Le sujet principal de cette thèse est le mouvement des systèmes anthropomorphes, et plus particulièrement la locomotion bipède des humains et des robots humanoïdes. Pour caractériser et comprendre la locomotion bipède, il est instructif d'en étudier les causes, qui résident dans le contrôle et l'organisation du mouvement, et les conséquences qui en résultent, que sont le mouvement et les interactions physiques avec l'environnement. Concernant les causes, par exemple, quels sont les principes qui régissent l'organisation des ordres moteurs pour élaborer une stratégie de déplacement spécifique ? Puis, quelles grandeurs physiques pouvons-nous calculer pour décrire au mieux le mouvement résultant de ces commandes motrices ? Ces questions sont en partie abordées par la proposition d'une extension mathématique de l'approche du Uncontrolled Manifold au contrôle moteur de tâches dynamiques, puis par la présentation d'un nouveau descripteur de la locomotion anthropomorphe. En lien avec ce travail analytique vient le problème de l'estimation de l'état pour les systèmes anthropomorphes. La difficulté d'un tel problème vient du fait que les mesures apportent un bruit qui n'est pas toujours séparable des données informatives, et que l'état du système n'est pas nécessairement observable. Pour se débarrasser du bruit, des techniques de filtrage classiques peuvent être employées, mais elles sont susceptibles d'altérer le contenu des signaux d'intérêt. Pour faire face à ce problème, nous présentons une méthode récursive, basée sur le filtrage complémentaire, pour estimer la position du centre de masse et la variation du moment cinétique d'un système en contact, deux quantités centrales de la locomotion bipède. Une autre idée pour se débarrasser du bruit de mesure est de réaliser qu'il résulte en une estimation irréaliste de la dynamique du système. En exploitant les équations du mouvement, qui dictent la dynamique temporelle du système, et en estimant une trajectoire plutôt qu'un point unique, nous présentons ensuite une estimation du maximum de vraisemblance en utilisant l'algorithme de programmation différentielle dynamique pour effectuer une estimation optimale de l'état centroidal des systèmes en contact. Finalement, une réflexion pluridisciplinaire est présentée, sur le rôle fonctionnel et computationnel joué par la tête chez les animaux. La pertinence de son utilisation en robotique mobile y est discutée, pour l'estimation d'état et la perception multisensorielle. / The substantive subject of this thesis is the motion of anthropomorphic systems, and more particularly the bipedal locomotion of humans and humanoid robots. To characterize and understand bipedal locomotion, it is instructive to study its motor causes and its resulting physical consequences, namely, the interactions with the environment. Concerning the causes, for instance, what are the principles that govern the organization of motor orders in humans for elaborating a specific displacement strategy? And then, which physical quantities can we compute for best describing the motion resulting from these motor orders ? These questions are in part addressed by the proposal of a mathematical extension of the Uncontrolled Manifold approach for the motor control of dynamic tasks and through the presentation of a new descriptor of anthropomorphic locomotion. In connection with this analytical work, comes the problem of state estimation in anthropomorphic systems. The difficulty of such a problem comes from the fact that the measurements carry noise which is not always separable from the informative data, and that the state of the system is not necessarily observable. To get rid of the noise, classical filtering techniques can be employed but they are likely to distort the signals. To cope with this issue, we present a recursive method, based on complementary filtering, to estimate the position of the center of mass and the angular momentum variation of the human body, two central quantities of human locomotion. Another idea to get rid of the measurements noise is to acknowledge the fact that it results in an unrealistic estimation of the motion dynamics. By exploiting the equations of motion, which dictate the temporal dynamics of the system, and by estimating a trajectory versus a single point, we then present maximum likelihood estimation using the dynamic differential programming algorithm to perform optimal centroidal state estimation for systems in contact. Finally, a multidisciplinary reflection on the functional and computational role played by the head in animals is presented. The relevance of using this solution in mobile robotics is discussed, particularly for state estimation and multisensory perception.

