The area of systems biology evolved in an attempt to introduce mathematical systems theory principles in biology. Although we believe that all biological processes are essentially chemical reactions, describing those using precise mathematical rules is not easy, primarily due to the complexity and enormity of biological systems. Here we introduce a formal approach for modeling biological dynamical relationships and diseases such as cancer. The immediate motivation behind this research is the urgency to find a practicable cure of cancer, the emperor of all maladies. Unlike other deadly endemic diseases such as plague, dengue and AIDS, cancer is characteristically heterogenic and hence requires a closer look into the genesis of the disease. The actual cause of cancer lies within our physiology. The process of cell division holds the clue to unravel the mysteries surrounding this disease. In normal scenario, all control mechanisms work in tandem and cell divides only when the division is required, for instance, to heal a wound platelet derived growth factor triggers cell division. The control mechanism is tightly regulated by several biochemical interactions commonly known as signal transduction pathways. However, from mathematical point of view, these pathways are marginal in nature and unable to cope with the multi-variability of a heterogenic disease like cancer. The present research is possibly one first attempt towards unraveling the mysteries surrounding the dynamics of a proliferating cell. A novel yet simple methodology is developed to bring all the marginal knowledge of the signaling pathways together to form the simplest mathematical abstract known as the Boolean Network. The malfunctioning in the cell by genetic mutations is formally modeled as stuck-at faults in the underlying Network. Finally a mathematical methodology is discovered to optimally find out the possible best combination drug therapy which can drive the cell from an undesirable condition of proliferation to a desirable condition of quiescence or apoptosis. Although, the complete biological validation was beyond the scope of the current research, the process of in-vitro validation has been already initiated by our collaborators. Once validated, this research will lead to a bright future in the field on personalized cancer therapy.

The main goal of this thesis is to develop practical digital controller architectures for multi-phase dc-dc converters utilized in low power (up to few hundred watts) and cost-sensitive applications. The proposed controllers are suitable for on-chip integration while being capable of providing advanced features, such as dynamic efficiency optimization, inductor current estimation, converter component identification, as well as combined dynamic current sharing and fast transient response. The first part of this thesis addresses challenges related to the practical implementation of digital controllers for low-power multi-phase dc-dc converters. As a possible solution, a multi-use high-frequency digital PWM controller IC that can regulate up to four switching converters (either interleaved or standalone) is presented. Due to its configurability, low current consumption (90.25 μA/MHz per phase), fault-tolerant work, and ability to operate at high switching frequencies (programmable, up to 10 MHz), the IC is suitable to control various dc-dc converters. The applications range from dc-dc converters used in miniature battery-powered electronic devices consuming a fraction of watt to multi-phase dedicated supplies for communication systems, consuming hundreds of watts. A controller for multi-phase converters with unequal current sharing is introduced and an efficiency optimization method based on logarithmic current sharing is proposed in the second part. By forcing converters to operate at their peak efficiencies and dynamically adjusting the number of active converter phases based on the output load current, a significant improvement in efficiency over the full range of operation is obtained (up to 25%). The stability and inductor current transition problems related to this mode of operation are also resolved. At last, two reconfigurable digital controller architectures with multi-parameter estimation are introduced. Both controllers eliminate the need for external analog current/temperature sensing circuits by accurately estimating phase inductor currents and identifying critical phase parameters such as equivalent resistances, inductances and output capacitance. A sensorless non-linear, average current-mode controller is introduced to provide fast transient response (under 5 μs), small voltage deviation and dynamic current sharing with multi-phase converters. To equalize the thermal stress of phase components, a conduction loss-based current sharing scheme is proposed and implemented.

