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Due to the escalating demand for traffic information and management, the significance of traffic state estimation, which involves the assessment of traffic conditions on road segments with limited measurement data, is increasing. Two primary estimation methods are model-driven and data-driven. The former uses traffic flow models, while the latter relies on extensive historical data to explore relationships between traffic states. Due to the uninterrupted nature of highway traffic flow, conventional model-driven approach is adopted in the study to estimate traffic information from sensing data. Data-driven approach is applied to enhance the estimation results. The project mainly focuses on comparing the estimation performance between the Particle Filter and the commonly used Extended Kalman Filter. These two methods are implemented in combination with two typical traffic flow models: Cell Transmission Model and METANET. Moreover, the project investigates the potential of using vehicle-to-everything (V2X) data in traffic state estimation, either alone or combined with traditional inductive loop detector (ILD) data. Being an emerging traffic data source, V2X communication has been recently installed and tested on the motorways near Stockholm. This study provides essential insights into how V2X data can benefit existing traffic information estimation and its performance. To evaluate the models mentioned above, the estimation algorithms and traffic flow models are implemented in a self-developed platform, which may be useful for further work. Results from simulation experiments show that Particle Filter can carry out traffic state estimation with comparable accuracy to Extended Kalman Filter. While standalone V2X speed data falls short, effective fusion methods are implemented to combine both data types, ultimately achieving the desired accuracy. These fusion methods encompass direct filtering, weighted averaging, and linear regression. Future investigations could broaden their scope to include new data sources, such as unmanned aerial vehicles (UAVs), and delve into advanced data fusion techniques, such as deep learning. / På grund av den ökande efterfrågan på trafikinformation och trafikhantering ökar betydelsen av trafiklägesuppskattning, vilket innebär bedömning av trafikförhållandena på vägsegment med begränsade mätningsdata. Två primära uppskattningsmetoder är modellbaserade och datadrivna metoder. Den förra använder trafikflödesmodeller, medan den senare förlitar sig på omfattande historiska data för att utforska samband mellan trafiklägen. På grund av det oavbrutna vägtrafikflödet antas en konventionell modellbaserad metod i studien för att uppskatta trafikinformation från sensordata. Den datadrivna metoden används för att förbättra estimatresultaten. Projektet fokuserar främst på att jämföra prestandan i uppskattningen mellan Partikelfiltret och den vanligtvis använda Extended Kalman Filter. Dessa två metoder implementeras i kombination med två typiska trafikflödesmodeller: Cell Transmission Model och METANET. Dessutom undersöker projektet möjligheterna att använda fordons-till-allt (V2X) data i trafiklägesuppskattning, antingen ensamt eller i kombination med data från traditionella induktiva slingdetektorer (ILD). Som en framväxande källa till trafikdata har V2X-kommunikation nyligen installerats och testats på motorvägarna nära Stockholm. Denna studie ger väsentlig inblick i hur V2X-data kan gynna befintlig uppskattning av trafikinformation och dess prestanda. För att utvärdera ovan nämnda modeller implementeras uppskattningsalgoritmerna och trafikflödesmodellerna i en självutvecklad plattform, vilket kan vara användbart för framtida arbete. Resultaten från simuleringsexperiment visar att Partikelfiltret kan utföra trafiklägesuppskattning med jämförbar noggrannhet jämfört med Extended Kalman Filter. Medan fristående V2X-hastighetsdata inte når hela vägen fram implementeras effektiva sammanslagningsmetoder för att kombinera båda datatyperna och slutligen uppnå önskad noggrannhet. Dessa sammanslagningsmetoder omfattar direkt filtrering, viktad medelvärdesbildning och linjär regression. Framtida undersökningar kan utvidga deras omfattning för att inkludera nya datakällor, såsom obemannade flygfordon (UAV:er), och utforska avancerade tekniker för datafusion, såsom djupinlärning.

