Orientadores: Ariovaldo Verandio Garcia, Eduardo Nobuhiro Asada / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T08:42:33Z (GMT). No. of bitstreams: 1 Almeida_MadsonCortesde_D.pdf: 1655535 bytes, checksum: 2d7ea952e257cfd27e731d17f44651d1 (MD5) Previous issue date: 2007 / Resumo: Desde a sua introdução em 1969 por Fred C. Schweppe, J. Wildes e D. Rom, a estimação de estado em sistemas de energia elétrica tem sido largamente estudada. Atualmente, a estimação de estado vem evoluindo para uma modelagem mais completa, capaz de tratar variáveis não consideradas no modelo barra/ramo das redes. Entre os avanços mais significativos destaca-se a modelagem de chaves e disjuntores proposta em 1993 por A. J. Monticelli que deu origem à chamada estimação de estado generalizada. Neste trabalho, a estimação de estado generalizada é aplicada a redes trifásicas de energia elétrica nas quais os desbalanços nas cargas e os desequilíbrios na rede são considerados. A utilização dessa modelagem leva a um grau de detalhamento superior ao dos modelos por fase e, conseqüentemente, o estado estimado é mais preciso. Além disso, a modelagem trifásica permite que os sistemas de transmissão e distribuição sejam tratados indistintamente. Para a função estimador de estado foram implementados os métodos baseados na matriz ganho e no tableauesparso de Hachtel. As particularidades associadas à aplicação desses métodos ao modelo generalizado trifásico são apresentadas e discutidas. Na análise de observabilidade implementou-se o método baseado na fatoração da matriz ganho e um novo método baseado na solução de mínima norma do estimador de estado, que é uma das principais contribuições deste trabalho. Por fim, no problema da detecção e identificação de erros grosseiros foram implementados os métodos dos resíduos normalizados e dos multiplicadores de Lagrange normalizados / Abstract: Since its introduction in 1969 by Fred C. Schweppe, J. Wildes and D. Rom, the power system state estimation has been widely studied. The state estimation is evolving to a more detailed modeling, which considers state variables not commonly used in bus/branch system modeling. A generalized state estimation model, in which switches and circuit breakers are represented, was proposed in 1993 by A. J. Monticelli. In this work the generalized state estimation approach is applied to a three-phase power system model, where the unbalanced loads and the asymmetric nature of the system are considered. This treatment provides a more precise real-time model and, consequently, a more precise estimated state. Moreover, it allows that the same state estimation model be applied to both transmission and distribution systems. The three-phase state estimator has been implemented with gain matrix and Hachtel's sparse tableau approaches. Details about differences between the three-phase model and single-phase models are presented. In observability analysis a new method based on minimum norm solution has been introduced, and represents the main contribution of this work. The observability analysis based on gain matrix factorization have also been implemented for comparison purposes. Finally, in the bad data detection and identification, the normaIized residuaIs method and the normalized Lagrange multipIiers method have been implemented. / Doutorado / Energia Eletrica / Doutor em Engenharia Elétrica

Orientador: Gilmar Barreto / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T06:42:54Z (GMT). No. of bitstreams: 1 Battaglin_PauloDavid_D.pdf: 3180685 bytes, checksum: 5e1e9893bb97a4df42116a4c0d8b10d6 (MD5) Previous issue date: 2014 / Resumo: Este trabalho mostrará a aplicação do filtro de Kalman a um processo computacional discreto, o qual será representado por um modelo matemático que é um sistema de equações lineares, multivariáveis, discretas, estocásticas e variantes no tempo. As contribuições desta pesquisa evidenciam a construção de um modelo matemático apropriado de observabilidade instantânea para representar sistemas que variam rapidamente no tempo; a construção dos fundamentos teóricos do filtro de Kalman a ser aplicado em sistemas lineares, multivariáveis, discretos, estocásticos e variantes no tempo; bem como a construção deste filtro neste contexto e sua aplicação a um processo computacional discreto. Neste trabalho propomos um método para determinar: a matriz de observabilidade instantânea, o vetor de estimação de estado interno, a matriz de covariâncias de erros de estimação de estado interno e a latência de um processo computacional discreto, quando as medidas na saída do computador são conhecidas. Aqui mostramos que quando a propriedade observabilidade instantânea do sistema é verificada, a latência de um processo computacional pode ser estimada. Esta