The main philosophy underlying this research is that a model should constitute a representation of both what we know and what we do not know about the structure and behavior of a system. In other words it should summarize, as far as possible, both our degree of certainty and degree of uncertainty, so that it facilitates statements about prediction uncertainty arising from model structural uncertainty. Based on this philosophy, the following issues were explored in the dissertation: Identification of a hydrologic system model based on assumption about perceptual and conceptual models structure only, without strong additional assumptions about its mathematical structure Development of a novel data assimilation method for extraction of mathematical relationships between modeled variables using a Bayesian probabilistic framework as an alternative to up-scaling of governing equations Evaluation of the uncertainty in predicted system response arising from three uncertainty types: o uncertainty caused by initial conditions, o uncertainty caused by inputs, o uncertainty caused by mathematical structure Merging of theory and data to identify a system as an alternative to parameter calibration and state-updating approaches Possibility of correcting existing models and including descriptions of uncertainty about their mapping relationships using the proposed method Investigation of a simple hydrological conceptual mass balance model with two-dimensional input, one-dimensional state and two-dimensional output at watershed scale and different temporal scales using the method

No description available.

This thesis explores the possibility of controller restructuring for improved closed-loop performance of nonlinear plants using a gradient based method of symbolic adaptation- Model Structure Adaptation Method (MSAM). The adaptation method starts with a controller which is a linear controller designed according to the linearized model of the nonlinear plant. This controller is then restructured into a series of nonlinear candidate controllers and adapted iteratively toward a desired closed-loop response. The noted feature of the adaptation method is its ability to quantify structural perturbations to the controllers. This quantification is important in scaling the structural Jacobian that is used in gradient-based adaptation of the candidate controllers. To investigate this, two nonlinear plants with unknown nonlinearities viz., nonlinear valve and nonlinear inverted pendulum are chosen. Furthermore, the properties of restructured controllers obtained for two systems, stability, effect of measurement noise, reachability, scalability and algorithmic issues of MSAM are studied and compared with the starting controller.

<p>Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors.</p><p>The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors.</p><p>Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters.</p><p>In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented. </p>

Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors. The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors. Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters. In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.

In order to support an integrated management policy and the rational use of water resources toward sustainable development, it is unavoidable the search of efficient instruments to measure the performance of hydrological and environmental systems. Indicators and indexes have the role of translating numerically one specific situation and point out to the decision maker the sustainability status of that region. The development of a hydro-environmental sustainability index complies a multidisciplinary analysis dealing with several interrelated aspects of hydrologic and environmental parameters, based on some important criteria such as: water availability, quality and use of water, people access to water, environment impact.To the development of such an index in the State of Cearà a representative area of the semiarid highland environment has been chosen: Baturità APA (Area of Environmental Protection).A model structure based on the Pression-State-Response approach has been considered, resulting in the proposition of a Hydro-Environmental Sustainability Index for the highlands in the Semiarid Brazilian Region. / No intuito de dar suporte à uma polÃtica de gestÃo integrada e uso racional dos recursos hÃdricos de forma a garantir um desenvolvimento sustentÃvel à imprescindÃvel a aplicaÃÃo de ferramentas capazes de medir o desempenho dos sistemas hÃdricos e ambientais. Os indicadores e Ãndices tÃm o papel de traduzir numericamente uma determinada situaÃÃo e apontar, ao tomador de decisÃo, o sentido da sustentabilidade de uma regiÃo. O desenvolvimento de um Ãndice de sustentabilidade hidroambiental (ISHA) corresponde a uma anÃlise multidisciplinar tratando de vÃrios aspectos de inter-relacionamento entre parÃmetros hÃdricos e ambientais tendo como base alguns critÃrios importantes como: disponibilidade hÃdrica, qualidade e uso da Ãgua, acesso à mesma e impacto no meio ambiente. Para o desenvolvimento de um Ãndice desta natureza no CearÃ, foi escolhida uma Ãrea representativa de Ambientes Serranos no SemiÃrido do Estado - a APA (Ãrea de ProteÃÃo Ambiental) do MaciÃo BaturitÃ. Foi considerado um modelo estrutural baseado na abordagem PressÃo-Estado-Resposta propondo uma metodologia de desenvolvimento de um Ãndice de Sustentabilidade Hidroambiental para Ãreas serranas do SemiÃrido brasileiro.