The (Battery management system)BMS has the task of ensuring that for the individual bat-tery cell parameters such as the allowed operating voltage window or the allowable temperature range are not violated. Since the battery itself is a highly distinct nonlinear electrochemical de-vice it is hard to detect its internal characteristics directly. The requirement of predicting battery packs’ present operating condition will become one of the most important task for the BMS. Therefore, special algorithms for battery monitoring are required.In this thesis, a model based battery state estimation technique using an adaptive filter tech-nology is investigated. Different battery models are studied in terms of complexity and accuracy. Following up with the introduction of different adaptive filter technology, the implementation of these methods into battery management system is decribed. Evaluations on different estimation methods are implemented from the point of view of the dynamic performance, the requirement on the computing power and the accuracy of the estimation. Real test drive data will be used as a reference to compare the result with the estimation value. Characteristics of different moni-toring methods and models are reported in this work. Finally, the trade-offs between different monitor’s performance and their computational complexity are analyzed. / BMS (eng. battery management system) har till uppgift att se till att viktiga parametrar såsom tillspännings- och temperaturintervall upprätthålls för varje individuell battericell. Då en battericells beteende är ickelinjärt är det svårt att bestämma cellens interna karakteristika direkt. Att kunna förutsäga dessa karakteristika för ett komplett batteripack kommer att en mycket viktig funktion hos framtida BMS. I detta examensarbete har en modellbaserad tillståndsestimeringsmetod med användande av adaptiv filtrering undersökts. Olika batterimodeller har studerats med avseende på komplexitet och noggrannhet. Efter introduktionen av olika metoder för adaptiv filtrering har dessa metoder implementerats i en BMS modell. Utvärdering av de olika metoderna för att åstadkomma tillståndsestimering har sedan utförts med avseende på dynamisk prestanda, krav på beräkningskraft och noggrannhet hos de resulterande estimaten. Data från uppmätta kördata från ett fordon har använts som referens för att jämföra de olika estimaten. Slutligen presenteras en jämförelse mellan de olika tillståndsestimeringsmetodernas prestanda när de appliceras på de olika batterimodellerna.

State estimation filters are computationally intensive blocks used to calculate uncertain/unknown state values from noisy/not available sensor inputs in any autonomous systems. The inputs to the actuators depend on these filter’s output and thus the scheduling of filter has to be at very small time intervals. The aim of this thesis is to investigate the possibility of using hardware accelerators to perform this computation. To make a comparative study, 3 filters that predicts 4, 8 and 16 state information was developed and implemented in Arm real time and application purpose CPU, NVIDIA Quadro and Turing GPU, and Xilinx FPGA programmable logic. The execution, memory transfer time, and the total developement time to realise the logic in CPU, GPU and FPGA is discussed. The CUDA developement environment was used for the GPU implementation and Vivado HLS with SDSoc environment was used for the FPGA implementation. The thesis concludes that a hardware accelerator is needed if the filter estimates 16 or more state information even if the processor is entirely dedicated for the computation of filter logic. Otherwise, for a 4 and 8 state filter the processor shows similar performance as an accelerator. However, in a real time environment the processor is the brain of the system, so it has to give instructions to many other functions parallelly. In such an environment, the instruction and data caches of the processor will be disturbed and there will be a fluctuation in the execution time of the filter for every iteration. For this, the best and worst case processor timings are calculated and discussed. / Tillståndsberäkningsfilter är beräkningsintensiva block som används för att beräkna osäkra / okända tillståndsvärden från bullriga / ej tillgängliga sensoringångar i autonoma system. Ingångarna till manöverdonen beror på filterens utgång och därför måste schemaläggningen av filtret ske med mycket små tidsintervall. Syftet med denna avhandling är att undersöka möjligheten att använda hårdvaruacceleratorer för att utföra denna beräkning. För att göra en jämförande studie utvecklades och implementerades 3 filter som förutsäger information om 4, 8 och 16 tillstånd i realtid med applikationsändamålen CPU, NVIDIA Quadro och Turing GPU, och Xilinx FPGA programmerbar logik. Exekvering, minnesöverföringstid och den totala utvecklingstiden för att förverkliga logiken i båda hårdvarorna diskuteras. CUDAs utvecklingsmiljö användes för GPU-implementeringen och Vivado HLS med SDSoc-miljö användes för FPGA-implementering. Avhandlingen drar slutsatsen att en hårdvaru-accelerator behövs om filtret uppskattar information om mer än 16 tillstånd även om processorn är helt dedikerad för beräkning av filterlogik. För 4 och 8 tillståndsfilter, visar processorn liknande prestanda som en accelerator. Men i realtid är processorn hjärnan i systemet; så den måste ge instruktioner till många andra funktioner parallellt. I en sådan miljö kommer processorns instruktioner och datacacher att störas och det kommer att bli en fluktuation i exekveringstiden för filtret för varje iteration. För detta beräknas och diskuteras de bästa och värsta fallstiderna.

