L'objectif principal de cette thèse est de mettre en évidence le rôle de la direction et du mouvement de la tête et des yeux dans la perception et le contrôle de trajectoires courbes, en référence aux propriétés des flux optiques générés par notre déplacement dans un environnement stable. Nous utilisons deux méthodes expérimentales : une approche comportementale sur simulateur de conduite et une approche psychophysique permettant d'évaluer les capacités d'observateurs humains à percevoir la direction du mouvement propre. Ces méthodes combinées visent à mettre en évidence les effets comportementaux d'une perception active de la direction du mouvement propre. L'introduction dresse l'état de la recherche sur les informations disponibles et les stratégies perceptives impliquées dans la prise de virage en conduite automobile. Ainsi, l'accent est à la fois mis sur le rôle du point de corde (dans le cas étudié d'un déplacement sur une route délimitée) et plus généralement sur le rôle du flux optique (description de la transformation apparente de l'environnement visuel lors du déplacement), soulignant notre capacité à interpréter spatialement le mouvement, mais aussi le caractère indissociable de la motricité et de la perception. Nous abordons ensuite le rôle des mouvements combinés des yeux et de la tête, dans une perspective fonctionnelle du contrôle du mouvement.Dans un premier chapitre expérimental, nous analysons les mouvements d'orientation de la tête lors de la prise de virage en conduite simulée. Nous montrons que les mouvements de la tête sont indépendants de la manipulation du volant et qu'ils participent activement à l'orientation du regard vers le point de corde. Dans un second chapitre expérimental, nous nous attachons à décrire les mouvements combinés des yeux et de la tête, en lien avec la géométrie de l'environnement routier. Dans une troisième partie, nous analysons plus finement le comportement du regard en lien avec la direction du point de corde et la vitesse locale du flux optique. Nous montrons à la fois que le point de corde correspond à un minimum local de vitesse optique et que la composante globale du flux optique induit un nystagmus optocinétique systématique. Enfin, lors d'une quatrième étude psychophysique, nous nous attachons à décrire finement l'effet de la variation de la direction du regard sur la discrimination de la direction du mouvement propre. Nous montrons que les seuils de discrimination de trajectoire sont minimaux lorsque le regard est orienté vers une zone de vitesse de flux minimal. Nous proposons finalement un modèle de détection de la trajectoire, basé sur une fraction de Weber des vitesses de flux fovéales, qui prédit très précisément les seuils expérimentaux. Les stratégies observées d'orientation du regard (combinaison des mouvements des yeux et de la tête) vers le point de corde sont compatibles avec une sélection optimale de l'information présente dans le flux optique. / The main purpose of this dissertation is to determine the role of the direction and movement of the eyes and the head in the perception and control of self-motion in curved trajectories, with respect to the properties of the optical flows generated in a stable environment. To do so, we used two experimental methods: a psychophysical approach which allows to assess human observers' ability to perceive the direction of self-motion; and a behavior-based approach on a driving simulator. The two methods combined should help to highlight active perception of self-motion.The introduction reviews the current knowledge of perceptuo-motor strategies during curve driving. In this context, we put a stress on both (1.) the particular role of the tangent point -- in the driving situation on a delimited road, and on the role of the optic flow in general (apparent transformation of the optic array during self-motion), emphasizing the capability of humans to spatially interpret the movement; and (2.) on the duality between movement and perception. We then address the role of head-and-eye combined movements, in a functional perspective of the control of self-motion. In a first experimental section, we analyze the oriented movements of the head in simulated curve driving. We demonstrate that head movements are independent from the handling of the steering wheel, and that they actively participate in the gaze orientation toward the tangent point.In a second experimental section, we set out to describe the combined movements of head and eyes, with respect to the geometry of the road environment. In a third section, we analyze in more details gaze behavior as a function of the tangent point direction and of the local speed of optical flow. We demonstrate that the tangent point corresponds to a local minimum of optic flow speed and that the global component of the optic flow induces a systematic optokinetic nystagmus. In a fourth section involving a psychophysical study, we scrutinize the effect of varying gaze direction on the discrimination of the direction of self-motion. We show that the trajectory discrimination thresholds are minimal when the gaze is oriented toward an area of minimum flow speed. We finally propose a model of trajectory change detection, relying on a Weber fraction of foveal flow speeds, predicting the experimental thresholds very precisely. The gaze orientation strategies we have observed (combination of head and eye movements) toward the tangent point are compatible with this model and with the hypothesis of an active an optimal selection of the information contained in the optical flow.

