Die vollständige Ausnutzung des Leichtbaupotentials bei der Dimensionierung von mehrschichtigen endlosfaserverstärkten Strukturbauteilen erfordert die Bereitstellung von geeigneten Optimierungswerkzeugen, da bei der Auslegung eine große Anzahl von Entwurfsvariablen zu berücksichtigen sind. In dieser Arbeit werden Optimierungsalgorithmen und -strategien zur Lösung wissenschaftlicher Fragestellungen für industrielle Anwendungen bei der Konstruktion von entsprechenden Faserkunststoffverbunden entwickelt und bewertet. Um das breite Anwendungsspektrum aufzuzeigen, werden drei unterschiedliche repräsentative Problemstellungen bearbeitet. Dabei wird für Mehrschichtverbunde die Festigkeitsoptimierung hinsichtlich eines bruchtypbezogenen Versagenskriteriums vorgenommen, ein Dämpfungsmodell zur Materialcharakterisierung entworfen sowie eine bivalente Optimierungsstrategie zur Auslegung von gewickelten Hochdruckbehältern erstellt. Die Grundlage der entwickelten Methoden bilden dabei jeweils stochastische naturanaloge Optimierungsheuristiken, da die betrachteten Aufgabenstellungen nicht konvex sind und derartige Verfahren flexibel eingesetzt werden können. / The full utilization of the light weight potential in the dimensioning of multilayer fiber reinforced composites requires suitable optimization tools, since a large number of design variables has to be taken into account. In this work, optimization algorithms and strategies for the solution of scientific questions for industrial applications are developed and evaluated in the design of corresponding fiber-plastic composites. In order to show the wide range of applications, three different representative topics have been chosen. It will carry out a strength optimization for multilayer composites with regard to a type-related failure criterion, devolop a damping model for material characterization and established a bivalent optimization strategy for the design of wound high-pressure vessels. The developed methods are based on stochastic natural-analog optimization heuristics, since the considered tasks are not convex and such methods can be used in a very flexible manner.

Cette thèse a pour objectif la conception d’une architecture de contrôle bio-inspirée permettant à un robot autonome de naviguer sur de grandes distances. Le modèle développé permet également d’améliorer la compréhension des mécanismes biologiques impliqués. De précédents travaux ont montré qu’un modèle de cellules de lieu, enregistrées chez le rat, permettait à un robot mobile d’apprendre des comportements de navigation robustes, tels qu’une ronde ou un retour au nid, à partir d’associations entre lieu et action. La reconnaissance d’un lieu ne reposait alors que sur des informations visuelles. L’ambiguïté de certaines situations (e.g. un long couloir) ne permettait pas de naviguer dans de grands environnements. L’ajout d’autres modalités constitue une solution efficace pour augmenter la robustesse dans des environnements complexes. Cette solution nous a permis d’identifier les briques minimales nécessaires à la fusion d’informations multimodales, d’abord par le biais d’un conditionnement simple entre 2 modalités sensorielles, puis par la formalisation d’un modèle, plus générique, de prédictions inter-modales. C’est un mécanisme bas niveau qui permet de générer une cohérence perceptive : l’ensemble des modalités sensorielles s’entraident pour ne renvoyer qu’une perception claire et cohérente aux mécanismes décisionnels de plus haut niveau. Les modalités les plus corrélées sont ainsi capables de combler les informations manquantes d’une modalité défaillante (cas pathologique). Ce modèle implique la mise en place d’un système de prédiction et donc une capacité à détecter de la nouveauté dans ses perceptions. Ainsi, le modèle est également capable de détecter une situation inattendue ou anormale et possède donc une capacité d’auto-évaluation : l’évaluation de ses propres perceptions. Nous nous sommes ensuite mis à la recherche des propriétés fondamentales à tout système d'auto-évaluation.La première propriété essentielle a été de constater qu’évaluer un comportement sensorimoteur revient à reconnaître une dynamique entre sensation et action, plutôt que la simple reconnaissance d’une forme sensorielle. La première brique encapsule donc un modèle interne minimaliste des interactions du robot avec son environnement, qui est la base sur laquelle le système fera des prédictions.La seconde propriété essentielle est la capacité à extraire l’information pertinente par le biais de calculs statistiques. Il est nécessaire que le robot apprenne à capturer les invariants statistiques en supprimant l’information incohérente. Nous avons donc montré qu’il était