Die Arbeit befasst sich mit der effizienten Simulation des funktionellen Verhaltens von Piezoverbundmodulen als Aktor oder Sensor zur Schwingungsbeeinflussung mechanischer Strukturen. Ausgehend von einem FE-Modell werden über den Ansatz energetischer Äquivalenz die effektiven elektro-mechanischen Materialparameter ermittelt. Zur Berücksichtigung im Inneren der Einheitszelle liegender Elektroden werden die elektrischen Randbedingungen der Homogenisierungslastfälle angepasst. Die Homogenisierungslastfälle werden auch genutzt, um Phasenkonzentrationen für die Beanspruchungen der Verbundkomponenten zu ermitteln. Diese Phasenkonzentrationen werden eingesetzt, um aus dem effektiven Gesamtmodell die Beanspruchungen der Komponenten zu extrahieren. Zur dynamischen Modellbildung wird die Zustandsraumbeschreibung verwendet. Die Überführung einer piezo-mechanischen FE-Diskretisierung in ein Zustandsraummodell gelingt mit der Betrachtung der mechanischen Freiheitsgrade als Zustandsvariablen. Zur Abbildung der elektrischen Impedanz im Zustandsraum muss die elektrische Kapazitätsmatrix als Durchgangsmatrix einbezogen werden. Die Reduktion des Zustandsraums basiert auf der modalen Superposition. Die modale Transformationsbasis wird um Moden ergänzt, die die Verformung bei statischer elektrischer Erregung charakterisieren. Die Zustandsraumbeschreibung wird sowohl für eine Potential- als auch für eine Ladungserregung ausgeführt. Das Zustandsraummodell wird unter Verwendung von Filtermatrizen um Ausgangssignale für die mechanischen und elektrischen Beanspruchungsgrößen erweitert. Dies gestattet eine Kopplung der Zustandsraummodelle mit den Beanspruchungsanalysen. Die Anwendung der Berechnungsmethode wird am Beispiel der im SFB/TRR PT-PIESA entwickelten Piezo-Metall-Module demonstriert, die durch direkte Integration von piezokeramischen Basiselementen in Blechstrukturen gekennzeichnet sind.:1 Einleitung 2 Grundlagen 3 Stand der Forschung 4 Beanspruchungsermittlung für piezo-mechanische Verbunde 5 Zustandsraumbeschreibung piezo-mechanischer Systeme 6 Gesamtmodell 7 Zusammenfassung / This thesis deals with the efficient simulation of the functional behaviour of piezo composite modules for applications as actuators or sensors to influence vibrations of machine structures. Based on a FE-discretisation the effective electro-mechanical material parameters of the piezo composite modules are determined with an ansatz of energetic equivalence. To consider electrodes which are located inside the representative volume element the electrical boundary conditions of the load cases for homogenisation are adapted. The load cases for homogenisation are also used to determine the phase concentrations (or fluctuation fields) of stress/strain and electric field/electric displacement field in the composite constituents. These phase concentrations are required to extract stress and strain of the composite components based on the overall model with effective material parameters. For dynamical modelling a state space representation is used. The transformation of a FE-discretisation of the piezo-mechanical system into a state space model is possible by choosing the mechanical degree of freedom as state variables. For consideration of the electrical impedance in the state space model the electrical stiffness respectively capacitance matrix has to incorporate as feedthrough matrix. The dynamical model reduction of the state space model is based on modal superposition. For the correct reproduction of the electrical impedance the modal transformation basis has to be amended by deformation modes which represent the deformation behaviour due to static electrical excitation at the electrodes. The state space representation is built for potential and charge excitation. The state space model is enhanced by filter matrices to incorporate output signals for stress/strain and also for electric field/electric displacement field. This allows the coupling of the state space models with the stress analyses. The application of the simulation method is demonstrated using the example of the piezo-metal-modules developed in the CRC/TR PT-PIESA (German: SFB/TRR PT-PIESA). These piezo-metal-modules are characterised by direct integration of piezoceramic base elements in sheet metal structures.:1 Einleitung 2 Grundlagen 3 Stand der Forschung 4 Beanspruchungsermittlung für piezo-mechanische Verbunde 5 Zustandsraumbeschreibung piezo-mechanischer Systeme 6 Gesamtmodell 7 Zusammenfassung

