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A Distributed Control Algorithm for Small Swarms in Cordon and PatrolAlder, C Kristopher 01 June 2016 (has links)
Distributed teams of air and ground robots have the potential to be very useful in a variety of application domains, and much work is being done to design distributed algorithms that produce useful behaviors. This thesis presents a set of distributed algorithms that operate under minimal human input for patrol and cordon tasks. The algorithms allow the team to surround and travel between objects of interest. Empirical analyses indicate that the surrounding behaviors are robust to variations on the shape of the object of interest, communication loss, and robot failures.
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Robustness and information processing constraints in economic modelsLewis, Kurt Frederick 01 January 2007 (has links)
In this dissertation, I examine the impact of uncertainty and information processing restrictions on standard economic models. Chapter 1 examines a reevaluation of the excess volatility puzzle in asset prices by assessing the impact of a shift in the agent's focus from minimizing average loss to minimizing maximum loss. Chapters 2 and 3 extend and clarify the newly developing arena of economic models in which the agent's capacity for information processing is systematically limited, as in the recent rational inattention literature.
Chapter 1, which represents joint work with Charles Whiteman, studies the consequences changing the present value formula for stock prices. In place of the squared-error-loss minimizing expected present value of future dividends, we use a predictor optimal for the min-max preference relationship appropriate in cases of ambiguity. With ``robust" predictions, the well-known variance bound is reversed in that prices are predicted to be far more volatile than what is observed. We also investigate an intermediate ``partially robust'' case in which the degree of ambiguity is limited, and discover that such an intermediate model cannot be rejected in favor of an unrestricted time series model.
Chapter 2 demonstrates the properties and solutions for the more general two-period rational inattention model. We show that the problem is convex, can be solved in seconds, and highlights several important features of information-processing-capacity-constrained models. Additionally, we show the importance of deriving, rather than assuming, the form of the final solution in rational inattention models.
Chapter 3 extends the work of Chapter 2 to a finite-horizon dynamic setting by creating a structure in which distributional state and control variables interact under information-processing constraints. Limited information processing capacity is used optimally, and agents have the opportunity to trade processing capacity for higher expected future income. The framework is applied to the canonical life-cycle model of consumption and saving, and an analysis of the impact of preference parameters on optimal attention allocation is conducted. The model produces a distinct hump-shaped profile in expected consumption.
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Feature Extraction for Automatic Speech Recognition in Noisy Acoustic Environments / Parameteruttrekning for automatisk talegjenkjenning i støyende omgivelserGajic, Bojana January 2002 (has links)
<p>This thesis presents a study of alternative speech feature extraction methods aimed at increasing robustness of automatic speech recognition (ASR) against additive background noise. </p><p>Spectral peak positions of speech signals remain practically unchanged in presence of additive background noise. Thus, it was expected that emphasizing spectral peak positions in speech feature extraction would result in improved noise robustness of ASR systems. If frequency subbands are properly chosen, dominant subband frequencies can serve as reasonable estimates of spectral peak positions. Thus, different methods for incorporating dominant subband frequencies into speech feature vectors were investigated in this study.</p><p>To begin with, two earlier proposed feature extraction methods that utilize dominant subband frequency information were examined. The first one uses zero-crossing statistics of the subband signals to estimate dominant subband frequencies, while the second one uses subband spectral centroids. The methods were compared with the standard MFCC feature extraction method on two different recognition tasks in various background conditions. The first method was shown to improve ASR performance on both recognition tasks at sufficiently high noise levels. The improvement was, however, smaller on the more complex recognition task. The second method, on the other hand, led to some reduction in ASR performance in all testing conditions.</p><p>Next, a new method for incorporating subband spectral centroids into speech feature vectors was proposed, and was shown to be considerably more robust than the standard MFCC method on both ASR tasks. The main difference between the proposed method and the zero-crossing based method is in the way they utilize dominant subband frequency information. It was shown that the performance improvement due to the use of dominant subband frequency information was considerably larger for the proposed method than for the ZCPA method, especially on the more complex recognition task. Finally, the computational complexity of the proposed method is two orders of magnitude lower than that of the zero-crossing based method, and of the same order of magnitude as the standard MFCC method.</p>