In this thesis we have investigated different numerical filters for joint input and state estimation, with the aim of designing a robust algorithm capable of monitoring the continuous motion and loading in a truck chassis. The algorithm has to be able to use sparse measurements of the motion on different parts of the truck as it is excited by road induced vibrations, and transform this data into knowledge of the state in the entire system. To do this, the algorithm has to be supplied with information about the dynamic properties of the current system. In Paper A we have developed and implemented a fixed-lag smoother for joint input and state estimation in linear time-invariant dynamic structures. A fixed-lag smoother maximizes the use of information available in the measurements by allowing a small time lag in the estimation. As input, external forces as well as support motions can be computed. Furthermore, both measurement noise and model errors are accounted for and simulated as stochastic processes. The filter is firstly verified with straightforward numerical simulations of a simply supported beam, followed by a more involved simulation of a truck fuel tank. It is shown that the fixed-lag smoother performs very well, it estimates both input and states with a high accuracy even though the signals are contaminated with noise and the model contains errors. In Paper B the fixed-lag smoother is applied on real measurements. We investigate the capabilities of the proposed filter by analysing acceleration measurements from a truck side skirt excited by road induced vibrations. In this study, we focus on estimating the state in the side skirt body from a minimum number of measurement sensors. The dynamic properties of the side skirt are obtained experimentally from an operational modal analysis. It is shown that the fixed-lag smoother estimates the state very well. The results also shows that the smoothing effect is larger when fewer measurement sensors are used. / <p>QC 20160928</p>

In vehicle control practice, there are some variables, such as lateral tire force, body slip angle and yaw rate, that cannot or is hard to be measured directly and accurately. Vehicle model, like the bicycle model, offers an alternative way to get them indirectly, however due to the widely existent simplification and inaccuracy of vehicle models, there are always biases and errors in prediction from them. When developing advanced vehicle control functions, it is necessary and significant to know these variables in relatively high precision. Kalman filter offers a choice to estimate these variables accurately with measurable variables and with vehicle model together. In this thesis, estimation models based on Extended Kalman Filter (EKF) and Uncented Kalman Filter (UKF) are built separately to evaluate the lateral tire force, body slip angel and yaw rate of two typical passenger vehicles. Matlab toolbox EKF/UKF developed by Simo Särkkä, et al. is used to implement the estimation models. By comparing their principle, algorithm and results, the better one for vehicle state estimation will be chosen and justified. The thesis is organized in the following 4 parts: First, EKF and UKF are studied from their theory and features. Second, vehicle model used for prediction in Kalman filter is build and justified. Third, algorithms of EKF and UKF for this specific case are analysed. EKF and UKF are then implemented based on the algorithms with the help of Matlab toolbox EKF/UKF. Finally, comparisons between EKF and UKF are presented and discussed.

Au sein des systèmes avancés d’aide à la conduite (Advanced Driver Assistance Systems - ADAS) pour les systèmes de transport intelligents (Intelligent Transport Systems - ITS), les systèmes de positionnement, ou de localisation, du véhicule jouent un rôle primordial. Le système GPS (Global Positioning System) largement employé ne peut donner seul un résultat précis à cause de facteurs extérieurs comme un environnement contraint ou l’affaiblissement des signaux. Ces erreurs peuvent être en partie corrigées en fusionnant les données GPS avec des informations supplémentaires provenant d'autres capteurs. La multiplication des systèmes d’aide à la conduite disponibles dans les véhicules nécessite de plus en plus de capteurs installés et augmente le volume de données utilisables. Dans ce cadre, nous nous sommes intéressés à la fusion des données provenant de capteurs bas cout pour améliorer le positionnement du véhicule. Parmi ces sources d’information, en parallèle au GPS, nous avons considérés les caméras disponibles sur les véhicules dans le but de faire de l’odométrie visuelle (Visual Odometry - VO), couplée à une carte de l’environnement. Nous avons étudié les caractéristiques de cette trajectoire reconstituée dans le but d’améliorer la qualité du positionnement latéral et longitudinal du véhicule sur la route, et de détecter les changements de voies possibles. Après avoir été fusionnée avec les données GPS, cette