In power electronics, the switched converter plays a fundamental role in the efficient conversion and dynamical control of electrical energy. Due to the switching operation of these systems, overlaid disturbances come into existence in addition to the desired behavior of the variables, causing deviations in the current and voltages. From a control perspective, these disturbances are of no interest since they cannot be compensated. They can even alter the measurements given to the control system, affecting its behavior. Furthermore, during the control design, averaged models are often used, by which the switching operation is somehow disregarded. They consider instead the average behavior of the system variables. Thus, it is essential that the measuring setup provides a measurement of the average value to the control system. To accomplish this goal, there are in practice different approaches. For example, the disturbances originated by the switching operation can be either suppressed using an analog or digital filter, or the sampling of the variables can be carried out in a suitable manner, synchronous to the carrier of the modulation method. Unfortunately, the use of filters adds an extra phase shift or delay to the control loop, reducing its dynamical performance. Moreover, the synchronous sampling method provides a good approximation of the average value only if certain conditions are met, otherwise a distortion due to aliasing takes place. A method is developed in this work to predict, in every switching cycle, the average value of the system variables in a switched power converter. In this context, the work presents an alternative method to carry out the measurement of the average value, avoiding the principal drawbacks of the standard measuring methods. To achieve this, a suitable model of the converter is used, incorporating the modulation method and the type of analog-to-digital converter, either a conventional sample-and-hold or a sigma-delta converter. The measurement given by the analog-to-digital converter is used to predict the time behavior of the system variables during the present switching period and then to evaluate its average value, before the period is completed. The method allows to obtain simultaneously the average value of currents and voltages, to get rid of the delay introduced by filtering, and to avoid the drawback of sampling in the measurement, i.e. aliasing. In this work, an overview of the standard measuring methods for switched power converters is first presented. The problematics that arise from the sampling process are also discussed. Next, the theoretical grounds of the method are developed and the tools needed to implement it are derived. To illustrate its applicability, the method is used first in DC-DC converters, where the case of the buck converter is analyzed in detail. Similarly, the method is applied to a three-phase two-level voltage source converter. In both cases, simulation results and experimental verification are presented for different operational modes. The usage of the method in open and closed loop is discussed, and its effect in the system behavior is shown. The performance of the prediction method is contrasted with other standard measuring methods.

Traffic light control (TLC) with transit signal priority (TSP) is an effective way to deal with urban congestion and travel delay. The growing amount of available connected vehicle data offers opportunities for signal control with transit priority, but the conventional control algorithms fall short in fully exploiting those datasets. This paper proposes a novel approach for dynamic TLC with TSP at an urban intersection. We propose a deep reinforcement learning based framework JenaRL to deal with the complex real-world intersections. The optimisation focuses on TSP while balancing the delay of all vehicles. A two-layer state space is defined to capture the real-time traffic information, i.e. vehicle position, type and incoming lane. The discrete action space includes the optimal phase and phase duration based on the real-time traffic situation. An intersection in the inner city of Jena is constructed in an open-source microscopic traffic simulator SUMO. A time-varying traffic demand of motorised individual traffic (MIT), the current TLC controller of the city, as well as the original timetables of the public transport (PT) are implemented in simulation to construct a realistic traffic environment. The results of the simulation with the proposed framework indicate a significant enhancement in the performance of traffic light controller by reducing the delay of all vehicles, and especially minimising the loss time of PT.

This thesis presents the design and implementation of control strategies for building energy management systems (BEMS). The controllers considered include the multi PI-loop controllers, state-space designs, constrained input and output MIMO adaptive controllers, fuzzy logic solutions and genetic algorithm techniques. The control performances of the designs developed using the various methods based on aspects such as regulation errors squared, energy consumptions and the settling periods are investigated for different designs. The aim of the control strategy is to regulate the room temperature and the humidity to required comfort levels. In this study the building system under study is a 3 input/ 2 output system subject to external disturbances/effects. The three inputs are heating, cooling and humidification, and the 2 outputs are room air temperature and relative humidity. The external disturbances consist of climatic effects and other stochastic influences. The study is carried out within a simulation environment using the mathematical model of the test room at Loughborough University and the designed control solutions are verified through experimental trials using the full-scale BMS facility at the University of Bradford.

