[pt] A partir da forma geral do algoritmo de adaptação, é proposto o uso de mais uma variável de projeto, denominada janela. Esta janela tem por objetivo melhorar as características de convergência de algoritmos, cujos parâmetros são partes de estruturas IIR. A introdução das janelas é justificada heuristicamente e seu desempenho é avaliado por meio de diversas simulações de identificação de sistemas. Os resultados obtidos indicam aumentos significativos na velocidade de convergência (cerca de uma ordem de grandeza mais rápido do que os algoritmos atualmente mais usados), na precisão das estimativas dos parâmetros do problema e na robustez dos novos algoritmos (menor número de pólos instáveis durante a adaptação). Estes resultados foram observados em ambientes estacionários e não estacionários, com e sem ruído de medida e com ordem de identificação suficiente ou não. / [en] From the adaptation algorithm general form it is proposed the usage of another design variable, called Window. The goal of this Window is to improve the convergence characteristics of algorithms whose parameters are parts of IIR Structures. The introduction of the Window is heuristically justified and its performance is eventuated by several system identification simulations. The results achieved suggest significant increase in the convergence speed (about one order of magnitude faster than the currently most used algorithms), in the parameter estimation precision and in the new algorithm robusteness (fewer unstable poles during adaptation). These results were observed in sationary and non-stationary environments, with and without measurement noise and with sufficient identification order or not.

A identificação de sistemas em malha fechada assume papel importante no contexto atual, já que reduz o custo operacional do processo de identificação no estágio de testes, evitando, por exemplo, a geração de produtos fora de especificação. Entretanto, requer uma série de cuidados especiais no tratamento dos dados a serem analisados para a obtenção dos modelos. Nesta dissertação um estudo acerca da identificação de sistemas monovariáveis lineares a partir de dados de operação em malha fechada, avaliando diferentes métodos e técnicas de identificação em malha fechada já consolidados é apresentado. Um novo método, recentemente proposto por Agüero (2005), o Virtual Closed Loop ouMétodo da Malha Virtual, que consiste na adição e remoção virtual de um controlador à malha analisada, de forma a filtrar a ação de controle real em um procedimento totalmente offline, é estudado em detalhes e uma adaptação é realizada na metodologia, com a simplificação do filtro virtual e forma de obtenção do modelo da malha aberta. O desenvolvimento e estudo da metodologia estão baseados em uma série de sistemas SISO distintos, com dinâmicas variáveis. Além disso, diferentes cenários com características peculiares são aplicados ao estudo, como distúrbios não medidos e ruído de medição, ilustrando de forma simples possíveis comportamentos dinâmicos encontrados em plantas industriais. / The identification of closed loop systems has taken on an important role in the current context, since it reduces the operational costs of the identification process in the testing stage, thus avoiding, for instance, the creation of non-specified products. However, it requires special care in the treatment of data to be analyzed for the obtainment of models. In this work, we present a study on the identification of linear models from closed loops operational data, evaluating different consolidated methods and techniques of closed loop identification. A new method is studied in detail in this work: the Virtual Closed Loop, which was proposed by Agüero (2005). It consists of the virtual addition and removal of a controller to the analyzed loop, so as to filter the input of the system in a completely offline procedure. It is also presented modifications on this methodology resulting in an simplification of the virtual filter and in the ways to obtain the open loop model. The development and study of this methodology are both based on different SISO systems, with variable dynamics. Other interesting characteristics, peculiar to the study, are considered in this work, as noise and dither signals. It illustrates, in a simple way, possible dynamic behavior patterns found in industrial plants.

abstract: The lack of healthy behaviors - such as physical activity and balanced diet - in modern society is responsible for a large number of diseases and high mortality rates in the world. Adaptive behavioral interventions have been suggested as a way to promote sustained behavioral changes to address these issues. These adaptive interventions can be modeled as closed-loop control systems, and thus applying control systems engineering and system identification principles to behavioral settings might provide a novel way of improving the quality of such interventions. Good understanding of the dynamic processes involved in behavioral experiments is a fundamental step in order to design such interventions with control systems ideas. In the present work, two different behavioral experiments were analyzed under the light of system identification principles and modelled as dynamic systems. In the first study, data gathered over the course of four days served as the basis for ARX modeling of the relationship between psychological constructs (negative affect and self-efficacy) and the intensity of physical activity. The identified models suggest that this behavioral process happens with self-regulation, and that the relationship between negative affect and self-efficacy is represented by a second order underdamped system with negative gain, while the relationship between self-efficacy and physical activity level is an overdamped second order system with positive gain. In the second study, which consisted of single-bouts of intense physical activity, the relation between a more complex set of behavioral variables was identified as a semi-physical model, with a theoretical set of system equations derived from behavioral theory. With a prescribed set of physical activity intensities, it was found that less fit participants were able to get higher increases in affective state, and that self-regulation processes are also involved in the system. / Dissertation/Thesis / Masters Thesis Chemical Engineering 2016

