Este trabalho trata da coordenação de robôs heterogêneos que consiste em um robô helicóptero e em múltiplos robôs móveis com rodas, de modo que estes sigam um líder. Para atingirmos este objetivo, neste trabalho desenvolve-se a estimação da atitude e posição de um robô utilizando filtros robustos. Os filros realizam a fusão dos sinais de uma unidade de medida inercial de baixo custo e de um receptor GPS (Global Positioning System) considerando um modelo de corpo rígido formulado em termos de quatérnios. Resultados experimentais são apresentados baseados em um estudo comparativo entre os filtros robustos e o filtro de Kalman. Desenvolve-se o controle de um robô móvel com rodas deslizantes e de um robô helicóptero. Para realizar o controle robusto utilzia-se um regulador robusto recursivo. Simulaçõoes do robô móvel com rodas deslizantes e do robô helicóptero são apresentadas utilizando os parâmetros de um robô móvel Pioneer 3AT e de um robô helicóptero RMAX. Desenvolve-se também um mini robô helicóptero e uma base de movimento para realização de testes de controle do mini robô helicóptero constiste de um mini helimodelo e de um piloto automático baseado em um microprocessador com conectividade Wi-Fi, uma unidade de medida inercial e uma placa de controle de servos motores. E por fim, desenvolve-se a coordenação de robôs heterogêneos de modo que estes sigam um líder. Para isto é utilziado um controlador descentralizado e cooperativo cuja finalidade é gerar trajetórias de referência para que os robôs heterogênos se movimentem em formação rígida. / This research deals with the coordination of the heterogeneous robots, consisting of a robotic helicopter and multiple uheeled mobile robots, to achieve this aim this work develops the estimation of attitude, heading and positions of a robot based on robust filters. The filters perform the fusion of the signals of a low-cost inertial measurement unit and a GPS (Global Positioning System) receiver considering a rigid body model formulated in terms of quaternions. Experimental results are presented based on a comparative study of the robust filters and Kalman filter. It develops a robust control scheme of a ski-steering mobile robot and a robotic helicopter. The robust control is performed through a the robust recursive regulator. Simulations of the skid-steering mobile robot and a robotic helicopter are presented using the parameters of a Pioneer 3AT and a robotic helicopter RMAX. It also develops a mini robotic helicopter and a motion base to perform control test of a mini robotic helicopter. The mini robotic helicopter consists of a mini helicopter TREX 450 XL and an autopilot based on a microprocessor with Wi-Fi, an inertial measurement unit a servo control board. To finish, it develops the coordination of the heterogeneous robots in order to perform a leader strategy based on the concept of rigid formation.

Aujourd'hui, les réseaux de communications sont largement utilisés pour relier les points de ressources, qui permettent la transmission de données à distance, de réduire la complexité dans le cadre de câblage et les coûts de support et de fournir l'aide dans la maintenance. En raison de ces avantages, les réseaux ont été introduits dans les systèmes automatiques au cours de ces dernières décennies et de nouveaux protocoles de réseau industriel ont été également développés pour assurer le contrôle à distance. Les systèmes contrôlés en réseau SCR (Networked Control System NCS) sont des systèmes automatiques traditionnels où les actionneurs, les capteurs, les contrôleurs et des autres composants sont distribués autour d'un réseau de communication, qui peut être partagé ou non avec d'autres applications. Les données de commande et de diagnostic sont échangées entre les composants du système (capteur, contrôleur, actionneur) via ce réseau partagé. Cette nouvelle architecture de système de contrôle introduit des problèmes originaux, en termes de retard variable affectant la transmission, des pertes de paquets, etc. Dans l'objectif de maintenir de bonnes performances du module de diagnostic face à des éventuelles variations introduites par le réseau, il est intéressant d'introduire des nouvelles approches. Nous avons rapporté les résultats relatifs aux techniques d'estimation optimale à base de filtre de Kalman, de façon à constituer un document aussi complet que possible traitant la génération de résidus et l'isolation des défauts dans SCR. Notre contribution consiste, dans un premier temps, à développer un modèle d'état d'un système contrôlé via un réseau. En deuxième temps, nous proposons un générateur de résidus en se basant sur les hypothèses simulant le retard induit par le réseau. Finalement, nous développons un filtre isolateur pour identifier directement les défauts affectant les actionneurs dans un SCR / Today's communications networks are widely used to connect the resources, enabling the remote data transmission, reducing the cabling complexity, minimizing costs and providing easy maintenance. Because of these advantages, the networks have been introduced in automatic systems during recent decades and new industrial network protocols were also developed for the remote control. The systems controlled by networks, the term "Networked Control Systems (NCS)" are automatic traditional systems where the actuators, the sensors, the controllers and other components are distributed around a communication network that can be shared or not with other applications. The data of both control and diagnostic are exchanged between system components (sensor, controller and actuator) via the shared network. This new architecture of control system introduces new problems in terms of variable delay affecting the transmission, the packet loss, etc. With the aim to maintain good performance of diagnostic module face of possible changes introduced by the network, it is interesting to introduce new approaches. We have reported results for the optimal estimation techniques based on Kalman filter, thus creating a report as complete as possible, treating the residual generation and fault isolation in NCS. Our contribution consists, firstly, to develop a state space model of a system controlled via a network. Secondly, we propose a residual generator based on the delay models induced by the network. Finally, we develop a isolation filter in order to directly identify in the actuators faults in a SCR

