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.

This project discusses the use of System Identification for Smart Maintenance. System Identification is the process of finding a mathematical model of a system using empirical data. The mathematical model can then be used to detect and predict the maintenance needs, which is considered as Smart Maintenance. Smart maintenance strategies have gained pretty much importance recently, since it contributes to economically sustainable production. This project uses the LAVA-framework, proposed in [1] for non-linear system identification, which has the capability of explaining the dynamics of the system very well, and at the same time follows the principle of parsimony. A nominal model is first identified using data from a system that operates under normal operating conditions, then the identified nominal model is used to detect when the system starts to deviate from normal behavior, and these deviations indicate the deteriorations in the system. Furthermore, a new Multiple Model Method which is developed in [2] using the similar idea from LAVA, is applied on the large data set of a system that operates on separate batches and units, which identifies individual model for each batch and unit, which is then used to detect the deficient units or batches and changes in the system behavior. Finally, the proposed methods are applied to two different real world industrial cases; a Heat exchanger and a Wood Moulder Machine. In the first, the purpose is to detect the dirt in a Heat Exchanger, and in the second, the goal is to detect when the tool in a Wood Moulder Machine needs to be changed.

<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>

<p>In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. This optimization problem is often quite complex with several local minima and is commonly solved using a local search algorithm. Thus, it is important to find a good initial estimate for the local search algorithm. This is the main topic of this thesis.</p><p>The first problem considered is the regressor selection problem for estimating the order of dynamical systems. The general problem formulation is difficult to solve and the worst case complexity equals the complexity of the exhaustive search of all possible combinations of regressors. To circumvent this complexity, we propose a relaxation of the general formulation as an extension of the nonnegative garrote regularization method. The proposed method provides means to order the regressors via their time lag and a novel algorithmic approach for the \textsc{arx} and \textsc{lpv-arx} case is given.</p><p> </p><p>Thereafter, the initialization of linear time-invariant polynomial models is considered. Usually, this problem is solved via some multi-step instrumental variables method. For the estimation of state-space models, which are closely related to the polynomial models via canonical forms, the state of the art estimation method is given by the subspace identification method. It turns out that this method can be easily extended to handle the estimation of polynomial models. The modifications are minor and only involve some intermediate calculations where already available tools can be used. Furthermore, with the proposed method other a priori information about the structure can be readily handled, including a certain class of linear gray-box structures. The proposed extension is not restricted to the discrete-time case and can be used to estimate continuous-time models.</p><p> </p><p>The final topic in this thesis is the initialization of discrete-time systems containing polynomial nonlinearities. In the continuous-time case, the tools of differential algebra, especially Ritt's algorithm, have been used to prove that such a model structure is globally identifiable if and only if it can be written as a linear regression model. In particular, this implies that once Ritt's algorithm has been used to rewrite the nonlinear model structure into a linear regression model, the parameter estimation problem becomes trivial. Motivated by the above and the fact that most system identification problems involve sampled data, a version of Ritt's algorithm for the discrete-time case is provided. This algorithm is closely related to the continuous-time version and enables the handling of noise signals without differentiations.</p>

<p>Disturbing sound sometimes should be cancelled when music has been recorded. In this thesis, MATLAB was used as a tool. System identification was a main method used to find the unknown system. By subtracting the simulated output, disturbing sound was cancelled. Two different systems were identified with both linear (ARX) model and nonlinear (Parallel Hammerstein) model. The quality of these models was measured and compared using different methods. Possibility to implement this work on hardware was also discussed.</p>

<p>When doing recording work in the studio, disturbing sound must be removed. In this thesis, the purpose of this thesis is to formulate a mathematical equation, by using MATLAB to identify a system, then using the system to do cancellation of disturbing sound. The method of doing cancellation is to subtract the simulated output by the actual output, and then the disturbing sound was cancelled. The main thesis work will focus on the system identification, which is the process of determining the characteristic of an unknown system. Three systems were identified with the same model structure, which is linear (ARX) model. After finding out the model, the model quality must be evaluated. If the model is valid, there is a discussion if it is possible to run the mathematical equation in the real application, and how is the market today.</p>

In system identification, the choice of model structure is important and it is sometimes desirable to use a flexible model structure that is able to approximate a wide range of systems. One such model structure is the Wiener class of systems, that is, systems where the input enters a linear time-invariant subsystem followed by a time-invariant nonlinearity. Given a sequence of input and output pairs, the system identification problem is often formulated as the minimization of the mean-square prediction error. Here, the prediction error has a nonlinear dependence on the parameters of the linear subsystem and the nonlinearity. Unfortunately, this formulation of the estimation problem is often nonconvex, with several local minima, and it is therefore difficult to guarantee that a local search algorithm will be able to find the global optimum. In the first part of this thesis, we consider the application of dimension reduction methods to the problem of estimating the impulse response of the linear part of a system in the Wiener class. For example, by applying the inverse regression approach to dimension reduction, the impulse response estimation problem can be cast as a principal components problem, where the reformulation is based on simple nonparametric estimates of certain conditional moments. The inverse regression approach can be shown to be consistent under restrictions on the distribution of the input signal provided that the true linear subsystem has a finite impulse response. Furthermore, a forward approach to dimension reduction is also considered, where the time-invariant nonlinearity is approximated by a local linear model. In this setting, the impulse response estimation problem can be posed as a rank-reduced linear least-squares problem and a convex relaxation can be derived. Thereafter, we consider the extension of the subspace identification approach to include linear time-invariant rational models. It turns out that only minor structural modifications are needed and already available implementations can be used. Furthermore, other a priori information regarding the structure of the system can incorporated, including a certain class of linear gray-box structures. The proposed extension is not restricted to the discrete-time case and can be used to estimate continuous-time models. The final topic in this thesis is the estimation of discrete-time models containing polynomial nonlinearities. In the continuous-time case, a constructive algorithm based on differential algebra has previously been used to prove that such model structures are globally identifiable if and only if they can be written as a linear regression model. Thus, if we are able to transform the nonlinear model structure into a linear regression model, the parameter estimation problem can be solved with standard methods. Motivated by the above and the fact that most system identification problems involve sampled data, a discrete-time version of the algorithm is developed. This algorithm is closely related to the continuous-time version and enables the handling of noise signals without differentiations.

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.