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Synthesis of PID controller from empirical data and guaranteeing performance specifications.Lim, Dongwon 15 May 2009 (has links)
For a long time determining the stability issue of characteristic polynomials has played avery important role in Control System Engineering. This thesis addresses the traditionalcontrol issues such as stabilizing a system with any certain controller analyzingcharacteristic polynomial, yet a new perspective to solve them. Particularly, in this thesis,Proportional-Integral-Derivative (PID) controller is considered for a fixed structuredcontroller. This research aims to attain controller gain set satisfying given performancespecifications, not from the exact mathematical model, but from the empirical data of thesystem. Therefore, instead of a characteristic polynomial equation, a speciallyformulated characteristic rational function is investigated for the stability of the systemin order to use only the frequency data of the plant. Because the performance satisfactionis highly focused on, the characteristic rational function for the investigation of thestability is mainly dealt with the complex coefficient polynomial case rather than realone through whole chapters, and the mathematical basis for the complex case is prepared.For the performance specifications, phase margin is considered first since it is avery significant factor to examine the system’s nominal stability extent (nominal performance). Second, satisfying H norm constraints is handled to make a more robustclosed loop feedback control system. Third, we assume undefined, but bounded outsidenoise, exists when estimating the system’s frequency data. While considering theseuncertainties, a robust control system which meets a given phase margin performance, isattained finally (robust performance).In this thesis, the way is explained how the entire PID controller gain setssatisfying the given performances mentioned in the above are obtained. The approachfully makes use of the calculating software e.g. MATLAB® in this research and isdeveloped in a systematically and automatically computational aspect. The result ofsynthesizing PID controller is visualized through the graphic user interface of acomputer.
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Tooth Interior Fatigue Fracture&Robustness of GearsMackAldener, Magnus January 2001 (has links)
The demands the automotive gear designer has to considerduring the gear design process have changed. To design a gearthat will not fail is still a challenging task, but now lownoise is also a main objective. Both customers and legalregulations demand noise reduction of gears. Moreover, thequality of the product is more in focus than ever before. Inaddition, the gear design process itself must be inexpensiveand quick. One can say that the gear designer faces a newdesign environment. The objective of this thesis is tocontribute to the answer to some of the questions raised inthis new design environment. In order to respond to the new design situation, the geardesigner must consider new phenomena of gears that werepreviously not a matter of concern. One such phenomenon is anew gear failure type, Tooth Interior Fatigue Fracture (TIFF).As the gear teeth are made more slender in an attempt to reducethe stiffness variation during the mesh cycle, therebypotentially reducing the noise, the risk of TIFF is increased.The phenomenon of TIFF is explored in detail (paper III-VI)through fractographic analysis, numerical crack initiationanalysis using FEM, determination of residual stress by meansof neutron diffraction measurements, testing for determiningmaterial fatigue properties, fracture mechanical FE-analysis,sensitivity analysis and the development of an engineeringdesign method. The main findings of the analysis of TIFF arethat TIFF cracks initiate in the tooth interior, TIFF occursmainly in case hardened idlers, the fracture surface has acharacteristic plateau at approximately the mid-height of thetooth and the risk of TIFF is more pronounced in slender gearteeth. Along with the more optimised gear design, there is atendency for the gear to be less robust. Low robustness, i.e.,great variation in performance of the product, implies a highincidence of rejects, malfunction and/or bad-will, all of whichmay have a negative effect on company earnings. As the use ofoptimisation decreases the