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In this thesis, we explore the potential of financing a fast charging system with energy storage by delivering ancillary services from the energy storage in an optimal way. Specifically, a system delivering frequency regulation services FCR-D Up and FCR-D Down in combination with energy arbitrage trading is considered. An optimization model is developed that could be implemented operationally and then used in a Monte-Carlo simulation to estimate the net present value of the system for four identified cases at three different energy market price scenarios. The main modeling approach is to formulate the system as a state-space model serving as the foundation for model predictive control, with the delay between decision and delivery of the frequency regulation services incorporated as a part of the system state. The optimization of the system is implemented using a dynamic programming approach with a time horizon of 48h, where the choice of admissible controls is optimized for computational efficiency. The result shows that the system could profitable under optimal operation, but it is heavily dependent on the size of the grid connection, future price levels for ancillary services, and the nature of fast-charging demand. As such, the business case and profitability should be evaluated with a specific use case in mind. The developed model showed relatively good computational efficiency for operational implementations with a run time for one iteration of the optimization problem of 15 seconds. The model could therefore be used as the foundation for future research within the specific field and for similar control problems considering delayed controls and stochastic demand. Several proposed improvements and suggested areas of future research are proposed. / I den här uppsatsen utforskar vi huruvida det är finansiellt lönsamt att leverera snabbladdning från ett energilager samtidigt som energilagret används för att leverera systemtjänster på ett optimalt sätt. Mer specifikt undersöks ett potentiellt system som levererar frekvensregleringstjänsterna FCR-D Up och FCR-D Down samt energiarbitragehandel. Vi utvecklar en optimeringsmodell som kan implementeras i ett fysiskt system och använder sedan modellen i en Monte-Carlo-simulering för att estimera nuvärdet av fyra olika systemkonfigurationer för tre olika prisscenarion. Den huvudsakliga modelleringsmetoden är att formulera systemet som en tillstånds-rum modell, som sedan används som grund för modellprediktiv styrning, där fördröjningen mellan beslut och leverans av frekvensregleringstjänster inkluderas som en del av systemets tillstånd. Optimeringen av systemet implementeras med en dynamisk programmeringsmetodik med en tidsram på 48 timmar, där valet av tillåtna kontroller optimeras för beräkningseffektivitet. Resultatet visar att systemet kan vara lönsamt under optimal drift, men det är starkt beroende av storleken på nätanslutningen, framtida prisnivåer för systemtjänster och typen av snabbladdningsbehovet. Därför bör lönsamheten utvärderas för varje specifikt fall. Den utvecklade modellen visade relativt god beräkningseffektivitet för praktiskt implementation med en körtid för en enskilt iteration på 15 sekunder. Modellen kan därför användas som grund för framtida forskning inom området och för liknande problem inom optimal styrteori som involverar fördröjda kontroller och stokastisk efterfrågan. Flera föreslagna förbättringar och områden för framtida forskning föreslås.

The field of classical planning deals with designing algorithms for generating plans or squences of actions that achieve specific goals. It involves representing a problem domain as a set of state variables, actions and goals, and then developing search algorithms that can explore the state of possible plans to find the one that satisfies the specified goal. Classical planning domains are often NP-hard, meaning that their worst-case complexity grows exponentially with the size of the problem. This means that as the number of state variables, actions and goals in the problem domain increases, the search space grows exponentially, making it very difficult to find a plan that satisfies the specified goal. This thesis is concerned with investigating these NP-hard domains, specifically by simplifying these domains into ones that have a polynomial solving time, creating a general policy of conditions and rules for which actions to take for the simplified domain, and then attempting to apply this policy onto the original domain. This creates a partial policy for the original domain, and the performance of this policy can be measured in order to judge its effectiveness. This can be explained as simplifying an intractable domain into a tractable one, creating a general policy for the tractable domain and then measuring its performance as a partial policy for the intractable domain.