Heutzutage gewinnt die Simulation von Gebäuden und Anlagen zunehmend an Bedeutung, um die Betriebsweise der Anlagen zu diagnostizieren bzw. zu bewerten und den Energiebedarf vorherzusagen. Dabei hängt die erzielte Genauigkeit von dem Kompliziertheitsgrad des angewendeten Simulationsprogramms ab. Deshalb ist Modellbildung und -validierung ein sehr wichtiger Bestandteil eines Softwareentwicklungsprozesses, um die Zuverlässigkeit zu sichern. Am Institut für Thermodynamik und Technische Gebäudeausrüstung liegen zahlreiche Simulationsmodelle vor. Im Rahmen dieser vorliegenden Arbeit wurden weitere benötigte Modelle (hygrisches Verhalten der Wände (vereinfachtes Verfahren), Rippenrohrwärmeüberträger, Wärmeregenerator et al.) entwickelt und in das Programm TRNSYS eingefügt sowie die vorhandenen Modelle an ihre Genauigkeit angepasst. Insbesondere sind dies die Modelle für Splitsysteme bei stetiger und nichtstetiger Regelung mit der detaillierten Betrachtung des Anlagenverhaltens sowohl beim Voll- als auch beim Teillastbetrieb. Damit ist es erstmals gelungen, das gesamte Anlagensystem der Splittechnik ausführlich zu beschreiben. Um die analytische Validierung durchführen zu können, wurden die analytischen Modelle für eine Splitanlage bei stetiger und nichtstetiger Regelung unter den vordefinierten Randbedingungen entwickelt. Zur analytischen Validierung finden auch die vorhandenen Simulationsmodelle Anwendung, so dass sich die meisten Komponenten und das Simulationsprogramm TRNSYS verifizieren ließen. Diese Validierung erfolgte im Rahmen des IEA-SHC/HVAC BESTEST TASK 22. Da an diesem TASK verschiedene Forschungsinstitutionen mit jeweils unterschiedlichen Simulationsprogrammen teilnahmen, ergab sich die beste Möglichkeit, vergleichende Tests durchzuführen. Wenn dabei ein Programm signifikante Unterschiede zu den anderen liefert, liegt dies nicht immer an Programmfehlern. Aber kollektive Erfahrungen aus diesem TASK zeigen, dass bei Abweichungen meistens Fehler bzw. fragwürdige Algorithmen gefunden wurden. Nachdem das Simulationsprogramm TRNSYS validiert war, erfolgte die Erstellung eines Konzeptes zur Fehlererkennung und Diagnose der Regelstrategien von RLTA. Das Verfahren erlaubt sowohl die Beseitigung der möglichen Fehler in der Planungsphase beim Entwurf der Regelstrategien als auch den Test der vorhandenen Regelstrategien. Dies erhöht die Zuverlässigkeit und damit die Sicherheit beim Anlagenbetrieb. Schließlich dient das Verfahren als Werkzeug zur Optimierung der Betriebsweise von RLTA. Das Regelverhalten wurde anhand typischer Fälle vorgestellt und diskutiert. Mit Hilfe des Verfahrens zur Fehlererkennung und Diagnose der Betriebsweise von RLTA ließen sich vorhandene Regelstrategien testen und verbessern.

The main goal of this thesis is to develop practical digital controller architectures for multi-phase dc-dc converters utilized in low power (up to few hundred watts) and cost-sensitive applications. The proposed controllers are suitable for on-chip integration while being capable of providing advanced features, such as dynamic efficiency optimization, inductor current estimation, converter component identification, as well as combined dynamic current sharing and fast transient response. The first part of this thesis addresses challenges related to the practical implementation of digital controllers for low-power multi-phase dc-dc converters. As a possible solution, a multi-use high-frequency digital PWM controller IC that can regulate up to four switching converters (either interleaved or standalone) is presented. Due to its configurability, low current consumption (90.25 μA/MHz per phase), fault-tolerant work, and ability to operate at high switching frequencies (programmable, up to 10 MHz), the IC is suitable to control various dc-dc converters. The applications range from dc-dc converters used in miniature battery-powered electronic devices consuming a fraction of watt to multi-phase dedicated supplies for communication systems, consuming hundreds of watts. A controller for multi-phase converters with unequal current sharing is introduced and an efficiency optimization method based on logarithmic current sharing is proposed in the second part. By forcing converters to operate at their peak efficiencies and dynamically adjusting the number of active converter phases based on the output load current, a significant improvement in efficiency over the full range of operation is obtained (up to 25%). The stability and inductor current transition problems related to this mode of operation are also resolved. At last, two reconfigurable digital controller architectures with multi-parameter estimation are introduced. Both controllers eliminate the need for external analog current/temperature sensing circuits by accurately estimating phase inductor currents and identifying critical phase parameters such as equivalent resistances, inductances and output capacitance. A sensorless non-linear, average current-mode controller is introduced to provide fast transient response (under 5 μs), small voltage deviation and dynamic current sharing with multi-phase converters. To equalize the thermal stress of phase components, a conduction loss-based current sharing scheme is proposed and implemented.