possible d’estimer une densité de probabilité par le biais d’un simple conditionnement. Cet apprentissage permet de réaliser l’équivalent d’une inférence bayésienne. Le système estime la probabilité de reconnaître un comportement à partir de la reconnaissance d’informations statistiques apprises. C’est donc par la mise en cascade de simples conditionnements que le système peut apprendre à estimer les moments statistiques d’une dynamique (moyenne, variance, asymétrie, etc...). La non-reconnaissance de cette dynamique lui permet de détecter qu’une situation est anormale.Mais détecter un comportement inhabituel ne nous renseigne pas pour autant sur son inefficacité. Le système doit également surveiller l’évolution de cette anomalie dans le temps pour pouvoir juger de la pertinence du comportement. Nous montrons comment un contrôleur émotionnel peut faire usage de cette détection de nouveauté pour réguler le comportement et ainsi permettre au robot d’utiliser la stratégie la plus adaptée à la situation rencontrée. Pour finir, nous avons mis en place une procédure de frustration permettant au robot de lancer un appel à l’aide lorsqu’il détecte qu’il se retrouve dans une impasse. Ce réseau de neurones permet au robot d’identifier les situations qu’il ne maîtrise pas dans le but d’affiner son apprentissage, à l’instar de certains processus développementaux. / The goal of this thesis is to build a bio-inspired architecture allowing a robot to autonomouslynavigate over large distances. In a cognitive science point of view, the model also aim at improv-ing the understanding of the underlying biological mechanisms. Previous works showed thata computational model of hippocampal place cells, based on neurobiological studies made onrodent, allows a robot to learn robust navigation behaviors. The robot can learn a round or ahoming behavior from a few associations between places and actions. The learning and recog-nition of a place were only defined by visual information and shows limitations for navigatinglarge environments.Adding other sensorial modalities is an effective solution for improving the robustness of placesrecognition in complex environments. This solution led us to the elementary blocks requiredwhen trying to perform multimodal information merging. Such merging has been done, first,by a simple conditioning between 2 modalities and next improved by a more generic model ofinter-modal prediction. In this model, each modality learns to predict the others in usual situa-tions, in order to be able to detect abnormal situations and to compensate missing informationof the others. Such a low level mechanism allows to keep a coherent perception even if onemodality is wrong. Moreover, the model can detect unexpected situations and thus exhibit someself-assessment capabilities: the assessment of its own perception. Following this model of self-assessment, we focus on the fundamental properties of a system for evaluating its behaviors.The first fundamental property that pops out is the statement that evaluating a behavior is anability to recognize a dynamics between sensations and actions, rather than recognizing a sim-ple sensorial pattern. A first step was thus to take into account the sensation/action couplingand build an internal minimalist model of the interaction between the agent and its environment.Such of model defines the basis on which the system will build predictions and expectations.The second fundamental property of self-assessment is the ability to extract relevant informa-tion by the use of statistical processes to perform predictions. We show how a neural networkcan estimate probability density functions through a simple conditioning rule. This probabilis-tic learning allows to achieve bayesian inferences since the system estimates the probability ofobserving a particular behavior from statistical information it recognizes about this behavior.The robot estimates the different statistical momentums (mean, variance, skewness, etc...) of abehavior dynamics by cascading few simple conditioning. Then, the non-recognition of such adynamics is interpreted as an abnormal behavior.But detecting an abnormal behavior is not sufficient to conclude to its inefficiency. The systemmust also monitor the temporal evolution of such an abnormality to judge the relevance of thebehavior. We show how an emotional meta-controller can use this novelty detection to regu-late behaviors and so select the best appropriate strategy in a given context. Finally, we showhow a simple frustration mechanism allows the robot to call for help when it detects potentialdeadlocks. Such a mechanism highlights situations where a skills improvement is possible, soas some developmental processes.