safety margins, greater attentionhas to be paid to guaranteeing the products' robustness.Moreover, in order to be cost-effective, the qualities of thegear must be verified early in the design process, implying anextended use of simulations. In this thesis, two robustnessanalyses are presented in which the analysing tool issimulation. The first one considers robust tooth root bendingfatigue strength as the gear is exposed to mounting errors, thesecond one considers robust noise characteristics of a gearexposed to manufacturing errors, varying torque and wear. Bothof these case studies address the problem of robustness ofgears and demonstrate how it can be estimated by use ofsimulations. The main result from the former robustnessanalysis is that wide gears are more sensitive to mountingerrors, while the latter analysis showed that to achieve robustnoise characteristics of a gear it should have large helixangles, and some profile- and lead crowning should beintroduced. The transverse contact ratio is a trade-off factorin the sense that both low average noise levels and low scatterin noise due to perturbations cannot be achieved. <b>Keywords</b>: robust design, Taguchi method, gear, idler,simulations, Finite Element Method, Tooth Interior FatigueFracture, TIFF
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Feature Extraction for Automatic Speech Recognition in Noisy Acoustic Environments / Parameteruttrekning for automatisk talegjenkjenning i støyende omgivelserGajic, Bojana January 2002 (has links)
This thesis presents a study of alternative speech feature extraction methods aimed at increasing robustness of automatic speech recognition (ASR) against additive background noise. Spectral peak positions of speech signals remain practically unchanged in presence of additive background noise. Thus, it was expected that emphasizing spectral peak positions in speech feature extraction would result in improved noise robustness of ASR systems. If frequency subbands are properly chosen, dominant subband frequencies can serve as reasonable estimates of spectral peak positions. Thus, different methods for incorporating dominant subband frequencies into speech feature vectors were investigated in this study. To begin with, two earlier proposed feature extraction methods that utilize dominant subband frequency information were examined. The first one uses zero-crossing statistics of the subband signals to estimate dominant subband frequencies, while the second one uses subband spectral centroids. The methods were compared with the standard MFCC feature extraction method on two different recognition tasks in various background conditions. The first method was shown to improve ASR performance on both recognition tasks at sufficiently high noise levels. The improvement was, however, smaller on the more complex recognition task. The second method, on the other hand, led to some reduction in ASR performance in all testing conditions. Next, a new method for incorporating subband spectral centroids into speech feature vectors was proposed, and was shown to be considerably more robust than the standard MFCC method on both ASR tasks. The main difference between the proposed method and the zero-crossing based method is in the way they utilize dominant subband frequency information. It was shown that the performance improvement due to the use of dominant subband frequency information was considerably larger for the proposed method than for the ZCPA method, especially on the more complex recognition task. Finally, the computational complexity of the proposed method is two orders of magnitude lower than that of the zero-crossing based method, and of the same order of magnitude as the standard MFCC method.
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Robustness aspects of Model Predictive ControlMegías Jiménez, David 07 April 2000 (has links)
Model, Model-based or Receding-horizon Predictive Control (MPC or RHPC) is a successful and mature control strategy which has gained the widespread acceptance of both academia and industry. The basis of these control laws, which have been reported to handle quite complex dynamics, is to perform predictions of the system to be controlled by means of a model. A control profile is then computed to minimise some cost function defined in terms of the predictions and the hypothesised controls.
It was soon realised that the first few predictive controllers failed to fulfil essential properties, such as the stability of the nominal closed-loop system. In addition, it was noticed that the discrepancies between the model and the true process, referred to as system uncertainty, can seriously affect the achieved performance. The robustness problem should, thus, be addressed. In this thesis, the problems of nominal stability and robustness are reviewed and investigated. In particular, the accomplishment of constraint specifications in the presence of various sources of uncertainty is a major objective of the methods developed throughout this PhD research.