It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system. Facing this problem, traditional methods, such as the Monte Carlo method and the Perturbation method, are either too time consuming or suffering from the strong nonlinearity in the system. To solve these, this Dissertation will mainly focus on developing the polynomial chaos based method to replace the traditional ones. Using it, the uncertainties from the model and the measurement are propagated through the polynomial chaos bases at a set of collocation points. The approximated polynomial chaos coefficients contain the statistical information. The method can greatly accelerate the calculation efficiency while not losing the accuracy, even when the system is highly stressed. In this dissertation, both the forward problem and the inverse problem of uncertainty quantification will be discussed. The forward problems will include the probabilistic power flow problem and statistical power system dynamic simulations. The generalized polynomial chaos method, the adaptive polynomial chaos-ANOVA method and the multi-element polynomial chaos method will be introduced and compared. The case studies show that the proposed methods have great performances in the statistical analysis of the large-scale power systems. The inverse problems will include the state and parameter estimation problem. A novel polynomial-chaos-based Kalman filter will be proposed. The comparison studies with other traditional Kalman filter demonstrate the good performances of the proposed Kalman filter. We further explored the area dynamic parameter estimation problem under the Bayesian inference framework. The polynomial-chaos-expansions are treated as the response surface of the full dynamic solver. Combing with hybrid Markov chain Monte Carlo method, the proposed method yields very high estimation accuracy while greatly reducing the computing time. For both the forward problem and the inverse problems, the polynomial chaos based methods haven shown great advantages over the traditional methods. These computational techniques can improve the efficiency and accuracy in power system planning, guarantee the rationality and reliability in power system operations, and, finally, speed up the power system dynamic security assessment. / PHD / It is a well-known fact that a power system state is inherently stochastic. Sources of stochasticity include load random variations, renewable energy intermittencies, and random outages of generating units, lines, and transformers, to cite a few. These stochasticities translate into uncertainties in the models that are assumed to describe the steady-sate and dynamic behavior of a power system. Now, these models are themselves approximate since they are based on some assumptions that are typically violated in practice. Therefore, it does not come as a surprise if recent research activities in power systems are focusing on how to cope with uncertainties when dealing with power system planning, monitoring and control. This Dissertation is developing polynomial-chaos-based method in quantifying, and managing these uncertainties. Three major topics, including uncertainty quantification, state estimation and parameter estimation are discussed. The developed method can improve the efficiency and accuracy in power system planning, guarantee the rationality and reliability in power system operations in dealing with the uncertainties, and, finally, enhancing the resilience of the power systems.