Revisorns roll har på senare tid kommit att innefatta mer än att säkerställa de finansiella talensriktighet. En stor del av revisorns arbetsuppgifter handlar om att tillstå sina klienter med alltyp av rådgivning. En följd av revisorns utökade arbetsuppgifter är att det ställer krav pårevisorn att inneha kunskap om en rad olika ämnen. Denna utveckling har kommit attinnebära att allmänheten anses ha höga förväntningar på revisorn i dess yrkesroll.Tidigare forskning visar att bedrägerier inom organisationer har ökat på senare tid och att dethar kommit att bli ett ökat problem i samhället. I och med indikationer på att bedrägerier iorganisationer har ökat anses allmänheten också ha förväntningar på att revisorn skallupptäcka bedrägerier när de utför revision. Revisorerna själva anser däremot inte att det är ettsyfte med revisionen att upptäcka eventuella bedrägerier. Forskare menar dock attallmänhetens förväntningar är så pass höga att revisorerna måste ta hänsyn till detta krav.Tidigare forskning samt de tillfrågade revisorerna bekräftar att bedrägerier i företag är mycketproblematiska att upptäcka med hänsyn till det faktum att de inte enskilt har någon storpåverkan på de finansiella räkenskaperna för ett bolag.Meningsskiljaktigheter råder mellan revisorerna och användarna kring revisorns roll och bådetidigare forskning samt de revisorer jag har intervjuat i denna studie vittnar om att det råderett förväntningsgap mellan de olika parterna. En vanlig missuppfattning menar revisorerna imin undersökning är att allmänheten tror att revisorerna reviderar och kontrollerar pådetaljnivå. I både tidigare forskning samt empiriskt underlag finner jag uppfattningen attkunskapen kring revisorns roll måste öka. Tidigare forskning och de tillfrågaderespondenterna är överens om att information är huvudnyckeln till att minska deförväntningsgap som råder och som från revisorernas sida uppfattas som mycket olyckligt.Studien har gjorts i syfte att med kvalitativa semistrukturerade intervjuer med revisorerklargöra hur revisorerna själva ser på sin roll att upptäcka bedrägerier i organisationer.Studien består sammanlagt av åtta respondenter representerade på fyra revisionsbyråer varavtre stycken är stora revisionsbolag och en mindre revisionsbyrå. Resultatet av denna studievisar att revisorerna är överens om att upptäcka bedrägerier inte är ett huvudmål medrevisionen utan utgör endast en del. Revisorerna i studien menar att det är deras uppgift attgranska utifrån väsentlighet och risk. Samtliga respondenter menar att det är ledningensansvar att upptäcka bedrägerier men framförallt betonar de vikten av att förebyggabedrägerier med hjälpa av goda interna kontroller. / Program: Civilekonomprogrammet

Les ateliers de traitement de surface automatisés, qui utilisent des robots de manutention commandés par ordinateur pour le transport de la pièce, ont été largement mis en place dans différents types d'entreprises industrielles, en raison de ses nombreux avantages par rapport à un mode de production manuel, tels que : une plus grande productivité, une meilleure qualité des produits, et l’impact sur les rythmes de travail. Notre recherche porte sur trois types de problèmes d'ordonnancement associés à ces systèmes, appelés Hoist Scheduling Problems, caractérisés par des contraintes de fenêtres de temps de traitement: (I) un problème à une seule ressource de transport où l’objectif est de minimiser le temps de cycle; (II) un problème bi-objectif avec une seule ressource de transport où il faut minimiser le temps de cycle et la consommation de ressources de traitement (et par conséquent le coût de production); et (III) un problème d'ordonnancement cyclique mono-objectif mais multi-robots.En raison de la NP-complétude des problèmes étudiés et de nombreux avantages de les outils de type quantum-inspired evolutionary algorithm (QEA), nous proposons d'abord un QEA hybride comprenant un mécanisme de décodage amélioré et une procédure réparation dédiée pour trouver le meilleur temps de cycle pour le premier problème. Après cela, afin d'améliorer à la fois la performance économique et environnementale qui constituent deux des trois piliers de la stratégie de développement durable de nos jours déployée dans de nombreuses industries, nous formulons un modèle mathématique bi-objectif pour le deuxième problème en utilisant la méthode de l'intervalle interdit. Ensuite, nous proposons un QEA bi-objectif couplé avec une procédure de recherche locale pour minimiser simultanément le temps de cycle et les coûts de production, en générant un ensemble de solutions Pareto-optimales pour ce problème. Quant au troisième problème, nous constatons que la plupart des approches utilisées dans les recherches actuelles, telles que la programmation entière mixte (MIP), peuvent conduire à l’obtention d’une solution non optimale en raison de la prise en compte courante d’une hypothèse limitant l’exploration de l’espace de recherche et relative aux mouvements en charge des robots. Par conséquent, nous proposons une approche de MIP améliorée qui peut garantir l'optimalité des solutions obtenues pour ce problème, en relaxant l'hypothèse mentionnée ci-dessus.Pour chaque problème, une étude expérimentale a été menée sur des cas industriels ainsi que sur des instances générées aléatoirement. Les résultats obtenus montrent que l’efficacité des algorithmes d'ordonnancement proposés, ce qui justifie les choix que nous avons faits. / Automated treatment surface facilities, which employ computer-controlled hoists for