é uma vantagem comparada com os métodos de observabilidade usual, os quais são baseados em cenários estáticos. A aplicação potencial dos resultados deste trabalho é na predição de congestionamentos em processos que variam no tempo e acontecem em computadores digitais. Em uma perspectiva mais ampla, o método da observabilidade instantânea pode ser aplicado na identificação de patologias, na previsão de tempo, em navegação e rastreamento no solo, na água e no ar; no mercado de ações e em muitas outras áreas / Abstract: This work will show the application of the Kalman filter to a discrete computational process, which will be represented by a mathematical model: a system of linear, multivariable, discrete, stochastic and time-varying equations. The contributions of this research show the construction of an appropriate mathematical model of instantaneous observability to represent systems that vary quickly in time; the construction of the theoretical foundations of the Kalman filter to be applied to a linear, multivariable, discrete, stochastic and time-varying system; the construction of this filter in this context and its application to a discrete computational process. In this research we propose a method to determine: the instantaneous observability matrix, the internal state vector estimation, Covariance matrix of internal state estimation error and the latency of a digital computational process, when the measures on the computer output are known. Here we show that when the instantaneous observability property of the system comes true, a computing process latency can be estimated. This is an advantage compared to usual observability methods, which are based on static scenarios. The potential application of the results of this work is to predict bottlenecks in time-varying processes which happen inside the discrete computers. In a broader perspective, the instantaneous observability method can be applied on identification of a pathology, weather forecast, navigation and tracking on ground, in the water and in the air; in stock market prediction and many other areas / Doutorado / Automação / Doutor em Engenharia Elétrica

L'estimation de la pose (position et l'attitude) en temps réel est une fonction clé pour les véhicules autonomes routiers. Cette thèse vise à étudier des systèmes de localisation pour ces véhicules en utilisant des capteurs automobiles à faible coût. Trois types de capteurs sont considérés : des capteurs à l'estime qui existent déjà dans les automobiles modernes, des récepteurs GNSS mono-fréquence avec antenne patch et une caméra de détection de la voie regardant vers l’avant. Les cartes très précises sont également des composants clés pour la navigation des véhicules autonomes. Dans ce travail, une carte de marquage de voies avec une précision de l’ordre du décimètre est considérée. Le problème de la localisation est étudié dans un repère de travail local Est-Nord-Haut. En effet, les sorties du système de localisation sont utilisées en temps réel comme entrées dans un planificateur de trajectoire et un contrôleur de mouvement pour faire en sorte qu’un véhicule soit capable d'évoluer au volant de façon autonome à faible vitesse avec personne à bord. Ceci permet de développer des applications de voiturier autonome aussi appelées « valet de parking ». L'utilisation d'une caméra de détection de voie rend possible l’exploitation des informations de marquage de voie stockées dans une carte géoréférencée. Un module de détection de marquage détecte la voie hôte du véhicule et fournit la distance latérale entre le marquage de voie détecté et le véhicule. La caméra est également capable d'identifier le type des marquages détectés au sol (par exemple, de type continu ou pointillé). Comme la caméra donne des mesures relatives, une étape importante consiste à relier les mesures à l'état du véhicule. Un modèle d'observation raffiné de la caméra est proposé. Il exprime les mesures métriques de la caméra en fonction du vecteur d'état du véhicule et des paramètres des marquages au sol détectés. Cependant, l'utilisation seule d'une caméra a des limites. Par exemple, les marquages des voies peuvent être absents dans certaines parties de la zone de navigation et la caméra ne parvient pas toujours à détecter les marquages au sol, en particulier, dans les zones d’intersection. Un récepteur GNSS, qui est obligatoire pour le démarrage à froid, peut également être utilisé en continu dans le système de localisation multi-capteur du fait qu’il permet de compenser la dérive de l’estime. Les erreurs de positionnement GNSS ne peuvent pas être modélisées simplement comme des bruits blancs, en particulier avec des récepteurs mono-fréquence à faible coût travaillant de manière autonome, en raison des perturbations