The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees. The contributions of the thesis are: ¿ Improved domain of attraction estimation of nonlinear systems for both continuous-time and discrete-time cases. An iterative methodology based on invariant-set results is presented for obtaining polynomial boundaries of such domain of attraction. ¿ Extension of the above problem to the case with bounded persistent disturbances acting. Different characterizations of inescapable sets with polynomial boundaries are determined. ¿ State estimation: extension of the previous results in literature to the case of fuzzy observers with polynomial gains, guaranteeing stability of the estimation error and inescapability in a subset of the zone where the model is valid. ¿ Proposal of a polynomial Lyapunov function with discrete delay in order to improve some polynomial control designs from literature. Preliminary extension to the fuzzy polynomial case. Last chapters present a preliminary experimental work in order to check and validate the theoretical results on real platforms in the future. / Pitarch Pérez, JL. (2013). Contributions to fuzzy polynomial techniques for stability analysis and control [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34773

This thesis presents an estimator to assist or replace a fighter aircraft’s air datasystem (ADS). The estimator is based on machine learning and LSTM neuralnetworks and uses the statistical correlation between states to estimate the angleof attack, angle of sideslip and Mach number using only the internal sensorsof the aircraft. The model is trained and extensively tested on a fighter jetsimulation model and shows promising results. The methodology and accuracyof the estimator are discussed, together with how a real-world implementationwould work. The estimators presented should act as a proof of concept of thepower of neural networks in state estimation, whilst the report discusses theirstrengths and weaknesses. The estimators can estimate the three targets wellin a vast envelope of altitudes, speeds, winds and manoeuvres. However, thetechnology is quite far from real-world implementation as it lacks transparencybut shows promising potential for future development. / Det här examensarbetet presenterar en estimator för att hjälpa eller ersätta ettstridsflygplans luftdatasystem (ADS). Estimatorn är baserad på maskininlärningoch LSTM neurala nätverk och använder statistisk korrelation mellan tillstånd föratt uppskatta anfallsvinkeln, sidglidningsvinkel och Mach-tal endast med hjälpav flygplanets interna sensorer. Modellen är tränad och utförligt testad på ensimuleringsmodell för stridsflygplan och visar lovande resultat. Estimatornsmetodik och noggrannhet diskuteras, tillsammans med hur en implementeringi verkligheten skulle fungera. De presenterade estimatorerna bör fungera somett “proof of concept” för kraften hos neurala nätverk för tillståndsuppskattning,medan rapporten diskuterar deras styrkor och svagheter. Estimatorerna kanuppskatta de tre tillstånden väl i ett stort spektra av altituder, hastigheter, vindaroch manövrar. Tekniken är dock ganska långt ifrån en verklig implementeringeftersom den saknar transparens, men visar lovande potential för framtidautveckling.

Les systèmes multi-capteurs exploitent les complémentarités des différentes sources sensorielles. Par exemple, le capteur visuo-inertiel permet d’estimer la pose à haute fréquence et avec une grande précision. Les méthodes de vision mesurent la pose à basse fréquence mais limitent la dérive causée par l’intégration des données inertielles. Les centrales inertielles mesurent des incréments du déplacement à haute fréquence, ce que permet d’initialiser la vision et de compenser la perte momentanée de celle-ci. Cette thèse analyse deux aspects du problème. Premièrement, nous étudions les méthodes visuelles directes pour l’estimation de pose, et proposons une nouvelle technique basée sur la corrélation entre des images et la pondération des régions et des pixels, avec une optimisation inspirée de la méthode de Newton. Notre technique estime la pose même en présence des changements d’illumination extrêmes. Deuxièmement, nous étudions la fusion des données a partir de la théorie de la commande. Nos résultats principaux concernent le développement d’observateurs pour l’estimation de pose, biais IMU et l’autocalibrage. Nous analysons la dynamique de rotation d’un point de vue non linéaire, et fournissons des observateurs stables dans le groupe des matrices de rotation. Par ailleurs, nous analysons la dynamique de translation en tant que système linéaire variant dans le temps, et proposons des conditions d’observabilité uniforme. Les analyses d’observabilité nous permettent de démontrer la stabilité uniforme des observateurs proposés. La méthode visuelle et les observateurs sont testés et comparés aux méthodes classiques avec des simulations et de vraies données visuo-inertielles. / Systems with multiple sensors can provide information unavailable from a single source, and complementary sensory characteristics can improve accuracy and robustness to many vulnerabilities as well. Explicit pose measurements are often performed either with high frequency or precision, however visuo-inertial sensors present both features. Vision algorithms accurately measure pose at low frequencies, but limit the drift due to integration of inertial data. Inertial measurement units yield incremental displacements at high frequencies that initialize vision algorithms and compensate for momentary loss of sight. This thesis analyzes two aspects of that problem. First, we survey direct visual tracking methods for pose estimation, and propose a new technique based on the normalized crosscorrelation, region and pixel-wise weighting together with a Newton-like optimization. This method can accurately estimate pose under severe illumination changes. Secondly, we investigate the data fusion problem from a control point of view. Main results consist in novel observers for concurrent estimation of pose, IMU bias and self-calibration. We analyze the rotational dynamics using tools from nonlinear control, and provide stable observers on the group of rotation matrices. Additionally, we analyze the translational dynamics using tools from linear time-varying systems, and propose sufficient conditions for uniform observability. The observability analyses allow us to prove uniform stability of the observers proposed. The proposed visual method and nonlinear observers are tested and compared to classical methods using several simulations and experiments with real visuo-inertial data.