Im Rahmen dieser Dissertation bemühe ich mich, den statistischen Ansatz der zeitkontinuierlichen dynamischen Modellierung, der die Rolle der Zeit explizit berücksichtigt, zu erweitern und praktisch anwendbar zu machen. Diese Dissertation ist so strukturiert, dass ich in Kapitel 1 die Natur dynamischer Modelle bespreche, verschiedene Ansätze zum Umgang mit mehreren Personen betrachte und ein zeitkontinuierliches dynamisches Modell mit Input-Effekten (wie Interventionen) und einem Gaußschen Messmodell detailliert darstelle. In Kapitel 2 beschreibe ich die Verwendung der Software ctsem für R, die als Teil dieser Dissertation entwickelt wurde und die Modellierung von Strukturgleichungen und Mixed-Effects über einen frequentistischen Schätzansatz realisiert. In Kapitel 3 stelle ich einen hierarchischen, komplett Random-Effects beinhaltenden Bayesschen Schätzansatz vor, unter dem sich Personen nicht nur in Interceptparametern, sondern in allen Charakteristika von Mess - und Prozessmodell unterscheiden können, wobei die Schätzung individueller Parameter trotzdem von den Daten aller Personen profitiert. Kapitel 4 beschreibt die Verwendung der Bayesschen Erweiterung der Software ctsem. In Kapitel 5} betrachte ich die Natur experimenteller Interventionen vor dem Hintergrund zeitkontinuierlicher dynamischer Modellierung und zeige Ansätze, die die Art und Weise adressieren, mit der Interventionen auf psychologische Prozesse über die Zeit wirken. Das berührt Fragen, wie: 'Nach welcher Zeit zeigt eine Intervention ihre maximale Wirkung', 'Wie ändert sich die Form des Effektes im Laufe der Zeit' und 'Für wen ist die Wirkung am stärksten oder dauert am längsten an'. Viele Bei-spiele, die sowohl frequentistische als auch bayessche Formen der Software ctsem verwenden, sind enthalten. Im letzten Kapitel fasse ich die Dissertation zusammen, zeige Limitationen der angebotenen Ansätze auf und stelle meine Gedanken zu möglichen zukünftigen Entwicklungen dar. / With this dissertation I endeavor to extend, and make practically applicable for psychology, the statistical approach of continuous time dynamic modelling, in which the role of time is made explicit. The structure of this dissertation is such that in Chapter 1, I discuss the nature of dynamic models, consider various approaches to handling multiple subjects, and detail a continuous time dynamic model with input effects (such as interventions) and a Gaussian measurement model. In Chapter 2, I describe the usage of the ctsem software for R developed as part of this dissertation, which provides a frequentist, mixed effects, structural equation modelling approach to estimation. Chapter 3 details a hierarchical Bayesian, fully random effects approach to estimation, allowing for subjects to differ not only in intercept parameters but in all characteristics of the measurement and dynamic models -- while still benefiting from other subjects data for parameter estimation. Chapter 4 describes the usage of the Bayesian extension to the ctsem software. In Chapter 5 I consider the nature of experimental interventions in the continuous time dynamic modelling framework, and show approaches to address questions regarding the way interventions influence psychological processes over time, with questions such as 'how long does a treatment take to reach maximum effect', `how does the shape of the effect change over time', and 'for whom is the effect strongest, or longest lasting'. Many examples using both frequentist and Bayesian forms of the ctsem software are given. For the final chapter I summarise the dissertation, consider limitations of the approaches offered, and provide some thoughts on possible future developments.

Ma thèse est composée de trois chapitres reliés à l'estimation des modèles espace-état et volatilité stochastique. Dans le première article, nous développons une procédure de lissage de l'état, avec efficacité computationnelle, dans un modèle espace-état linéaire et gaussien. Nous montrons comment exploiter la structure particulière des modèles espace-état pour tirer les états latents efficacement. Nous analysons l'efficacité computationnelle des méthodes basées sur le filtre de Kalman, l'algorithme facteur de Cholesky et notre nouvelle méthode utilisant le compte d'opérations et d'expériences de calcul. Nous montrons que pour de nombreux cas importants, notre méthode est plus efficace. Les gains sont particulièrement grands pour les cas où la dimension des variables observées est grande ou dans les cas où il faut faire des tirages répétés des états pour les mêmes valeurs de paramètres. Comme application, on considère un modèle multivarié de Poisson avec le temps des intensités variables, lequel est utilisé pour analyser le compte de données des transactions sur les marchés financières. Dans le deuxième chapitre, nous proposons une nouvelle technique pour analyser des modèles multivariés à volatilité stochastique. La méthode proposée est basée sur le tirage efficace de la volatilité de son densité conditionnelle sachant les paramètres et les données. Notre méthodologie s'applique aux modèles avec plusieurs types de dépendance dans la coupe transversale. Nous pouvons modeler des matrices de corrélation conditionnelles variant dans le temps en