atmosphériques sur les signaux des satellites et les erreurs d’orbites. Un récepteur GNSS peut également être affecté par de fortes perturbations locales qui sont principalement dues aux multi-trajets. Cette thèse étudie des modèles formeurs de biais d’erreur GNSS qui sont utilisés dans le solveur de localisation en augmentant le vecteur d'état. Une variation brutale due à multi-trajet est considérée comme une valeur aberrante qui doit être rejetée par le filtre. Selon le flux d'informations entre le récepteur GNSS et les autres composants du système de localisation, les architectures de fusion de données sont communément appelées « couplage lâche » (positions et vitesses GNSS) ou « couplage serré » (pseudo-distance et Doppler sur les satellites en vue). Cette thèse étudie les deux approches. En particulier, une approche invariante selon la route est proposée pour gérer une modélisation raffinée de l'erreur GNSS dans l'approche par couplage lâche puisque la caméra ne peut améliorer la performance de localisation que dans la direction latérale de la route. / Estimating the pose (position and attitude) in real-time is a key function for road autonomous vehicles. This thesis aims at studying vehicle localization performance using low cost automotive sensors. Three kinds of sensors are considered : dead reckoning (DR) sensors that already exist in modern vehicles, mono-frequency GNSS (Global navigation satellite system) receivers with patch antennas and a frontlooking lane detection camera. Highly accurate maps enhanced with road features are also key components for autonomous vehicle navigation. In this work, a lane marking map with decimeter-level accuracy is considered. The localization problem is studied in a local East-North-Up (ENU) working frame. Indeed, the localization outputs are used in real-time as inputs to a path planner and a motion generator to make a valet vehicle able to drive autonomously at low speed with nobody on-board the car. The use of a lane detection camera makes possible to exploit lane marking information stored in the georeferenced map. A lane marking detection module detects the vehicle’s host lane and provides the lateral distance between the detected lane marking and the vehicle. The camera is also able to identify the type of the detected lane markings (e.g., solid or dashed). Since the camera gives relative measurements, the important step is to link the measures with the vehicle’s state. A refined camera observation model is proposed. It expresses the camera metric measurements as a function of the vehicle’s state vector and the parameters of the detected lane markings. However, the use of a camera alone has some limitations. For example, lane markings can be missing in some parts of the navigation area and the camera sometimes fails to detect the lane markings in particular at cross-roads. GNSS, which is mandatory for cold start initialization, can be used also continuously in the multi-sensor localization system as done often when GNSS compensates for the DR drift. GNSS positioning errors can’t be modeled as white noises in particular with low cost mono-frequency receivers working in a standalone way, due to the unknown delays when the satellites signals cross the atmosphere and real-time satellites orbits errors. GNSS can also be affected by strong biases which are mainly due to multipath effect. This thesis studies GNSS biases shaping models that are used in the localization solver by augmenting the state vector. An abrupt bias due to multipath is seen as an outlier that has to be rejected by the filter. Depending on the information flows between the GNSS receiver and the other components of the localization system, data-fusion architectures are commonly referred to as loosely coupled (GNSS fixes and velocities) and tightly coupled (raw pseudoranges and Dopplers for the satellites in view). This thesis investigates both approaches. In particular, a road-invariant approach is proposed to handle a refined modeling of the GNSS error in the loosely coupled approach since the camera can only improve the localization performance in the lateral direction of the road. Finally, this research discusses some map-matching issues for instance when the uncertainty domain of the vehicle state becomes large if the camera is blind. It is challenging in this case to distinguish between different lanes when the camera retrieves lane marking measurements.As many outdoor experiments have been carried out with equipped vehicles, every problem addressed in this thesis is evaluated with real data. The different studied approaches that perform the data fusion of DR, GNSS, camera and lane marking map are compared and several conclusions are drawn on the fusion architecture choice.