part transportation, have been extensively established in various kinds of industrial companies, because of its numerous advantages over manual system, such as higher productivity, better product quality, and reduced labor intensity. Our research investigates three typical hoist scheduling problems with processing time windows in treatment surface facilities, which are: (I) cyclic single-hoist scheduling problem to minimize the cycle time; (II) cyclic single-hoist scheduling problem to minimize the cycle time and processing resource consumption (and consequently production cost); and (III) cyclic multi-hoist scheduling problem to minimize the cycle time.Due to the NP-completeness of the studied problems and numerous advantages of quantum-inspired evolutionary algorithm (QEA), we first propose a hybrid QEA with improved decoding mechanism and repairing procedure to find the best cycle time for the first problem. After that, to enhance with both the economic and environmental performance, which constitute two of the three pillars of the sustainable strategy nowadays deployed in many industries, we formulate a bi-objective mathematical model for the second problem by using the method of prohibited interval. Then we propose a bi-objective QEA with local search procedure to simultaneously minimize the cycle time and production cost, and we find a set of Pareto-optimal solutions for this problem. As for the third problem, we find that most existing approaches, such as mixed integer programming (MIP) approach, may identify a non-optimal solution to be an optimal one due to an assumption related to the loaded hoist moves which is made in many existing researches. Consequently, we propose an improved MIP approach for this problem by relaxing the above-mentioned assumption. Our approach can guarantee the optimality of its obtained solutions.For each problem, experimental study on industrial instances and random instances has been conducted. Computational results demonstrate that the proposed scheduling algorithms are effective and justify the choices we made.

The conventional Mini and Large scale Unmanned Aerial Vehicle systems span anywhere from approximately 12 inches to 12 feet; endowing them with larger propulsion systems, batteries/fuel-tanks, which in turn provide ample power reserves for long-endurance flights, powerful actuators, on-board avionics, wireless telemetry etc. The limitations thus imposed become apparent when shifting to Micro Aerial Vehicles (MAVs) and trying to equip them with equal or near-equal flight endurance, processing, sensing and communication capabilities, as their larger scale cousins. The conventional MAV as outlined by The Defense Advanced Research Projects Agency (DARPA) is a vehicle that can have a maximum dimension of 6 inches and weighs no more than 100 grams. Under these tight constraints, the footprint, weight and power reserves available to on-board avionics and actuators is drastically reduced; the flight time and payload capability of MAVs take a massive plummet in keeping with these stringent size constraints. However, the demand for micro flying robots is increasing rapidly. The applications that have emerged over the years for MAVs include search&rescue operations for trapped victims in natural disaster succumbed urban areas; search&reconnaissance in biological, radiation, natural disaster/hazard succumbed/prone areas; patrolling&securing home/office/building premises/urban areas. VTOL capable rotary and fixed wing flying vehicles do not scale down to micro sized levels, owing to the severe loss in aerodynamic efficiency associated with low Reynolds number physics on conventional airfoils; whereas, present state of the art in flapping wing designs lack in one or more of the minimum qualities required from an MAV: Appreciable flight time, appreciable payload capacity for on-board sensors/telemetry and 6DoF hovering/VTOL performance. This PhD. work is directed towards overcoming these limitations. Firstly, this PhD thesis presents the advent of a novel Quad-Wing MAV configuration (called the QV). The Four-Wing configuration is capable of performing all 6DoF flight maneuvers including VTOL. The thesis presents the design, conception, simulation study and finally hardware design/development of the MAV. Secondly, this PhD thesis proves and demonstrates significant improvement in on-board Energy-Harvesting resulting in increased flight times and payload capacities of the order of even 200%-400% and more. Thirdly, this PhD thesis defines a new actuation principle called, Fixed Frequency, Variable Amplitude (FiFVA). It is demonstrated that by the use of passive elastic members on wing joints, a further significant increase in energy efficiency and consequently reduction in input power requirements is observed. An actuation efficiency increase of over 100% in many cases is possible. The natural evolution of actuation development led to invention of two novel actuation systems to illustrate the FiFVA actuation principle and consequently show energy savings and flapping efficiency improvement. Lastly, but not in the least, the PhD thesis presents supplementary work in the design, development of two novel Micro Architecture and Control (MARC) avionics platforms (autopilots) for the application of demonstrating flight control and communication capability on-board the Four-Wing Flapping prototype. The design of a novel passive feathering mechanism aimed to improve lift/thrust performance of flapping motion is also presented.