System identification is used in engineering sciences to build mathematical models from data. A common issue in system identification problems is that the true inputs to the system are not fully known. In this thesis, existing approaches to unknown input problems are classified and some of their properties are analyzed.  A new indirect framework is proposed to treat system identification problems with unknown inputs. The effects of the unknown inputs are assumed to be measured through possibly unknown dynamics. Furthermore, the measurements may also be dependent on other known or measured inputs and can in these cases be called indirect input measurements. Typically, these indirect input measurements can arise when a subsystem of a larger system is of interest and only a limited set of sensors is available. Two examples are when it is desired to estimate parts of a mechanical system or parts of a dynamic network without full knowledge of the signals in the system. The input measurements can be used to eliminate the unknown inputs from a mathematical model of the system through algebraic manipulations. The resulting indirect model structure only depends on known and measured signals and can be used to estimate the desired dynamics or properties. The effects of using the input measurements are analyzed in terms of identifiability, consistency and variance properties. It is shown that cancelation of shared dynamics can occur and that the resulting estimation problem is similar to errors-in-variables and closed-loop estimation problems because of the noisy inputs used in the model. In fact, the indirect framework unifies a number of already existing system identification problems that are contained as special cases. For completeness, an instrumental variable method is proposed as one possibility for estimating the indirect model. It is shown that multiple datasets can be used to overcome certain identifiability issues and two approaches, the multi-stage and the joint identification approach, are suggested to utilize multiple datasets for estimation of models. Furthermore, the benefits of using the indirect model in filtering and for control synthesis are briefly discussed.  To show the applicability, the framework is applied to the roll dynamics of a ship for tracking of the loading conditions. The roll dynamics is very sensitive to changes in these conditions and a worst-case scenario is that the ship will capsize.  It is assumed that only motion measurements from an inertial measurement unit (IMU) together with measurements of the rudder angle are available. The true inputs are thus not available, but the measurements from the IMU can be used to form an indirect model from a well-established ship model. It is shown that only a subset of the unknown parameters can be estimated simultaneously. Data was collected in experiments with a scale ship model in a basin and the joint identification approach was selected for this application due to the properties of the model. The approach was applied to the collected data and gave promising results. / Till skillnad från många andra industrier där avancerade styrsystem har haft en omfattande utveckling under de senaste decennierna så har styrsystem för skepps- och marinindustrin inte alls utvecklats i samma utsträckning. Det är framförallt under de senaste 10 åren som lagkrav och stigande driftskostnader har ökat intresset för effektivitet och säkerhet genom användning av styrsystem. Rederier och den marina industrin är nu intresserade av hur de avancerade styrsystem som används inom andra områden kan tillämpas för marina ändamål. Huvudmålet är typiskt att minska den totala energianvändningen, och därmed också bränsleförbrukningen, genom att hela tiden planera om hur skeppet skall användas med hjälp av ny information samt styra skeppet och dess ingående system på ett sätt som maximerar effektiviteten. För många av dessa avancerade styrsystem är det grundläggande att ha en god förståelse för beteendet hos det systemet som skall styras. Ofta används matematiska modeller av systemet för detta ändamål. Sådana modeller kan skapas genom att observera hur systemet reagerar på yttre påverkan och använda dessa observationer för att finna eller skatta den modell som bäst beskriver observationerna. Observationerna är mätningar som görs med så kallade sensorer och tekniken att skapa modeller från mätningarna kallas för systemidentifiering. Detta är i grunden ett utmanande problem och det kan försvåras ytterligare om de sensorer som behövs inte finns tillgängliga eller är för dyra att installera. I denna avhandling föreslås en ny teknik där de mätningar som finns tillgängliga används på ett nytt och annorlunda sätt. Detta kan minska mängden nödvändiga sensorer eller möjliggöra användandet av alternativa sensorer i modell-framtagningen. Med hjälp av denna nya teknik kan enkla sensorer användas för att skatta en matematisk modell för en del av skeppet på ett sätt som inte är möjligt med traditionella metoder. Genom att skatta denna modell kan fysikaliska egenskaper hos skeppet, så som dess massa och hur massan är fördelad över skeppet, övervakas för att upptäcka förändringar. Just dessa två egenskaper har stor inverkan på hur skeppet beter sig och om skeppet är fellastat kan det i värsta fall kapsejsa. Vetskapen om dessa fysikaliska egenskaper kan alltså utöver effektivisering användas för att varna besättningen eller påverka styrsystemen så att farliga manövrar undviks. För att visa att tekniken fungerar i verkligheten har den använts på mätningar som har samlats in från ett skalenligt modellskepp. Experimenten utfördes i bassäng och resultaten visar att tekniken fungerar. Denna nya teknik är inte specifik för marint bruk utan kan också vara användbar i andra typer av tillämpningar. Även i dessa tillämpningar möjliggörs användandet av färre eller alternativa sensorer för att skatta modeller. Tekniken kan vara speciellt användbar när en modell av ett system eller process som verkar i ett nätverk av många system är av intresse, något som också diskuteras i avhandlingen.