Contrôlabilité de systèmes gouvernés par des équations aux dérivées partielles / Controllability of systems governed by partial differential equations

Dans le futur, les réseaux de distribution deviendront intelligents et actifs et seront utilisés au plus près de leurs limites car l'avènement de la dérégulation avec l'introduction massive de la production décentralisée induira une optimisation des infrastructures de l'énergie pour des besoins environnementaux d'une part, mais aussi par l'introduction de la concurrence dans un secteur autrefois monopolistique. Si on veut utiliser pleinement le potentiel des productions décentralisées dans les réseaux de distribution, on devra rendre ces réseaux observables au moyen de mesures qui seront intégrées dans les systèmes de supervision et de contrôle car sans observabilité et reconstruction d’état du réseau, le contrôle du réseau est impossible. Pour un grand réseau de distribution (quelques milliers de nœuds), avec un taux d’insertion important de productions décentralisées, l’observabilité en temps-réel devient très difficile. Dans la littérature scientifique, on ne trouve pas de travaux prenant en compte les réseaux intelligents et reconfigurables de demain avec production décentralisée massive. L'enjeu du projet sera donc de développer des algorithmes de reconstruction d'état prenant en compte les spécificités des réseaux du futur, à les valider et à les intégrer dans les outils de gestion des réseaux du distributeur. La reconstruction d'état permettra d'aider à l'automatisation et donc à l'introduction d'intelligence dans les réseaux de distribution du futur ainsi qu'à l'insertion massive de productions décentralisées.________________________________________ / In the future, distribution networks will become intelligent and active and will be operated as close as possible to their limits, with the advent of deregulation and with the introduction of mass production that will lead to a decentralized infrastructure, this will guide to an optimization of energy for environmental issues in one side, but also to the introduction of competition in a sector once monopolistic. If the potential of distributed generation in the distribution networks is to be fully exploited, we will make these networks observable through measures which will be integrated into supervision systems and control, because without observability and reconstruction of network status, control network is impossible. For a large distribution network (a few thousand nodes), with an important quantity of distributed generation, real-time observability becomes very difficult. In the scientific literature, there is no work taking into consideration intelligent networks and reconfigurable of the future with decentralized mass power generation. The challenge of the project will be to develop algorithms of reconstruction of the state of the network, taking into account the characteristics of the networks of the future, to validate and integrate them into management tools distribution networks. The reconstruction of the state will assist in the automation and hence the introduction of intelligence in the distribution networks of the future and the insertion massive distributed generation.

With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.

In this thesis the issue with the description, modeling and analysis of hybrid dynamic systems is solved. The main goal is to select appropriate method for solving hybrid dynamic systems and to aim to question on solving controllability, observability and stability of these systems. Two types of hybrid systems are covered in this work. In the first part, the description and simulation of hybrid systems that are compiled of continous and discrete parts with the systems of discrete affair are specified. For the description and modeling of hybrid automata and hybrid Petri nets have been used. The possibilities mentioned above are showned on the simple examples. Second part of this work covers the description, modeling and analysis of hybrid systems that are compiled of links continous and discreate dynamic system. The state space has been used. The universal state equations are mentioned and have been used for the description of the hybrid system and the structure of the matrixes A, B, C, D. The relation and basic method are spin off for the analysis of hybrid dynamic systems. From the analysis point of view, these are general methods for the controllability, observability, stability and steady state of hybrid dynamic systems. The examples of hybrid control systems for mentioned methods are specified. If it is allowed, the results are verified by the simulation in MATLAB Simulink background.