trajectoire générée est couplée avec la carte de l’environnement provenant d’Open-StreetMap (OSM). L'erreur de positionnement latérale est réduite en utilisant les informations de distribution de voie fournies par OSM, tandis que le positionnement longitudinal est optimisé avec une correspondance de courbes entre la trajectoire provenant de l’odométrie visuelle et les routes segmentées décrites dans OSM. Pour vérifier la robustesse du système, la méthode a été validée avec des jeux de données KITTI en considérant des données GPS bruitées par des modèles de bruits usuels. Plusieurs méthodes d’odométrie visuelle ont été utilisées pour comparer l’influence de la méthode sur le niveau d'amélioration du résultat après fusion des données. En utilisant la technique d’appariement des courbes que nous proposons, la précision du positionnement connait une amélioration significative, en particulier pour l’erreur longitudinale. Les performances de localisation sont comparables à celles des techniques SLAM (Simultaneous Localization And Mapping), corrigeant l’erreur d’orientation initiale provenant de l’odométrie visuelle. Nous avons ensuite employé la trajectoire provenant de l’odométrie visuelle dans le cadre de la détection de changement de voie. Cette indication est utile dans pour les systèmes de navigation des véhicules. La détection de changement de voie a été réalisée par une somme cumulative et une technique d’ajustement de courbe et obtient de très bon taux de réussite. Des perspectives de recherche sur la stratégie de détection sont proposées pour déterminer la voie initiale du véhicule. En conclusion, les résultats obtenus lors de ces travaux montrent l’intérêt de l’utilisation de la trajectoire provenant de l’odométrie visuelle comme source d’information pour la fusion de données à faible coût pour la localisation des véhicules. Cette source d’information provenant de la caméra est complémentaire aux données d’images traitées qui pourront par ailleurs être utilisées pour les différentes taches visée par les systèmes d’aides à la conduite. / With the growing research on Advanced Driver Assistance Systems (ADAS) for Intelligent Transport Systems (ITS), accurate vehicle localization plays an important role in intelligent vehicles. The Global Positioning System (GPS) has been widely used but its accuracy deteriorates and susceptible to positioning error due to factors such as the restricting environments that results in signal weakening. This problem can be addressed by integrating the GPS data with additional information from other sensors. Meanwhile, nowadays, we can find vehicles equipped with sensors for ADAS applications. In this research, fusion of GPS with visual odometry (VO) and digital map is proposed as a solution to localization improvement with low-cost data fusion. From the published works on VO, it is interesting to know how the generated trajectory can further improve vehicle localization. By integrating the VO output with GPS and OpenStreetMap (OSM) data, estimates of vehicle position on the map can be obtained. The lateral positioning error is reduced by utilizing lane distribution information provided by OSM while the longitudinal positioning is optimized with curve matching between VO trajectory trail and segmented roads. To observe the system robustness, the method was validated with KITTI datasets tested with different common GPS noise. Several published VO methods were also used to compare improvement level after data fusion. Validation results show that the positioning accuracy achieved significant improvement especially for the longitudinal error with curve matching technique. The localization performance is on par with Simultaneous Localization and Mapping (SLAM) SLAM techniques despite the drift in VO trajectory input. The research on employability of VO trajectory is extended for a deterministic task in lane-change detection. This is to assist the routing service for lane-level direction in navigation. The lane-change detection was conducted by CUSUM and curve fitting technique that resulted in 100% successful detection for stereo VO. Further study for the detection strategy is however required to obtain the current true lane of the vehicle for lane-level accurate localization. With the results obtained from the proposed low-cost data fusion for localization, we see a bright prospect of utilizing VO trajectory with information from OSM to improve the performance. In addition to obtain VO trajectory, the camera mounted on the vehicle can also be used for other image processing applications to complement the system. This research will continue to develop with future works concluded in the last chapter of this thesis.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20024号 / 情博第619号 / 新制||情||108(附属図書館) / 33120 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 松山 隆司, 教授 熊田 孝恒, 准教授 川嶋 宏彰 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different I testeeders and the results obtained are justified.