The interest for marine research and exploration has increased rapidly during the past decades and autonomous underwater vehicles (AUV) have been found useful in an increased amount of applications. The demand for versatile platform AUVs, able to perform a wide range of tasks, has become apparent. A vital part of an AUV is its motion control system, and an emerging problem for multipurpose AUVs is that the control performance is affected when the vehicle is configured with different payloads for each mission. Instead of having to manually re-tune the control system between missions, a method for automatic tuning of the control system has been developed in this master’s thesis. A model-based approach was implemented, where the current vehicle dynamics are identified by performing a sequence of excitation maneuvers, generating informative data. The data is used to estimate model parameters in predetermined model structures, and model-based control design is then used to determine an appropriate tuning of the control system. The performance and potential of the suggested approach were evaluated in simulation examples which show that improved control can be obtained by using the developed auto-tuning method. The results are considered to be sufficiently promising to justify implementation and further testing on a real AUV. The automatic tuning process is performed prior to a mission and is meant to compensate for dynamic changes introduced between separate missions. However, the AUV dynamics might also change during a mission which requires an adaptive control system. By using the developed automatic tuning process as foundation, the first steps towards an indirect adaptive control approach have been suggested. Also, the AUV which was studied in the thesis composed another interesting control problem by being overactuated in yaw control, this because yawing could be achieved by using rudders but also by differential drive of the propellers. As an additional and separate part of the thesis, an approach for using both techniques simultaneously have been proposed.

Este trabalho propõe investigar, desenvolver e validar uma metodologia de projeto para sistemas de diagnóstico para detecção de falhas e anomalias em aplicações de acionamento de motores elétricos, comumente utilizados na indústria de manufatura. A metodologia proposta é baseada na coleta e interpretação de dados de processo de redes PROFINET, perfil PROFIdrive, e ferramentas de aprendizagem de máquinas. Técnicas de extração e redução de atributos são aplicadas nos dados de processo coletados. Estes atributos são utilizados em algoritmos para reconhecimento de padrões, os algoritmos investigados são o k-Nearest Neighbor, Redes Neurais Artificiais, Support Vector Machines, e adicionalmente uma adaptação da metodologia é feita utilizando um algoritmo para detecção de novidades. A avaliação da metodologia considerou quatro cenários para estudos de caso, para falhas comuns em aplicações de máquinas rotativas. Os resultados alcançados demonstram a eficácia da metodologia, que foi capaz de detectar as falhas e anomalias investigadas de maneira satisfatória, similares a trabalhos correlatos, com o diferencial de não exigirem sensores adicionais dedicados na coleta de dados. Desta maneira, o trabalho contribui para área de redes de comunicação industrial, mais especificamente o protocolo PROFINET, diagnósticos de anomalias em máquinas acionadas por motores elétricos, e ferramentas de aprendizagem de máquinas. / This work proposes to investigate, develop and validate a methodology to design diagnostic systems to detect faults and anomalies in motion control applications, commonly used in manufacturing industry. The proposed methodology is based on collection and interpretation of process data from PROFINET networks, PROFIdrive profile, and machine learning tools. Feature extraction and selection techniques are applied to the collected process data. These features are used in algorithms for pattern recognition problems. Investigated algorithms are k-Nearest Neighbor, Artificial Neural Networks, Support Vector Machines and in addition, an adaptation of the methodology is held for novelty detection. Four scenarios were considered as case of studies for methodology evaluation, based on common faults in rotating machine applications. The results proved the methodology effectiveness for diagnostic system design, which were able to detect satisfactorily the investigated faults and anomalies, similar to related work, with the differential of not requiring additional dedicated sensors for data collection. In this way, the work contributes to the area of industrial communication networks, more specifically in PROFINET protocol, diagnostic systems for fault detection in motion control applications, and machine learning tools.

