment while the TMDs are in service. The main goal of this thesis is to present a methodology to conduct condition assessment while the TMDs are in service. The proposed methodology does not involve either restraining the TMD or providing controlled external excitation to the structure, and relies on ambient measurements only. The first phase in the condition assessment is to estimate the bare structure's modal properties using acceleration measurements obtained from the structure while the TMDs are unrestrained. The present work accomplishes this goal within the framework of parametric identification using Kalman filtering, where the unknown parameters (bare modal properties) are appended to the state vector and estimated. Unlike most of the literature on this subject, the noise statistics for the filter are not assumed to be known a priori. They are estimated from the measurements and incorporated into the filter equations. This filter involves direct feedthrough of the process noise in the measurement equation and the appropriate filter is derived and used following the noise covariance estimation step. In the next phase, criteria to assess the condition of the TMD are developed. They include optimal tuning parameters established using simulated experiments and measured equivalent viscous damping. The research considered pendulum tuned mass dampers (PTMDs), which presently account for a large fraction of full-scale applications. Results were demonstrated using numerical investigations, a bench-scale model equipped with an adaptive mechanism for adjusting auxiliary damper parameters, and a full-scale PTMD-equipped structure. The main contributions of this thesis are: (a) a broader understanding of the coupled biaxial behaviour of PTMDs has been developed; (b) a systematic procedure for estimating the underlying modal characteristics of the structure from ambient vibration measurements within the framework of Kalman filtering has been achieved; (c) a comprehensive framework to undertake condition assessment of TMDs has been presented, integrating parametric identification from measured response data and performance prediction for design period wind events using boundary layer wind tunnel studies. The work provided new insight into the design and behaviour of PTMDs and presented a comprehensive approach to quantify their performance. The Kalman filtering framework also provides an efficient platform to build adaptive passive tuned mass dampers that can be tuned in place and adjusted to correct for detuning and accommodate various operating conditions.

The goal of this project is to apply system identification techniques to model people's perception of emotion in music as a function of time. Emotional appraisals of six selections of classical music are measured from volunteers who continuously quantify emotion using the dimensions valence and arousal. Also, features that communicate emotion are extracted from the music as a function of time. By treating the features as inputs to a system and the emotional appraisals as outputs of that system, linear models of the emotional appraisals are created. The models are validated by predicting a listener's emotional appraisals of a musical selection (song) unfamiliar to the system. The results of this project show that system identification provides a means to improve previous models for individual songs by allowing them to generalize emotional appraisals for a genre of music. The average <i>R</i>² statistic of the best model structure in this project is 7. 7% for valence and 75. 1% for arousal, which is comparable to the <i>R</i>² statistics for models of individual songs.

Inom processindustrin finns en etablerad reglerhierarki där basreglering sker med PID-regulatorer och där avancerad, multivariabel styrning sköts av MPC-programvara. Steget mellan dessa två nivåer kan upplevas som stort. För mindre och snabba multivariabla processer undvikes helst en multivariabel ansats, med försämrad reglering som följd. På Preem AB har detta upplevts som ett problem. Syftet med examensarbetet har varit att utveckla en alternativ, multivariabel styrstrategi för en process med ett mindre antal reglerstorheter som interagerar. Detta har gjorts genom en utveckling av en LQG-regulator i styrsystemet DeltaV. För att implementera en regulator i ett styrsystem måste hänsyn tas till en rad faktorer, såsom hantering av olika körlägen, bortfall av signaler, integratoruppvridning, kommunikation med slavregulatorer och inte minst operatörernas gränssnitt för hantering av regulatorn. Att sedan utveckla en regulator för en process kräver bland annat stegförsök, analys och anpassning av stegtestdata, modellidentifiering, framtagning av trimningskonstanter, testning av styrstrategi i simulerad miljö och idrifttagning. Den typen av frågeställningar addresseras i rapporten. Examensarbetet visar att det finns en plats för LQG-regulatorn i processindustrin för en viss typ av problem. Den utvecklade regulatorn har implementerats på en avsvavlingsprocess på Preems oljeraffenaderi i Lysekil med lyckat resultat. Oscillationer i processen, som tidvis påverkat produktionen av propen, har kunnat reduceras. / Process control in process industry is done in a hierarchy in which PID controllers are used for basic control and MPC software is used for advanced, multivariable process control. The implementation of multivariable control using MPC software is a major undertaking and development of such controllers for small and fast multivariable processes is therefore avoided. To achieve better control for such processes, a simpler approach to multivariable control is often sought. The purpose of this masters thesis is to develop an alternative, multivariable control strategy for processes with a smaller number of interacting control variables. This is achieved by developing an LQG-controller in the DCS DeltaV at Preem AB. Implementation of such a controller in a DCS requires that consideration is given to a number of factors, including handling of different modes, loss of signals, reset windup, communication with slave controllers and construction of operator interface. To develop a controller for a specific process also requires step testing, model identification, tuning of the controller parameters, simulation of the control strategy and commissioning. Solutions to such issues are addressed in this report. The thesis shows that  LQG-controllers can be useful in process industry for some niche applications. The LQG-controller has successfully been applied to a desulphurisation process at Preem's oil refinery in Lysekil, where oscillations affecting the production of propylene have been reduced.