A identificação de sistemas em malha fechada assume papel importante no contexto atual, já que reduz o custo operacional do processo de identificação no estágio de testes, evitando, por exemplo, a geração de produtos fora de especificação. Entretanto, requer uma série de cuidados especiais no tratamento dos dados a serem analisados para a obtenção dos modelos. Nesta dissertação um estudo acerca da identificação de sistemas monovariáveis lineares a partir de dados de operação em malha fechada, avaliando diferentes métodos e técnicas de identificação em malha fechada já consolidados é apresentado. Um novo método, recentemente proposto por Agüero (2005), o Virtual Closed Loop ouMétodo da Malha Virtual, que consiste na adição e remoção virtual de um controlador à malha analisada, de forma a filtrar a ação de controle real em um procedimento totalmente offline, é estudado em detalhes e uma adaptação é realizada na metodologia, com a simplificação do filtro virtual e forma de obtenção do modelo da malha aberta. O desenvolvimento e estudo da metodologia estão baseados em uma série de sistemas SISO distintos, com dinâmicas variáveis. Além disso, diferentes cenários com características peculiares são aplicados ao estudo, como distúrbios não medidos e ruído de medição, ilustrando de forma simples possíveis comportamentos dinâmicos encontrados em plantas industriais. / The identification of closed loop systems has taken on an important role in the current context, since it reduces the operational costs of the identification process in the testing stage, thus avoiding, for instance, the creation of non-specified products. However, it requires special care in the treatment of data to be analyzed for the obtainment of models. In this work, we present a study on the identification of linear models from closed loops operational data, evaluating different consolidated methods and techniques of closed loop identification. A new method is studied in detail in this work: the Virtual Closed Loop, which was proposed by Agüero (2005). It consists of the virtual addition and removal of a controller to the analyzed loop, so as to filter the input of the system in a completely offline procedure. It is also presented modifications on this methodology resulting in an simplification of the virtual filter and in the ways to obtain the open loop model. The development and study of this methodology are both based on different SISO systems, with variable dynamics. Other interesting characteristics, peculiar to the study, are considered in this work, as noise and dither signals. It illustrates, in a simple way, possible dynamic behavior patterns found in industrial plants.

As trucks are getting more and more advanced, information about their weight has become a key factor for controlling them in a more fuel efficient and safe manner. Knowing the mass of a heavy duty vehicle in real time has been a difficult challenge for the truck manufacturers. With the processing power for electronic control units in trucks steadily increasing, more advanced algorithms for calculating the mass has been developed, but at the moment there still is a wish for better performance. Since there is a lack of good information regarding the external forces acting on the vehicle, forces that depends on the slope of the road, foundation of the road and the wind, the methods have to be able to disregard these. Such an approach, based on an indirect least square solution, has been evaluated in this thesis. The results have been promising and based on these a recommendation about further evaluation has been made.

Please read the abstract (Summary) in the section, 00front of this document / Dissertation (M Eng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted

A flight data acquisition system was developed to aid unmanned vehicle designers in verifying the vehicle's design performance. The system is reconfigurable and allows the designer to choose the correct combination of complexity, risk, and cost for a given flight test. The designer can also reconfigure the system to meet packaging and integration requirements. System functionality, repeatbility, and accuracy was validated by collecting data during multiple flights of a radio-controlled aircraft. Future work includes sensor fusion, thrust prediction methods, stability and control derivative estimation, and growing Cal Poly's small-scale component aerodynamic database.

No description available.

This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.