A mechatronic chewing robot of 6-DOF mechanism which consists mainly of the skull, six crank actuators, end effector and motion control system has been designed and is required to simulate human chewing behaviours while the chewed food properties are evaluated. The robotic mechanism is proposed and its kinematic parameters are defined according to the biomechanical findings and measurements of the human masticatory system. This thesis is concerned with the design and implementation of trajectory tracking control for robotic jaw actuators via Galil motion controller. The aim of this project is to simulate the dynamics behaviour and force-motion control of the robot, and to quantitatively assess food texture changes during chewing. A control system based Galil motion control card has been formed to achieve the motion of simulated human mastication. Some real human mastication motion have been tracked and used as targeted trajectories for the robot to reproduce. Several experiments have been executed to measure the jaw movements and chewing forces. To reduce the vibration of the actuators and protect sensitive linkage part of the robot, the traditional PID control and some advanced control theories were implemented to achieve most effective efforts. A mathematical model was also designed at the first stage when a test actuator powered by brushless motor was formed; however, it is finally proven not well controlled in either mechanical and control ways. Major features of the built robot including the motion control system are presented and tested. Experimental results including free chewing, soft-food and hard-food chewing are given where the foods are simulated by foam and hard objects. Also the joint actuations and driving torques required are compared for the chewing of different foods. In conclusion, tracking motion control has been attempted on the physical robot and a solution to the trajectory control has been developed.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

&ldquo / Numerically Controlled&rdquo / (NC) machine tools, which are automatically operated by encoded (digital) commands, are capable of machining components with quality and quantity. Manufacturing industry heavily depends on these machines. Many different control architectures have been adapted in today&rsquo / s CNC technology. Centralized control system is quite popular in industry due to its ease of implementation. If the number of controlled axes on a CNC machine tool (&gt / 3), increases so does the computational burden on the central processors. Hence, more powerful processors are needed. An alternative architecture, which is not commonly used in CNC technology, is the decentralized (distributed) control. In this topology, the tasks handled by the distributed controllers that are interconnected to each other by a communication network. As the need arises, a new controller can be added easily to the network without augmenting the physical configuration. Despite its attractive features, this architecture has not been fully embraced by the CNC industry. Synchronization among the axes in the coordinated motion is proven to be quite challenging. In this thesis, alternative distributed controller architecture was proposed for CNC machine tools. It was implemented on a 3-axis CNC milling machine. Open-loop control performance was investigated under various conditions. Different communication protocols along with different physical communication interfaces and a number of controller hardware were devised. An industry-standard network (RS-485) was set up by interconnecting these distributed controllers. Different data transmission protocols were devised in order to establish appropriate communication methods. Also, computer software (a.k.a. graphical user interface), which can coordinate the interconnected controllers, interpret NC part programs and generate reference position data for each axis, was designed within the scope of this thesis.