O problema de projeto de sistemas de medição, para efeito de Estimação de Estado em Sistemas Elétricos de Potência, é um problema de otimização multiobjetivo, combinatório, que exige a investigação de um grande número de possíveis soluções. Dessa forma, metaheurísticas vêm sendo empregadas para sua solução. Entretanto, a maioria delas trata o problema de forma mono-objetivo e as poucas que consideram uma formulação multiobjetivo, não contemplam todos os requisitos de desempenho que devem ser atendidos para obtenção de um Sistema de Medição Confiável (SMC) (observabilidade e ausência de Medidas Críticas, Conjuntos Críticos de Medidas, Unidades Terminais Remotas Críticas e Unidades de Medição Fasoriais Críticas). Propõe-se, nesta tese, uma formulação multiobjetivo para o problema de projeto de sistemas de medição de uma forma mais ampla, considerando todas requisitos de desempenho que devem ser atendidos para obtenção de um SMC. Propõe-se, ainda, o desenvolvimento e implantação, em computador, de um método para tratamento desse problema, considerando o trade-off entre os requisitos de desempenho e o custo, fazendo uso do conceito de Fronteira de Pareto. O método possibilita, em uma única execução, a obtenção de quatro tipos de sistemas de medição, a partir da análise de soluções não dominadas. O método permite o projeto de sistemas de medição novos e o aprimoramento de sistemas de medição já existentes, considerando a existência apenas de medidas convencionais SCADA, apenas de Medidas Fasoriais Sincronizadas ou a existência dos dois tipos de medidas. O método proposto faz uso de um Algoritmo Evolutivo Multiobjetivo e do procedimento de obtenção e análise da matriz HΔ. Esse procedimento permite a realização de uma Busca Local, minimizando o custo para atendimento de cada um dos requisitos de desempenho mencionados acima. Simulações são realizadas utilizando dados dos sistemas de 6, 14, 30, 118 e 300 barras do IEEE, bem como do sistema de 61 barras da Eletropaulo, de forma a ilustrar, testar e validar o método proposto. Alguns dos resultados dessas simulações são comparados com resultados obtidos por outros métodos encontrados na literatura. / Metering system planning for power system state estimation is a multi-objective, combinatorial optimization problem that may require the investigation of many possible solutions. As a consequence, meta-heuristics have been employed to solve the problem. However in the majority of them the multi-objective problem is converted in a mono-objective problem and those few considering a multi-objective formulation do not consider all the performance requirements that must be attended in order to obtain a Reliable Metering System (RMS) (system observability and absence of Critical Measurements, Critical Sets, Critical Remote Terminal Units and Critical Phasor Measurement Units). This thesis proposes a multi-objective formulation for the metering system planning problem in a wide way, that is, considering all the performance requirements that must be attended to obtain a RMS. This thesis also proposes the development and implementation, in computer, of a method to solve the metering system planning problem, considering the trade-off between the two conflicting objectives of the problem (minimizing cost while maximizing the performance requirements) making use of the concept of Pareto Frontier. The method allows, in only one execution, the project of four types of metering systems, from the analysis of non-dominated solutions. The method enable the design of new metering systems as well as the improvement of existing ones, considering the existence of only conventional SCADA measurements, or only synchronized phasor measurements or the existence of both types of measurements. The proposed method combines a multi-objective evolutionary algorithm based on subpopulation tables with the properties of the so-called HΔ matrix. The subpopulations tables adequately model several metering system performance requirements enabling a better exploration of the solution space. On the other hand, the properties of the HΔ matrix enable a local search that improves the evolutionary process and minimizes the computational effort. Simulations results with IEEE 6, 14, 30, 118 and 300-bus test systems and with a 61-bus system of Eletropaulo illustrate the efficiency of the proposed method. Some of the results of these simulations will be compared with those published in literature.