In science, technology, and engineering, creating models of the environment to predict future events has always been a key component. The models could be everything from how the friction of a tire depends on the wheels slip  to how a pathogen is spread throughout society.  As more data becomes available, the use of data-driven black-box models becomes more attractive. In many areas they have shown promising results, but for them to be used widespread in safety-critical applications such as autonomous driving some notion of uncertainty in the prediction is required. An example of such a black-box model is neural networks (NNs). This thesis aims to increase the usefulness of NNs by presenting an method where uncertainty in the prediction is obtained by linearization of the model. In system identification and sensor fusion, under the condition that the model structure is identifiable, this is a commonly used approach to get uncertainty in the prediction from a nonlinear model. If the model structure is not identifiable, such as for NNs, the ambiguities that cause this have to be taken care of in order to make the approach applicable. This is handled in the first part of the thesis where NNs are analyzed from a system identification perspective, and sources of uncertainty are discussed. Another problem with data-driven black-box models is that it is difficult to know how flexible the model needs to be in order to correctly model the true system. One solution to this problem is to use a model that is more flexible than necessary to make sure that the model is flexible enough. But how would that extra flexibility affect the uncertainty in the prediction? This is handled in the later part of the thesis where it is shown that the uncertainty in the prediction is bounded from below by the uncertainty in the prediction of the model with lowest flexibility required for representing true system accurately.  In the literature, many other approaches to handle the uncertainty in predictions by NNs have been suggested, of which some are summarized in this work. Furthermore, a simulation and an experimental studies inspired by autonomous driving are conducted. In the simulation study, different sources of uncertainty are investigated, as well as how large the uncertainty in the predictions by NNs are in areas without training data. In the experimental study, the uncertainty in predictions done by different models are investigated. The results show that, compared to existing methods, the linearization method produces similar results for the uncertainty in predictions by NNs. An introduction video is available at https://youtu.be/O4ZcUTGXFN0 / Inom forskning och utveckling har det har alltid varit centralt att skapa modeller av verkligheten. Dessa modeller har bland annat använts till att förutspå framtida händelser eller för att styra ett system till att bete sig som man önskar. Modellerna kan beskriva allt från hur friktionen hos ett bildäck påverkas av hur mycket hjulen glider till hur ett virus kan sprida sig i ett samhälle. I takt med att mer och mer data blir tillgänglig ökar potentialen för datadrivna black-box modeller. Dessa modeller är universella approximationer vilka ska kunna representera vilken godtycklig funktion som helst. Användningen av dessa modeller har haft stor framgång inom många områden men för att verkligen kunna etablera sig inom säkerhetskritiska områden såsom självkörande farkoster behövs en förståelse för osäkerhet i prediktionen från modellen. Neuronnät är ett exempel på en sådan black-box modell. I denna avhandling kommer olika sätt att tillförskaffa sig kunskap om osäkerhet i prediktionen av neuronnät undersökas. En metod som bygger på linjärisering av modellen för att tillförskaffa sig osäkerhet i prediktionen av neuronnätet kommer att presenteras. Denna metod är välbeprövad inom systemidentifiering och sensorfusion under antagandet att modellen är identifierbar. För modeller såsom neuronnät, vilka inte är identifierbara behövs det att det tas hänsyn till tvetydigheterna i modellen. En annan utmaning med datadrivna black-box modeller, är att veta om den valda modellmängden är tillräckligt generell för att kunna modellera det sanna systemet. En lösning på detta problem är att använda modeller som har mer flexibilitet än vad som behövs, det vill säga en överparameteriserad modell.  Men hur påverkas osäkerheten i prediktionen av detta? Detta är något som undersöks i denna avhandling, vilken visar att osäkerheten i den överparameteriserad modellen kommer att vara begränsad underifrån av modellen med minst flexibilitet som ändå är tillräckligt generell för att modellera det sanna systemet. Som avslutning kommer dessa resultat att demonstreras i både en simuleringsstudie och en experimentstudie inspirerad av självkörande farkoster. Fokuset i simuleringsstudien är hur osäkerheten hos modellen är i områden med och utan tillgång till träningsdata medan experimentstudien fokuserar på jämförelsen mellan osäkerheten i olika typer av modeller.Resultaten från dessa studier visar att metoden som bygger på linjärisering ger liknande resultat för skattningen av osäkerheten i prediktionen av neuronnät, jämfört med existerande metoder. / iQdeep