Large marine vessels carry their loads all over the world. It can be a container ship carrying over 10 000 containers filled with foods, textiles and electronics or a bulk freighter carrying 400 000 tons of coal. Vessels usually have a ballast system that pumps water into ballast tanks to stabilize the vessel. The ballast system can be used to change the vessel’s trim and list angles. Trim and list are the ship equivalents of pitch and roll. By changing the trim angle the water resistance can be reduced and thus also the fuel consumption. Since the vessel is consuming a couple of hundred tons of fuel per day, a small reduction in fuel consumption can save a considerable amount of money, and it is good for the environment. In this thesis, the ship’s power consumption has been estimated using an artificial neural network, which is a mathematical model based on data. The name refers to certain structural similarities with the neural synapse system in animals. The idea with neural networks has been to create brain-like systems. For applications such as learning to interpret sensor data, artificial neural networks are an effective learning method. The goal is to estimate the ship power using a artificial neural network and then use it to calculate the trim angle, to be able to save fuel. The data used in the artificial neural network come from sensor systems mounted on a container ship sailing between Europe and Asia. The sensor data have been thoroughly preprocessed and this includes for example removing the parts when the ship is docked in harbour, data patching and synchronisation and outlier detection based on a Kalman filter. A physical model of a marine craft including wind, wave, hydrodynamic and hydrostatic effects, has also been introduced to help analyse the performance and behaviour of the artificial neural network. The artificial neural network developed in this thesis could successfully estimate the power consumption of the ship. Based on the developed networks it can be seen that the fuel consumption is reduced by trimming the ship by bow, i.e., the ship is angled so the bow is closer to the water line than the stern. The method introduced here could also be applied on other marine vessels, such as bulk freighters or tank ships.

Haptic applications often employ devices with many degrees of freedom in order to allow the user to have natural movement during human-machine interaction. From the development point of view, the complexity in mechanical dynamics imposes a lot of challenges in modelling the behaviour of the device. Traditional system identification methods for nonlinear systems are often computationally expensive. Moreover, current research on using neural network approaches disconnect the physical device dynamics with the identification process. This thesis proposes a different approach to system identification of complex haptic devices when analytical models are formulated. It organizes the unknowns to be identified based on the governing dynamic equations of the device and reduces the cost of computation. All the experimental work is done with the Freedom 6S, a haptic device with input and feedback in positions and velocities for all 6 degrees of freedom . <br /><br /> Once a symbolic model is developed, a subset of the overall dynamic equations describing selected joint(s) of the haptic robot can be obtained. The advantage of being able to describe the selected joint(s) is that when other non-selected joints are physically fixed or locked up, it mathematically simplifies the subset dynamic equation. Hence, a reduced set of unknowns (e. g. mass, centroid location, inertia, friction, etc) resulting from the simplified subset equation describes the dynamic of the selected joint(s) at a given mechanical orientation of the robot. By studying the subset equations describing the joints, a locking sequence of joints can be determined to minimize the number of unknowns to be determined at a time. All the unknowns of the system can be systematically determined by locking selected joint(s) of the device following this locking sequence. Two system identification methods are proposed: Method of Isolated Joint and Method of Coupling Joints. Simulation results confirm that the latter approach is able to successfully identify the system unknowns of Freedom 6S. Both open-loop experimental tests and close-loop verification comparison between the measured and simulated results are presented. <br /><br /> Once the haptic device is modelled, fuzzy logic is used to address chattering phenomenon common to strong virtual effects. In this work, a virtual wall is used to demonstrate this approach. The fuzzy controller design is discussed and experimental comparison between the performance of using a proportional-derivative gain controller and the designed fuzzy controller is presented. The fuzzy controller is able to outperform the traditional controller, eliminating the need for hardware upgrades for improved haptic performance. Summary of results and conclusions are included along with suggested future work to be done.