incorporant des facteurs dans l'équation de rendements, où les facteurs sont des processus de volatilité stochastique indépendants. Nous pouvons incorporer des copules pour permettre la dépendance conditionnelle des rendements sachant la volatilité, permettant avoir différent lois marginaux de Student avec des degrés de liberté spécifiques pour capturer l'hétérogénéité des rendements. On tire la volatilité comme un bloc dans la dimension du temps et un à la fois dans la dimension de la coupe transversale. Nous appliquons la méthode introduite par McCausland (2012) pour obtenir une bonne approximation de la distribution conditionnelle à posteriori de la volatilité d'un rendement sachant les volatilités d'autres rendements, les paramètres et les corrélations dynamiques. Le modèle est évalué en utilisant des données réelles pour dix taux de change. Nous rapportons des résultats pour des modèles univariés de volatilité stochastique et deux modèles multivariés. Dans le troisième chapitre, nous évaluons l'information contribuée par des variations de volatilite réalisée à l'évaluation et prévision de la volatilité quand des prix sont mesurés avec et sans erreur. Nous utilisons de modèles de volatilité stochastique. Nous considérons le point de vue d'un investisseur pour qui la volatilité est une variable latent inconnu et la volatilité réalisée est une quantité d'échantillon qui contient des informations sur lui. Nous employons des méthodes bayésiennes de Monte Carlo par chaîne de Markov pour estimer les modèles, qui permettent la formulation, non seulement des densités a posteriori de la volatilité, mais aussi les densités prédictives de la volatilité future. Nous comparons les prévisions de volatilité et les taux de succès des prévisions qui emploient et n'emploient pas l'information contenue dans la volatilité réalisée. Cette approche se distingue de celles existantes dans la littérature empirique en ce sens que ces dernières se limitent le plus souvent à documenter la capacité de la volatilité réalisée à se prévoir à elle-même. Nous présentons des applications empiriques en utilisant les rendements journaliers des indices et de taux de change. Les différents modèles concurrents sont appliqués à la seconde moitié de 2008, une période marquante dans la récente crise financière. / My thesis consists of three chapters related to the estimation of state space models and stochastic volatility models. In the first chapter we develop a computationally efficient procedure for state smoothing in Gaussian linear state space models. We show how to exploit the special structure of state-space models to draw latent states efficiently. We analyze the computational efficiency of Kalman-filter-based methods, the Cholesky Factor Algorithm, and our new method using counts of operations and computational experiments. We show that for many important cases, our method is most efficient. Gains are particularly large for cases where the dimension of observed variables is large or where one makes repeated draws of states for the same parameter values. We apply our method to a multivariate Poisson model with time-varying intensities, which we use to analyze financial market transaction count data. In the second chapter, we propose a new technique for the analysis of multivariate stochastic volatility models, based on efficient draws of volatility from its conditional posterior distribution. It applies to models with several kinds of cross-sectional dependence. Full VAR coefficient and covariance matrices give cross-sectional volatility dependence. Mean factor structure allows conditional correlations, given states, to vary in time. The conditional return distribution features Student's t marginals, with asset-specific degrees of freedom, and copulas describing cross-sectional dependence. We draw volatility as a block in the time dimension and one-at-a-time in the cross-section. Following McCausland(2012), we use close approximations of the conditional posterior distributions of volatility blocks as Metropolis-Hastings proposal distributions. We illustrate using daily return data for ten currencies. We report results for univariate stochastic volatility models and two multivariate models. In the third chapter, we evaluate the information contributed by (variations of) realized volatility to the estimation and forecasting of volatility when prices are measured with and without error using a stochastic volatility model. We consider the viewpoint of an investor for whom volatility is an unknown latent variable and realized volatility is a sample quantity which contains information about it. We use Bayesian Markov Chain Monte Carlo (MCMC) methods to estimate the models, which allow the formulation of the posterior densities of in-sample volatilities, and the predictive densities of future volatilities. We then compare the volatility forecasts and hit rates from predictions that use and do not use the information contained in realized volatility. This approach is in contrast with most of the empirical realized volatility literature which most often documents the ability of realized volatility to forecast itself. Our empirical applications use daily index returns and foreign exchange during the 2008-2009 financial crisis.