First of all, controllers which guarantee nominal stability, such as the CRHPC and the GPC∞, are highlighted and formulated, and 1-norm counterparts are obtained. The robustness of these strategies in the unconstrained case has been analysed, and it has been concluded that the infinite horizon approach often leads to more convenient performance and robustness results for typical choices of the tuning knobs. Then the constrained case has been undertaken, and min-max controllers based on the global uncertainty approach have been formulated for both 1-norm and 2-norm formulations. For these methods, a band updating algorithm has been suggested to modify the assumed uncertainty bounds on-line. Although both formulations provide similar results, which overcome the classical approach to robustness when constraints are specified, the 1-norm controllers are computationally more efficient, since the optimal control move sequence can be computed with a standard LP problem.
Finally, a refinement of the min-max approach which includes the notion that feedback is present in the receding-horizon implementation of predictive controllers, termed as feedback min-max MPC, is shown to overcome some of the drawbacks of the standard min-max approach. / El Control Predictiu Basat en Models (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) és una estratègia de control madura i de gran èxit, que ha assolit l'acceptació de les comunitats acadèmica i industrial. La base d'aquest tipus de lleis de control, la capacitat de les quals per treballar amb dinàmiques complexes s'ha documentat en la literatura, és realitzar prediccions del sistema a controlar mitjançant un model. A partir de les prediccions, es calcula un perfil de controls per tal de minimitzar un funció de cost definida en termes de les prediccions i dels controls futurs.
Després de les primeres formulacions es van detectar las carències dels controladors predictius per satisfer determinades propietats essencials, com garantir l'estabilitat del sistema nominal en llaç tancat. A més, era ben conegut que les discrepàncies existents entre el model i el procés, denominades incertesa del sistema, podien afectar severament el rendiment. Calia, per tant, abordar el problema de la robustesa. En aquesta tesi es revisa i s'investiguen els problemes de l'estabilitat nominal i la robustesa. En particular, la satisfacció de les especificacions de restriccions en presència de diverses fonts d'incertesa és un objectiu principal dels mètodes desenvolupats al llarg d'aquesta recerca.
En primer lloc, s'ha fet una revisió dels controladors que asseguren estabilitat nominal, com el CRHPC i el GPC∞, i s'han suggerit controladors equivalents en norma 1. A continuació, s'ha estudiat la robustesa d'aquestes estratègies en absència de restriccions i s'ha conclòs que l'aproximació d'horitzons infinits condueix, habitualment, a millors resultats pel que fa al rendiment i a la robustesa per a valors típics dels paràmetres de sintonia. Seguidament s'ha tractat el problema de la robustesa en presència de restriccions i s'han formulat controladors min-max, tant en norma 1 com en norma 2, basats en el concepte d'incertesa global. Per a aquests mètodes, s'ha proposat un algorisme d'actualització de les bandes que permet modificar les fites de la incertesa en línia. Tot i que ambdues formulacions proporcionen resultats semblants, que superen els mètodes clàssics de robustesa quan s'especifiquen restriccions, els controladors en norma 1 són més eficients des del punt de vista del temps de còmput, atès que el problema d'optimització es pot resoldre fent servir programació lineal.
Finalment, s'han proposat nous controladors basats en un últim avanç de l'aproximació min-max que incorpora la noció que la realimentació és present en la implementació d'horitzó mòbil dels controladors predictius. Aquestes tècniques, anomenades feedback min-max MPC, permeten de superar alguns dels desavantatges de la formulació min-max estàndard. / El Control Predictivo Basado en Modelos (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) es una estrategia de control madura y de gran éxito, que ha conseguido la aceptación de las comunidades académica e industrial. La base de este tipo de leyes de control, cuya capacidad para manejar dinámicas complejas se ha documentado en la literatura, es realizar predicciones del sistema a controlar por medio de un modelo. A partir de las predicciones, se calcula un perfil de controles para minimizar una función de coste definida en términos de las predicciones y de los controles futuros.