Large scale outages have occurred worldwide in recent decades with some impacting 15-25% of a nation’s population. The complexity of blackouts has been extensively studied but many questions remain. As there are no perfect solutions to prevent blackouts, usually caused by a complex sequence of cascading events, a number of different measures need to be undertaken to minimize impact of future disturbances. Increase in deployment of phasor measurement units (PMUs) across the grid has given power industry an unprecedented technology to study dynamic behavior of the system in real time. Integration of large scale synchronized measurements with SCADA system requires a careful roadmap and methodology. When properly engineered, tested, and implemented, information extracted from synchrophasor data streams provides realtime observability for transmission system. Synchrophasor data can provide operators with quick insight into precursors of blackout (e.g., angular divergence) which are unavailable in traditional SCADA systems. Current visualization tools and SE functions, supported by SCADA, provide some basic monitoring. Inaccuracies in measurements and system models, absence of redundancy in the measured parameters or breaker statuses in most cases, and lack of synchronization and time resolution in SCADA data result in limited functionality and precision for a typical EMS required in today’s operating environment of tighter margins that require more frequent and more precise data. Addition of synchrophasor data, typically having several orders of magnitude higher temporal resolution, (i.e., 60 to 120 measurements per second as opposed to one measurement every 4 to 8 seconds), can help detect higher speed phenomena and system oscillations. Also, time synchronization to one micro-second allows for accurate comparison of phase angles across the grid and identification of major disturbances and islanding. This dissertation proposes a more comprehensive, holistic set of criteria for optimizing PMU placement with consideration for diverse factors that can influence PMU siting decision-making process and incorporates several practical implementation aspects. An innovative approach to interoperability testing is presented and solutions are offered to address the challenges. The proposed methodology is tested to prove the concept and address real-life implementation challenges, such as interoperability among the PMUs located across a large area.

Battery Electric Vehicles (BEV) are gaining market share but still must overcome several engineering challenges related to the lithium-ion battery packs powering them. The batteries must be carefully managed to optimize safety and performance. The estimation of battery states, which cannot be measured directly, is an important part of battery management and remains an active area of research since small gains in estimation accuracy can help reduce cost and increase BEV range. This thesis presents several improvements to battery state estimation using different methods. Electrochemical Impedance Spectroscopy (EIS) is receiving increased attention from researchers as a method for state estimation and diagnostics for real-time applications. Due to battery relaxation behaviour, long rest times are commonly used before performing the EIS measurement. In this work, methods were developed to significantly shorten the required rest times, and a State of Health (SoH) estimation strategy was proposed by taking advantage of the relaxation effect as measured by EIS. This method was demonstrated to have an estimation error of below 1%. At low temperatures, the accuracy of the battery model becomes poor due to the non-linear battery response to current. By using an adaptive filter called the Interacting Multiple Model (IMM) filter, the next part of this work showed how to significantly improve low temperature State of Charge (SoC) estimation. Further reduction in estimation errors was achieved by pairing the IMM with the Smooth Variable Structure Filter (SVSF), for SoC estimation errors below 2%. The work presented in this thesis also includes the application of Deep Neural Networks (DNN) for SoC estimation from EIS data. Finally, an extensive aging study was conducted and an accelerated protocol was compared to a realistic drive cycle based protocol using EIS as a characterization tool. / Thesis / Doctor of Philosophy (PhD) / Replacing conventional gasoline/diesel powered cars with battery powered vehicles is part of a solution to the climate crisis. However, the initial costs paired with range anxiety stops many from switching to electric cars. Both cost and range are related to the battery pack. To achieve the best possible range for the lowest possible cost, battery packs must be carefully controlled by sophisticated algorithms. Unfortunately, battery range or health cannot be measured directly, but must be inferred through measurable indicators. This thesis explores battery behavior under different operating conditions and develops improved methods which can be used to determine battery health and/or range. A powerful method usually used only in laboratory settings is studied and improved to make it more suitable for implementation in electric cars. In this work it is used for accurate battery health determination. Furthermore, a strategy for improving battery range determination at low temperatures is also proposed.