Dans cette thèse, nous étudions la stabilisation et la contrôlabilité exacte de certains problèmes distribués avec différents types d’amortissement. Dans la première partie, nous étudions la stabilité d'un système Bresse mono-dimensionnel avec un contrôle de type mémoire infini et/ou avec une conduction de chaleur donnée par la loi de Cattaneo agissant sur le déplacement de l'angle de cisaillement. Nous considérons le cas intéressant de conditions aux bords de types entièrement Dirichlet. En effet, sous la condition d'égalité de la vitesse de propagation des ondes, nous établissons la stabilité exponentielle du système. Cependant, dans le cas physique naturel lorsque les vitesses de propagation sont différentes, en utilisant une méthode de décomposition de spectre, nous montrons que le système de Bresse n'est pas uniformément stable. Dans ce cas, nous établissons un taux de décroissance énergétique polynomiale. Notre étude est valable pour toutes les autres conditions aux bords mixtes. Dans la deuxième partie, nous étudions la stabilisation d'un système élastique faiblement amorti d’un système couplé abstrait du second ordre. Dans le cadre de certains paramètres, en utilisant la méthode spectrale, nous établissons la stabilité exponentielle du système. Cependant, lorsque le système n'est pas uniformément stable, nous établissons le taux optimal de la décroissance polynomiale de l'énergie du système. Dans la troisième partie, nous étudions la contrôlabilité exacte indirecte d'un système de Timoshenko mono-dimensionnel. En effet, nous considérons les cas lorsque la vitesse de propagation des ondes sont égales ou différentes. Tout d'abord, nous utilisons des analyses non harmoniques pour établir une inégalité d'observabilité faible, qui dépend du rapport des vitesses de propagation des ondes. Ensuite, en utilisant la méthode HUM, nous prouvons que le Système est parfaitement contrôlable et que le temps de contrôle peut être faible. / In this work, we study the stabilization and the exact controllability of some distributed problems. In the first part, we study the stability of a one-dimensional Bresse System with infinite memory type control and/or with heat conduction given by Cattaneo's law acting in the shear angle displacement, where we consider the interesting case of fully Dirichlet boundary conditions. Indeed, under a equal speed of propagation condition, we establish the exponential stability of the System. However, in the natural physical case when the speeds of propagation are different, using a spectrum method, we show that the Bresse System is not uniformly exponentially stable. In this case, we establish a polynomial energy decay rate. Our study is valid for all other mixed boundary conditions. In the second part, we study the stabilization of a weakly damped elastic System of an abstract second order equation. Indeed, under some condition on the parameters, using a spectrum method, we establish the exponential stability of the System. However, when the System is not uniformly stable, using a spectrum method, we establish the optimal polynomial decay rate of the energy of the System. In the third part, we study the indirect boundary exact controllability of a one-dimensional Timoshenko System. Indeed, we consider the cases when the speed waves propagate with equal or different speeds. We use non harmonic analysis to establish weak observability inequality, which depends on the ratio of the waves propagation speeds. Next, using the HUM method, we prove that the System is exactly controllable, and that the control time can be small.

5G is expected to provide a high-performance and highly efficient network to prominent industry verticals with ubiquitous access to a wide range of services with orders of magnitude of improvement over 4G. Network slicing, which allocates network resources according to users’ specific requirements, is a key feature to fulfil the diversity of requirements in 5G network. However, network slicing also brings more orchestration and difficulty in monitoring and admission control. Although the problem of admission control has been extensively studied, those research take measurements for granted. Fixed high monitoring frequency can waste system resources, while low monitoring frequency (low level of observability) can lead to insufficient information for good admission control decisions. To achieve efficient admission control in 5G, we consider the impact of configurable observability, i.e. control observed information by configuring measurement frequency, is worth investigating. Generally, we believe more measurements provide more information about the monitored system, thus enabling a capable decision-maker to have better decisions. However, more measurements also bring more monitoring overhead. To study the problem of configurable observability, we can dynamically decide what measurements to monitor and their frequencies to achieve efficient admission control. In the problem of admission control with configurable observability, the objective is to minimize monitoring overhead while maintaining enough information to make proper admission control decisions. In this thesis, we propose using the Deep Reinforcement Learning (DRL) method to achieve efficient admission control in a