La stratégie de l'équipe biorobotique est de s'inspirer de découvertes faites en biologie chez l'insecte ailé dont la vision est adaptée à la navigation autonome dans un environnement 3D inconnu. Cette inspiration donne naissance la réalisation de capteurs visuels minimalistes permettant de rendre autonomes des robots volants, pour des tâches complexes telles que : le décollage et l'atterrissage automatiques, l'évitement d'obstacles et, dans le cas de cette thèse, le vol stationnaire.Cette thèse présente la mise en œuvre des capteurs visuels minimalistes bio-inspirés qui, grâce à des algorithmes de traitement que nous avons réalisés, sont capables de localiser la position d'objets visuels en tirant partie de propriétés souvent bannies en optique : un flou, obtenu par défocalisation, associé à un micro-mouvement rétinien actif. Nous montrons que la précision en localisation ainsi obtenue est considérablement améliorée par rapport à la résolution statique définie par l'échantillonnage spatial : ces capteurs optiques bio-inspirés sont donc dotés d'hyperacuité.Cette thèse présente aussi l'œil composé artificiel miniature CurvACE (de 2,2cm3 pour 1,75g) doté d'une vision panoramique (180x60°). Cette thèse décrit la caractérisation et la mise en œuvre du capteur CurvACE sur le robot HyperRob. En fusionnant les mesures de position données par une quarantaine de pixels couvrant un grand champ visuel, l'œil CurvACE mesure sa position par rapport à un environnement visuel texturé complexe. Nous montrons aussi que le robot volant HyperRob, attaché au bout d'un bras, stabilise son roulis et sa position, dans le plan azimutal, grâce à son œil composé artificiel doté d'hyperacuité. / The biorobotics team from the Institute of Movement Sciences (Marseille, France) takes its inspiration from biological studies on flying insects which are able to navigate into unknown 3D environments with a high maneuverability. These studies led us to build minimalist optical sensors to make aerial robots autonomous for achieving complex tasks such as automatic landing and take-off, obstacle avoidance and very accurate hovering flight depicted in this doctoral thesis. This work presents several bio-inspired visual sensors implemented with different visual processing algorithms. All these sensors are able to locate visual objects (contrasting edges and bars) with unusual properties for optical sensing devices: a blur obtained by defocusing optics related with active retinal micro-movements to improve the sensor resolution. We showed that the resolution in locating contrasting objects can be improved up to 160 fold better than the static resolution defined by the pixel pitch, which means that these bio-inspired optical sensors are endowed with hyperacuity.The thesis presents a miniature artificial compound eye CurvACE (of 1.75g for 2.2cm3) with a panoramic field of view (180x60°). This thesis describes thoroughly the characterization and the implementation of the CurvACE sensor onboard an aerial robot named HyperRob. This artificial compound eye acts as a position sensing device able to measure its position relative to a complex textured scene by fusing the position measurements obtained by 40 pixels. The tethered flying robot HyperRob (a 150-g bi-rotor with a 23-cm wingspan) stabilizes its roll and its position thanks to its hyperacute artificial compound eye.

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches.