Tras las primeras formulaciones se detectaron las carencias de los controladores predictivos para satisfacer determinadas propiedades esenciales, como garantizar la estabilidad del sistema nominal en lazo cerrado. Además, era bien sabido que las discrepancias existentes entre el modelo y el proceso, denominadas incertidumbre del sistema, podían afectar severamente al rendimiento. El problema de la robustez debía, por tanto, ser abordado. En esta tesis se revisan e investigan los problemas de estabilidad nominal y robustez. En particular, la satisfacción de las especificaciones de restricciones en presencia de varias fuentes de incertidumbre es un objetivo principal de los métodos desarrollados a lo largo de esta investigación.
En primer lugar, se han revisado los controladores que aseguran estabilidad nominal, como el CRHPC y el GPC∞ y se han propuesto controladores equivalentes en norma 1. A continuación se ha estudiado la robustez de estas estrategias en ausencia de restricciones y se ha concluido que la aproximación de horizontes infinitos conduce, habitualmente, a mejores resultados en lo referente al rendimiento y a la robustez para valores típicos de los parámetros de sintonía. Seguidamente, se ha tratado el problema de la robustez en presencia de restricciones, y se han formulado controladores min-max, tanto en norma 1como en norma 2, basados en el concepto de incertidumbre global. Para estos métodos, se ha sugerido un algoritmo de actualización de las bandas que permite modificar las cotas de la incertidumbre en línea. Aunque ambas formulaciones proporcionan resultados similares, que superan al enfoque clásico de la robustez cuando se especifican restricciones, los controladores en norma 1 son más eficientes desde el punto de vista de tiempo de cómputo, puesto que el problema de optimización se puede resolver usando programación lineal.
Finalmente, se han propuesto otros controladores basados en un último avance de la aproximación min-max que incorpora la noción de que la realimentación está presente en la implementación de horizonte móvil de los controladores predictivos. Estas técnicas, denominadas feedback min-max MPC, permiten superar algunas de las desventajas de la formulación min-max estándar.
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Proactive management of uncertainty to improve scheduling robustness in proces industriesBonfill Teixidor, Anna 18 December 2006 (has links)
Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés. / Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment.
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The Role of Dominant Cause in Variation Reduction through Robust Parameter DesignAsilahijani, Hossein 24 April 2008 (has links)
Reducing variation in key product features is a very important goal in process improvement. Finding and trying to control the cause(s) of variation is one way to reduce variability, but is not cost effective or even possible in some situations. In such cases, Robust Parameter Design (RPD) is an alternative.
The goal in RPD is to reduce variation by reducing the sensitivity of the process to the sources of variation, rather than controlling these sources directly. That is, the goal is to find levels of the control inputs that minimize the output variation imposed on the process via the noise variables (causes). In the literature, a variety of experimental plans have been proposed for RPD, including Robustness, Desensitization and Taguchi’s method. In this thesis, the efficiency of the alternative plans is compared in the situation where the most important source of variation, called the “Dominant Cause”, is known. It is shown that desensitization is the most appropriate approach for applying the RPD method to an existing process.
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The Role of Dominant Cause in Variation Reduction through Robust Parameter DesignAsilahijani, Hossein 24 April 2008 (has links)
Reducing variation in key product features is a very important goal in process improvement. Finding and trying to control the cause(s) of variation is one way to reduce variability, but is not cost effective or even possible in some situations. In such cases, Robust Parameter Design (RPD) is an alternative.
The goal in RPD is to reduce variation by reducing the sensitivity of the process to the sources of variation, rather than controlling these sources directly. That is, the goal is to find levels of the control inputs that minimize the output variation imposed on the process via the noise variables (causes). In the literature, a variety of experimental plans have been proposed for RPD, including Robustness, Desensitization and Taguchi’s method. In this thesis, the efficiency of the alternative plans is compared in the situation where the most important source of variation, called the “Dominant Cause”, is known. It is shown that desensitization is the most appropriate approach for applying the RPD method to an existing process.
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