Model based techniques for automated condition monitoring of HVAC systems have been under development for some years. Results from the application of these methods to systems installed in real buildings have highlighted robustness and sensitivity issues. The generation of false alarms has been identified as a principal factor affecting the potential usefulness of condition monitoring in HVAC applications. The robustness issue is a direct result of the uncertain measurements and the lack of experimental control that axe characteristic of HVAC systems. This thesis investigates the uncertainties associated with implementing a condition monitoring scheme based on simple first principles models in HVAC subsystems installed in real buildings. The uncertainties present in typical HVAC control system measurements are evaluated. A sensor validation methodology is developed and applied to a cooling coil subsystem installed in a real building. The uncertainty in steady-state analysis based on transient data is investigated. The uncertainties in the simplifications and assumptions associated with the derivation of simple first principles based models of heat-exchangers are established. A subsystem model is developed and calibrated to the test system. The relationship between the uncertainties in the calibration data and the parameter estimates are investigated. The uncertainties from all sources are evaluated and used to generate a robust indication of the subsystem condition. The sensitivity and robustness of the scheme is analysed based on faults implemented in the test system during summer, winter and spring conditions.

No description available.

[pt] Sales and Operations Planning (SeOP), também chamado de Integrated Business Planning (IBP), é um processo de negócios que integra os planejamentos dos setores de uma empresa de forma a balancear a oferta e a demanda por produtos em um horizonte de planejamento de médio prazo, apoiando assim a integração horizontal da empresa. O S(e)OP/IBP serve também de ponte entre os planejamentos estratégico e operacional, apoiando assim a integração vertical da empresa. Apesar de esse processo ter adquirido um papel de destaque na área de gestão da cadeia de suprimentos, os benefícios esperados ainda não foram totalmente obtidos, na prática, pela indústria. Além disso, a literatura indica a necessidade de uma caracterização completa do processo e que um modelo de referência beneficiaria os usuários do processo. Visando endereçar tais carências, esta dissertação elabora uma estrutura de modelo de referência preliminar para o processo de S(e)OP/IBP, com base em trabalhos acadêmicos, white papers escritos por praticantes, um estudo de caso em uma indústria química do Brasil e a aplicação de grupos focais e painel de especialistas em S(e)OP/IBP. Este trabalho contribui com um estudo empírico no tema, além de oferecer uma estrutura de modelo de referência preliminar, avaliada por praticantes de S(e)OP/IBP, que pode ser usada como inspiração para empresas que desejam desenhar ou melhorar seu processo e por acadêmicos em pesquisas futuras com o intuito de aprimorá-lo. Ademais, são apresentados 13 pontos de atenção a serem avaliados pelos praticantes, a fim de amenizar os impactos das diferenças entre contextos. / [en] Sales and Operations Planning (SandOP), also called Integrated Business Planning (IBP), is a business process that integrates business plans from different functional areas of a firm to balance supply and demand of products in a mid-term planning horizon, thus supporting the horizontal integration of the firm. S(and)OP/IBP also serves as a bridge between strategic and operational plans, thus supporting the vertical integration of the firm. Although this process has gained prominence in the area of supply chain management, the expected benefits at the time of implementation have not yet been fully realized in practice by the industry. Moreover, the literature indicates the need for a complete characterization of the process and that a reference model would benefit process users. To address these research-practice gaps, this dissertation elaborates a preliminary reference model structure for the S(and)OP/IBP process, based on academic papers, white papers written by practitioners, a case study in a Brazilian chemical industry and the application of focus group with S(and)OP/IBP experts. This work contributes with an empirical study on the theme, as well as it offers a preliminary reference model structure, evaluated by practitioners of S(and)OP/IBP, which can be used as a guidance for firms willing to design or improve their own process and for academics in future researches with the aim of refining it. In addition, 13 points of attention to be assessed by practitioners are presented in order to mitigate the impacts of differences between contexts.