[pt] Através das técnicas utilizadas em Identificação de Sistemas é possível obter um modelo matemático para um sistema dinâmico somente a partir de dados medidos de suas entradas e saídas. Por possuírem comportamento naturalmente dinâmico e um procedimento de treinamento simples e rápido, o uso de redes neurais do tipo Echo State Networks (ESNs) é vantajoso nesta área. Entretanto, as ESNs possuem hiperparâmetros que devem ser ajustados para que obtenham um bom desempenho em uma dada tarefa, além do fato de que a inicialização aleatória de pesos da camada interna destas redes (reservatório) nem sempre ser a ideal em termos de desempenho. Por teoricamente conseguirem obter boas soluções com poucas avaliações, o AEIQ-R (Algoritmo Evolutivo com Inspiração Quântica e Representação Real) e a estratégia evolucionária com adaptação da matriz de covariâncias (CMA-ES) representam alternativas de algoritmos evolutivos que permitem lidar de maneira eficiente com a otimização de hiperparâmetros e/ou pesos desta rede. Sendo assim, este trabalho propõe um modelo neuroevolucionário que define automaticamente uma ESN para aplicações de Identificação de Sistemas. O modelo inicialmente foca na otimização dos hiperparâmetros da ESN utilizando o AEIQ-R ou o CMA-ES, e, num segundo momento, seleciona o reservatório mais adequado para esta rede, o que pode ser feito através de uma segunda otimização focada no ajuste de alguns pesos do reservatório ou por uma escolha simples baseando-se em redes com reservatórios aleatórios. O método proposto foi aplicado a 9 problemas benchmark da área de Identificação de Sistemas, apresentando bons resultados quando comparados com modelos tradicionais. / [en] Through System Identification techniques is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, the use of Echo State Networks, which are a kind of neural networks, for System Identification is advantageous. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, a random reservoir may not be ideal in terms of performance. Due to their theoretical ability of obtaining good solutions with few evaluations, the Real Coded Quantum-Inspired Evolutionary Algorithm (QIEA-R) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) represent efficient alternatives of evolutionary algorithms for optimizing ESN global parameters and/or weights. Thus, this work proposes a neuro-evolutionary method that automatically defines an ESN for System Identification problems. The method initially focuses in finding the best ESN global parameters by using the QIEA-R or the CMA-ES, then, in a second moment, in selecting its best reservoir, which can be done by a second optimization focused on some reservoir weights or by doing a simple choice based on networks with random reservoirs. The method was applied to 9 benchmark problems in System Identification, showing good results when compared to traditional methods.

Orientador: Percival Bueno de Araujo / Resumo: Este trabalho apresenta o Novel Bat Algorithm com uma nova técnica para realizar o ajuste coordenado dos parâmetros de controladores suplementares de amortecimento (Estabilizadores de Sistemas de Potência e do conjunto Generalized Unified Power Flow Controller – Power Oscillation Damping) em sistemas elétricos de potência multimáquinas. O objetivo principal é inserir amortecimento adicional aos modos oscilatórios de baixa frequência e, consequentemente, garantir a estabilidade do sistema elétrico frente a pequenas perturbações. Para representar o sistema elétrico de potência será utilizado o Modelo de Sensibilidade de Potência. Desse modo, todos os seus dispositivos e componentes foram modelados por injeções de potência. Análises estáticas e dinâmicas foram realizadas em dois sistemas teste, sendo: o Sistema Simétrico de Duas Áreas e o Sistema New England. A eficiência do dispositivo FACTS Generalized Unified Power Flow Controller atuando em conjunto com uma estrutura de controle baseada em controladores Proporcional – Integral foi criteriosamente avaliada para o controle de fluxos de potências ativa e reativa, para a melhoria do perfil de tensão do sistema elétrico e na redução das perdas no sistema de transmissão. O desempenho do Novel Bat Algorithm, no que concerne ao ajuste dos parâmetros dos controladores, foi comparado a outros quatro algoritmos bio-inspirados bastante difundidos na literatura: Particle Swarm Optimization, Bacterial Foragim Optimization, Bat Algorithm e... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work presents the Novel Bat Algorithm as a new technique for the to perform the coordinated tuning of the parameters of the supplementary damping controllers (Power Systems Stabilizers and Generalized Unified Power Flow Controller - Power Oscillation Damping) in multi-machine electric power systems. The main objective is to insert damping to low-frequency oscillations and thus ensure the stability of the electrical system against minor disturbances. The Power Sensitivity Model is used to represent the system. Thus, all devices and their components are modeled by power injection. Static and dynamic analyzes were performed in the two systems: the two-areas symmetric, and the New England. The performance of the proposed methodology (Novel Bat Algorithm), for tuning of the parameters of the controllers was compared to four other algorithms, presented in the literature: The Particle Swarm Optimization method, Bacterial Foraging Optimization method, Bat Algorithm method and a Genetic Algorithm with elitism. The results demonstrated that the Novel Bat Algorithm was more effective than the other techniques presented, generating robust solutions when variations on the scenarios of loads were considered, and therefore accredited it as a tool in the analysis of the study of small-signal stability. / Doutor

Submitted by Ednei Luiz Miotto (edneimiotto@utfpr.edu.br) on 2018-11-05T12:58:43Z No. of bitstreams: 1 TESE EDNEI LUIZ MIOTTO.pdf: 5057627 bytes, checksum: 74b7d6f2bd477e7e02941873ca291fa3 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-11-08T19:07:02Z (GMT) No. of bitstreams: 1 miotto_el_dr_ilha.pdf: 5057627 bytes, checksum: 74b7d6f2bd477e7e02941873ca291fa3 (MD5) / Made available in DSpace on 2018-11-08T19:07:02Z (GMT). No. of bitstreams: 1 miotto_el_dr_ilha.pdf: 5057627 bytes, checksum: 74b7d6f2bd477e7e02941873ca291fa3 (MD5) Previous issue date: 2018-10-18 / Este trabalho apresenta o Novel Bat Algorithm com uma nova técnica para realizar o ajuste coordenado dos parâmetros de controladores suplementares de amortecimento (Estabilizadores de Sistemas de Potência e do conjunto Generalized Unified Power Flow Controller – Power Oscillation Damping) em sistemas elétricos de potência multimáquinas. O objetivo principal é inserir amortecimento adicional aos modos oscilatórios de baixa frequência e, consequentemente, garantir a estabilidade do sistema elétrico frente a pequenas perturbações. Para representar o sistema elétrico de potência será utilizado o Modelo de Sensibilidade de Potência. Desse modo, todos os seus dispositivos e componentes foram modelados por injeções de potência. Análises estáticas e dinâmicas foram realizadas em dois sistemas teste, sendo: o Sistema Simétrico de Duas Áreas e o Sistema New England. A eficiência do dispositivo FACTS Generalized Unified Power Flow Controller atuando em conjunto com uma estrutura de controle baseada em controladores Proporcional – Integral foi criteriosamente avaliada para o controle de fluxos de potências ativa e reativa, para a melhoria do perfil de tensão do sistema elétrico e na redução das perdas no sistema de transmissão. O desempenho do Novel Bat Algorithm, no que concerne ao ajuste dos parâmetros dos controladores, foi comparado a outros quatro algoritmos bio-inspirados bastante difundidos na literatura: Particle Swarm Optimization, Bacterial Foragim Optimization, Bat Algorithm e o Algoritmo Genético com Elitismo. Os resultados demonstraram que o Novel Bat Algorithm foi mais eficiente que as demais técnicas avaliadas, obtendo soluções com amortecimento satisfatório, mesmo quando variações nos cenários de carregamento do sistema são consideradas, sendo, portanto, credenciado como ferramenta promissora para a análise da estabilidade a pequenas perturbações em sistemas elétricos de potência multimáquinas. / This work presents the Novel Bat Algorithm as a new technique for the to perform the coordinated tuning of the parameters of the supplementary damping controllers (Power Systems Stabilizers and Generalized Unified Power Flow Controller - Power Oscillation Damping) in multi-machine electric power systems. The main objective is to insert damping to low-frequency oscillations and thus ensure the stability of the electrical system against minor disturbances. The Power Sensitivity Model is used to represent the system. Thus, all devices and their components are modeled by power injection. Static and dynamic analyzes were performed in the two systems: the two-areas symmetric, and the New England. The performance of the proposed methodology (Novel Bat Algorithm), for tuning of the parameters of the controllers was compared to four other algorithms, presented in the literature: The Particle Swarm Optimization method, Bacterial Foraging Optimization method, Bat Algorithm method and a Genetic Algorithm with elitism. The results demonstrated that the Novel Bat Algorithm was more effective than the other techniques presented, generating robust solutions when variations on the scenarios of loads were considered, and therefore accredited it as a tool in the analysis of the study of small-signal stability.