Cette thèse porte sur l’analyse des signaux sEMG multicanaux à l’aide de la transformée en ondelettes, de la transformation de Hilbert-Huang et d’autres méthodes d’analyse ou de traitement des signaux, telles que le filtrage de Kalman et de Goertzel, pour détecter, mesurer, filtrer et décomposer les signaux sEMG afin d’identifier des tendances dans le temps et en fréquence des mouvements de flexion-extension des doigts de la main en observant les signaux myoélectriques des muscles superficiels. Le but est de prédire le mouvement des doigts de la main et de minimiser le temps de calcul pour permettre de contrôler des prothèses à l’aide de capteurs superficiels. L’hypothèse est fondée sur l’idée que tous les mouvements de la main sont une réponse active de l’activité myoélectrique des muscles spécifiques et que l’activité électrique peut être mesurée comme un signal associé à une séquence de mouvement des éléments de la main (doigts). Chaque doigt peut effectuer une trajectoire de la position de repos à la position finale, cette trajectoire qui n’est évidemment pas instantanée, engendre un signal myoélectrique lui-même non instantanée. L’activité électrique du muscle est présente sur les signaux enregistrés. Cette activité électrique peut être reconnue par un algorithme à partir d’une base de données de modèles de mouvement. Comparer l’activité électrique des muscles en temps réel par rapport à ces données peut permettre de détecter une tendance du comportement de la main et donc de prévoir le mouvement spécifique avant ou en même temps. Cette prédiction doit autoriser une baisse du temps de réponse de la prothèse / This thesis deals with the analysis of multichannel sEMG signals using wavelet transform, Hilbert-Huang transformation and other signal analysis or processing methods such as Kalman and Goertzel filtering, for detecting, measuring, filtering and decomposing sEMG signals to identify patterns in the time and frequency of flexion-extension movements of the fingers of the hand by analyzing the myoelectric signals of the superficial muscles. The aim is to predict the movement of the fingers of the hand and to minimize the computation time to allow the control of prostheses by means of superficial sensors. The hypothesis is based on the idea that all hand movements are an active response of the myoelectric activity of specific muscles and that electrical activity can be measured as a signal associated with a sequence of motion of the elements of the hand (fingers). Each finger can make a trajectory from the rest position to the final position, this trajectory which is obviously not instantaneous, generates a non-instantaneous myoelectric signal itself. The electrical activity of the muscle is present on the recorded signals. This electrical activity can be recognized by an algorithm from a database of the motion models. Comparing the electrical activity of the muscles in real time with respect to these data can make it possible to detect a tendency in the behavior of the hand and therefore to predict the specific movement before or at the same time. This prediction must allow a reduction in the response time of the prosthesis

Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made.

A flood routing and stage prediction model is developed using the techniques of State Space and Kalman filtering. The governing equation is the physically based hydrologic method of flood routing with the output being an optimal estimate of stage given known inputs of streamflow. These equations are developed in state space and the Kalman filter is employed to estimate the flow and river stage. The model is applied to the Toutle and Cowlitz Rivers in the State of Washington, where the stage is affected by a shifting bed elevation. With the deterministic inputs at Mayfield Dam on the Cowlitz and Tower Road on the Toutle, the optimal output of stage is predicted at Castle Rock on the Cowlitz River. Addition of the Kalman Filter improves stage prediction based on an application to an observed storm event.

<p>The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently.</p><p>The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing.</p> / <p>Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt.</p><p>Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.</p>

The presence of vibration is an important problem in many engineering applications. Various passive techniques have traditionally been used in order to reduce waves and vibrations, and their harmful effects. Passive techniques are, however, difficult to apply in the low frequency region. In addition, the use of passive techniques often involve adding mass to the system, which is undesirable in many applications. As an alternative, active techniques can be used to manipulate system dynamics and to control the propagation of waves and vibrations. This thesis deals with modeling, estimation and active control of systems that have resonant dynamics. The systems are exposed to stochastic disturbances. Some of them excite the system and generate vibrational responses and other corrupt measured signals. Feedback control of a beam with attached piezoelectrical elements is studied. A detailed modeling approach is described and system identification techniques are employed for model order reduction. Disturbance attenuation of a non-measured variable shows to be difficult. This issue is further analyzed and the problems are shown to depend on fundamental design limitations. Feedforward control of traveling waves is also considered. A device with properties analogous to those of an electrical diode is introduced. An `ideal´ feedforward controller based on the mechanical properties of the system is derived. It has, however, poor noise rejection properties and it therefore needs to be modified. A number of feedforward controllers that treat the measurement noise in a statistically sound way are derived. Separation of overlapping traveling waves is another topic under investigation. This operation also is sensitive to measurement noise. The problem is thoroughly analyzed and Kalman filtering techniques are employed to derive wave estimators with high statistical performance. Finally, a nonlinear regression problem with close connections to unbalance estimation of rotating machinery is treated. Different estimation techniques are derived and analyzed with respect to their statistical accuracy. The estimators are evaluated using the example of separator balancing.

The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently. The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing. / Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt. Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning. As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing. In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation. In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed. A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.