This thesis focuses on the design of an electrical drive system for the purpose of high precision motion control. A modern electrical drive is usually equipped with a current regulated voltage source along with powerful motion controller system utilizing one or more micro-controllers and/or digital signal processors (DSPs). That is, the motor drive control is mostly performed by a dedicated digital-motion controller system. Such a motor drive mostly interfaces with its host processor via various serial communication protocols such as Profibus, CAN+, RS-485 etc. for the purpose of receiving commands and sending out important status/control signals. Considering that the motor drives lie at the heart of every (multi-axis) motion control system, the aim of this thesis is to explore the design and implementation of a conventional DC motor drive system suitable for most industrial applications that require precision and accuracy. To achieve this goal, various underlying control concepts and important implementation details are rigorously investigated in this study. A low power DC motor drive system with a power module, a current regulator and a motion controller is built and tested. Several design revisions on these subsystems are made so as to improve the overall performance of the drive system itself. Consequently, important &ldquo / know-how&rdquo / required for building high performance (and high power) DC motor drives is gained in this research.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The fairly recent introduction of low-cost depth sensors such as Microsoft’s Xbox Kinect has encouraged a large amount of research on the use of depth sensors for many common Computer Vision problems. Depth images are advantageous over normal colour images because of how easily objects in a scene can be segregated in real-time. Microsoft used the depth images from the Kinect to successfully separate multiple users and track various larger body joints, but has difficulty tracking smaller joints such as those of the fingers. This is a result of the low resolution and noisy nature of the depth images produced by the Kinect. The objective of this project is to use the depth images produced by the Kinect to remotely track the user’s hands and to recognise the static hand poses in real-time. Such a system would make it possible to control an electronic device from a distance without the use of a remote control. It can be used to control computer systems during computer aided presentations, translate sign language and to provide more hygienic control devices in clean rooms such as operating theatres and electronic laboratories. The proposed system uses the open-source OpenNI framework to retrieve the depth images from the Kinect and to track the user’s hands. Random Decision Forests are trained using computer generated depth images of various hand poses and used to classify the hand regions from a depth image. The region images are processed using a Mean-Shift based joint estimator to find the 3D joint coordinates. These coordinates are finally used to classify the static hand pose using a Support Vector Machine trained using the libSVM library. The system achieves a final accuracy of 95.61% when tested against synthetic data and 81.35% when tested against real world data. / AFRIKAANSE OPSOMMING: Die onlangse bekendstelling van lae-koste diepte sensors soos Microsoft se Xbox Kinect het groot belangstelling opgewek in navorsing oor die gebruik van die diepte sensors vir algemene Rekenaarvisie probleme. Diepte beelde maak dit baie eenvoudig om intyds verskillende voorwerpe in ’n toneel van mekaar te skei. Microsoft het diepte beelde van die Kinect gebruik om verskeie persone en hul ledemate suksesvol te volg. Dit kan egter nie kleiner ledemate soos die vingers volg nie as gevolg van die lae resolusie en voorkoms van geraas in die beelde. Die doel van hierdie projek is om die diepte beelde (verkry vanaf die Kinect) te gebruik om intyds ’n gebruiker se hande te volg oor ’n afstand en die statiese handgebare te herken. So ’n stelsel sal dit moontlik maak om elektroniese toestelle oor ’n afstand te kan beheer sonder die gebruik van ’n afstandsbeheerder. Dit kan gebruik word om rekenaarstelsels te beheer gedurende rekenaargesteunde aanbiedings, vir die vertaling van vingertaal en kan ook gebruik word as higiëniese, tasvrye beheer toestelle in skoonkamers soos operasieteaters en elektroniese laboratoriums. Die voorgestelde stelsel maak gebruik van die oopbron OpenNI raamwerk om die diepte beelde vanaf die Kinect te lees en die gebruiker se hande te volg. Lukrake Besluitnemingswoude ("Random Decision Forests") is opgelei met behulp van rekenaar gegenereerde diepte beelde van verskeie handgebare en word gebruik om die verskeie handdele vanaf ’n diepte beeld te klassifiseer. Die 3D koördinate van die hand ledemate word dan verkry deur gebruik te maak van ’n Gemiddelde-Afset gebaseerde ledemaat herkenner. Hierdie koördinate word dan gebruik om die statiese handgebaar te klassifiseer met behulp van ’n Steun-Vektor Masjien ("Support Vector Machine"), opgelei met behulp van die libSVM biblioteek. Die stelsel behaal ’n finale akkuraatheid van 95.61% wanneer dit getoets word teen sintetiese data en 81.35% wanneer getoets word teen werklike data.