Nombreux sont les problèmes classiquement rencontrés dans les sciences de l'ingénieur dont la résolution fait appel à l'estimation d'état d'un système par le biais d'un observateur. La synthèse d'un observateur n'est envisageable qu'à la condition de disposer d'un modèle à la fois exploitable et représentatif du comportement dynamique du système. Or, la modélisation du système et la synthèse de l'observateur deviennent des tâches difficiles à accomplir dès lors que le comportement dynamique du système doit être représenté par un modèle de nature non linéaire. Face à ces difficultés, l'approche multimodèle peut être mise à profit. Les travaux présentés dans cette thèse portent sur les problèmes soulevés par l'identification, l'estimation d'état et le diagnostic de systèmes non linéaires représentés à l'aide d'un multimodèle découplé. Ce dernier, composé de sous-modèles qui peuvent être de dimensions différentes, est doté d'un haut degré de généralité et de flexibilité et s'adapte particulièrement bien à la modélisation des systèmes complexes à structure variable. Cette caractéristique le démarque des approches multimodèles plus conventionnelles qui ont recours à des sous-modèles de même dimension. Après une brève introduction à l'approche multimodèle, le problème de l'estimation paramétrique du multimodèle découplé est abordé. Puis sont présentés des algorithmes de synthèse d'observateurs d'état robustes vis-à-vis des perturbations, des incertitudes paramétriques et des entrées inconnues affectant le système. Ces algorithmes sont élaborés à partir de trois types d'observateurs dits à gain proportionnel, à gain proportionnel-intégral et à gain multi-intégral. Enfin, les différentes phases d'identification, de synthèse d'observateurs et de génération d'indicateurs de défauts sont illustrées au moyen d'un exemple académique de diagnostic du fonctionnement d'un bioréacteur / The state estimation of a system, with the help of an observer, is largely used in many practical situations in order to cope with many classic problems arising in control engineering. The observer design needs an exploitable model able to give an accurate description of the dynamic behaviour of the system. However, system modelling and observer design can not easily be accomplished when the dynamic behaviour of the system must be described by non linear models. The multiple model approach can be used to tackle these difficulties. This thesis deals with black box modelling, state estimation and fault diagnosis of nonlinear systems represented by a decoupled multiple model. This kind of multiple model provides a high degree of generality and flexibility in the modelling stage. Indeed, the decoupled multiple model is composed of submodels which dimensions can be different. Thus, this feature is a significant difference between the decoupled multiple model and the classical used multiple model where all the submodels have the same dimension. After a brief introduction to the multiple model approach, the parametric identification problem of a decoupled multiple model is explored. Algorithms for robust observers synthesis with respect to perturbations, modelling uncertainties and unknown inputs are afterwards presented. These algorithms are based on three kinds of observers called proportional, proportional-integral and multiple-integral. Lastly, identification, observers synthesis and fault sensitivity signals generation are illustrated via a simulation example of a bioreactor