simulated 5G end-to-end network, including core network, radio access network and four dynamic UEs. The proposed method is evaluated by comparing with baseline methods using different performance metrics, and then the results are discussed. With experiments, the proposed method demonstrates the ability to learn from interaction with the simulated environment and have good performance in admission control and used low measurement frequencies. After 11000 steps of learning, the proposed DRL agents generally achieve better performance than the threshold-based baseline agent, which takes admission decisions based on combined threshold conditions on RTT and throughput. Furthermore, the DRL agents that take non-zero measurement costs into consideration uses much lower measurement frequencies than DRL agents that take measurement costs as zero. / 5G förväntas ge ett högpresterande och högeffektivt nätverk till framstående industrivertikaler genom allmän tillgång till ett brett utbud av tjänster, med förbättringar i storleksordningar jämfört med 4G. Network slicing, som allokerar nätverksresurser enligt specifika användarkrav, är en nyckelfunktion för att uppfylla mångfalden av krav i 5G-nätverk. Network slicing kräver däremot också mer orkestrering och medför svårigheter med övervakning och tillträdeskontroll. Även om problemet med tillträdeskontroll har studerats ingående, tar de studierna mätfrekvenser för givet. Detta trots att hög övervakningsfrekvens kan slösa systemresurser, medan låg övervakningsfrekvens (låg nivå av observerbarhet) kan leda till otillräcklig information för att ta bra beslut om antagningskontroll. För att uppnå effektiv tillträdeskontroll i 5G anser vi att effekten av konfigurerbar observerbarhet, det vill säga att kontrollera observerad information genom att konfigurera mätfrekvens, är värt att undersöka. Generellt tror vi att fler mätningar ger mer information om det övervakade systemet, vilket gör det möjligt för en kompetent beslutsfattare att fatta bättre beslut. Men fler mätningar ger också högre övervakningskostnader. För att studera problemet med konfigurerbar observerbarhet kan vi dynamiskt bestämma vilka mätningar som ska övervakas och deras frekvenser för att uppnå effektiv tillträdeskontroll. I problemet med tillträdeskontroll med konfigurerbar observerbarhet är målet att minimera övervakningskostnader samtidigt som tillräckligt med information bibehålls för att fatta korrekta beslut om tillträdeskontroll. I denna avhandling föreslår vi att använda Deep Reinforcement Learning (DRL)-metoden för att uppnå effektiv tillträdeskontroll i ett simulerat 5G-änd-till-änd-nätverk, inklusive kärnnät, radioaccessnätverk och fyra dynamiska användarenheter. Den föreslagna metoden utvärderas genom att jämföra med standardmetoder som använder olika prestationsmått, varpå resultaten diskuteras. I experiment visar den föreslagna metoden förmågan att lära av interaktion med den simulerade miljön och ha god prestanda i tillträdeskontroll och använda låga mätfrekvenser. Efter 11 000 inlärningssteg uppnår de föreslagna DRL-agenterna i allmänhet bättre prestanda än den tröskelbaserade standardagenten, som fattar tillträdesbeslut baserat på kombinerade tröskelvillkor för RTT och throughput. Dessutom använder de DRL-agenter som tar hänsyn till nollskilda mätkostnader, mycket lägre mätfrekvenser än DRL-agenter som tar mätkostnaderna som noll.

Threaded fasteners tend to break and loosen when exposed to cyclic loads or potent temperature variations. Additionally, if the joint is held tightly to the structure, distortion will occur under thermal expansion issues. These complications can be prevented by identifying and regulating the clamping force to an appropriate degree – adapted to the properties of the joint. Torque-controlled tightening is a way of monitoring the clamping force, but it assumes constant friction and therefore has low accuracy, with an error of around 17% - 43%.This thesis investigates if the friction and clamping force can be estimated using the Extended and Unscented Kalman filters to increase the precision of the torque-controlled methodology. Before the investigation, data were collected for two widely used tightening strategies. The first tightening strategy is called Continuous Drive, where the angular velocity is kept at a constant speed while torque is increased. The second strategy is TurboTight, where the angular velocity starts at a very high speed and decreases with increased torque. The collected data were noisy and had to be filtered. A hybrid between a Butterworth lowpass filter and a Sliding Window was developed and exploited for noise cancellation.The investigations revealed that it was possible to use both the Extended and Unscented Kalman filers to estimate friction and clamping force in threaded fasteners. In Continuous Drive tightening, both the EKF and UKF performed well - with an averagequality factor of 81.87% and 88.38%, and with an average error (at max torque) of 3.54% and 4.09%, respectively. However, the TurboTight strategy was much more complex and had a higher order of statistical moments to account for. Thus, the UKF outperformed the EKF with an average quality factor of 93.02% relative to 24.49%, and with an average error (at max torque) of 3.50% compared to 4.19%