Dans cette thèse, nous proposons de nouvelles representations pourles poses du mouvement humain, apprises sur des données réelles, envue d’une synthèse de nouveaux mouvements en temps-réel. Dans unepremière partie, nous exploitons une méthode statistique adaptée auxgroupes de Lie (Analyse en Géodésiques Principales, AGP) pour approximerla variété des poses d’un sujet en mouvement, à partir de donnéesde capture de mouvement. Nous proposons un algorithme de cinématiqueinverse exploitant cette paramétrisation réduite, permettantpar construction de synthétiser des poses proches des données initiales.Nous validons ce modèle cinématique par une application à la compressionde données de mouvements, dans laquelle seules quelques trajectoiresdes extrémités des membres du squelettes permettent de reconstruireune bonne approximation de l’ensemble des données initiales.Dans une deuxième partie, nous étendons cette approche à l’animationphysique de personnages virtuels. La paramétrisation réduitepar AGP fournit les coordonnées généralisées de la formulation Lagrangiennede la mécanique. Nous dérivons un intégrateur temporelexplicite basé sur les intégrateurs variationnels. Afin d’en améliorer lastabilité, nous proposons un modèle d’amortissement inspiré de l’algorithmede Levenberg-Marquardt. Nous présentons également une méthodegéométrique d’apprentissage des limites angulaires sur des donnéesde capture de mouvement, ainsi que leur application comme contraintescinématiques.Dans une troisième partie, nous abordons le problème du contrôledu mouvement. En formulant les étapes de la simulation physique d’unepart, et de la cinématique inverse d’autre part comme deux programmesquadratiques, nous proposons un algorithme de pseudo-contrôle parinterpolation des métriques, permettant un compromis intuitif entre simulationphysique non-contrôlée, et cinématique inverse. Cette approchefaisant intervenir des forces externes, nous proposons une formulationalternative, utilisant uniquement les forces associées à la paramétrisationréduite des poses. Cette formulation est obtenue par relaxationdu problème théorique de contrôle sous contraintes unilatérales, nonconvexe,en un programme quadratique convexe. Ces algorithmes sontévalués sur des contrôleurs d’équilibre et de suivi. / In this thesis, we propose novel, data-driven representations for humanposes, suitable for real-time synthesis of novel character motion. Inthe first part, we exploit Lie group statistical analysis techniques (PrincipalGeodesic Analysis, PGA) to approximate the pose manifold of amotion capture sequence by a reduced set of pose geodesics. We proposean inverse kinematics algorithm using this reduced parametrizationto automatically produce poses that are close to the learning set. Wedemonstrate the efficiency of the resulting pose model by an applicationto motion capture data compression, where only a few end-effector trajectoriesare used to recover a good approximation of the initial data.In the second part, we extend this approach to the physically-basedanimation of virtual characters. The PGA-reduced parametrization providesgeneralized coordinates in a Lagrangian formulation of mechanics.We derive an explicit time integrator by approximating existingvariational integrators, and propose a damping model based on theLevenberg-Marquardt algorithm. We also describe a geometric, datadriven,angular limit learning algorithm, and the associated kinematicconstraints.In the third part, we reach the problem of task-space motion control.By formulating both physical simulation and inverse kinematicstime stepping schemes as two quadratic programs, we propose a simplepseudo-control algorithm that interpolates between the two metrics.This allows for an intuitive trade-off between uncontrolled simulationand kinematic manipulation. Since this approach makes use of externalforces, we propose an alternate formulation using only the generalizedforces associated to the pose parametrization. A control algorithmis obtained by the relaxation of the exact, non-convex control problemunder unilateral constraints, into a convex quadratic program. Thesealgorithms are evaluated on simple balance and tracking controllers.