This thesis presents the development of a fault detection and diagnosis (FDD) procedure capable of capturing, isolating and identifying multiple abrupt parametric faults. The proposed method relies on parameter estimation deployed in a set membership framework. This approach presupposes the utilization of a linearly parametrizable model and the a priori knowledge of bounded noise errors and parameter perturbations. Under these assumptions, a data-hyperspace is generated at every time instant. The goal of set membership identification (SMI) is the determination of the parametric set, formed as an orthotope or ellipsoid, within which the nominal parameter vector resides and intersects with the data-hyperspace. The fault detection mechanism is activated when the normal operation of the SMI procedure is interrupted due to an empty intersection of the data-hyperspace and the estimated parametric set. At the detection instant, a resetting procedure is performed in order to compute the parameter set and the data-hyperspace that contain the varied nominal parameter vector, allowing the SMI algorithm to continue its operation. During the fault isolation, consistency tests are executed, relying on the projections of the worst case parametric sets and the ones arisen from the normal operation of SMI. A faulty component is indicated when these projections do not intersect, while the distance of their centers is used for fault identification. In case of the ellipsoidal SMI-based FDD and under the assumption of a time invariant parameter vector, a new fault detection criterion is defined based on the intersection of support orthotopes of ellipsoids. A more accurate estimation of the time instant of fault occurrence is proposed based on the application of a backward-in-time procedure starting from the fault detection instant, while the conditions under which a fault will never be detected by the orthotopic and ellipsoidal SMI based FDD are provided. This dissertation explores the efficiency of the proposed FDD methodology for capturing failure modes of two microelectromechanical systems; an electrostatic parallel-plate microactuator and a torsionally resonant atomic force microscope. From an engineering point of view, failure modes appeared in the microcomponents of the microactuator and the TR-AFM are encountered as parameter variations and are captured, isolated and identified by the proposed FDD methodology. / Σε αυτή την διατριβή, παρουσιάζεται η ανάπτυξη μιας διαδικασίας Ανίχνευσης και Διάγνωσης Σφαλμάτων, η οποία είναι ικανή να εντοπίζει, απομονώνει και αναγνωρίζει πολλαπλά, απότομα παραμετρικά σφάλματα. H προτεινόμενη μέθοδος βασίζεται στην αναγνώριση του συνόλου συμμετοχής των παραμέτρων. Ο στόχος της Αναγνώρισης Συνόλου Συμμετοχής είναι ο καθορισμός του παραμετρικού συνόλου εντός του οποίου κείται το ονομαστικό διάνυσμα παραμέτρων, δεδομένου ότι το ονομαστικό διάνυσμα παραμέτρων ανήκει επίσης σε έναν υπερχώρο δεδομένων. Το παραμετρικό σύνολο απεικονίζεται ως ένα ορθότοπο ή ένα ελλειψοειδές, λόγω της εύκολης μαθηματικής τους περιγραφής. Έτσι, η διαδικασία Αναγνώρισης Συνόλου Συμμετοχής αντιστοιχεί σε ένα πρόβλημα βελτιστοποίησης, το οποίο αποσκοπεί στον υπολογισμό του ορθοτόπου ή ελλειψοειδούς το οποίο περιέχει το ονομαστικό διάνυσμα παραμέτρων και τέμνεται με τον υπερχώρο δεδομένων. Ο μηχανισμός Ανίχνευσης Σφαλμάτων ενεργοποιείται όταν διακόπτεται η φυσιολογική λειτουργία της Αναγνώρισης Συνόλου Συμμετοχής, λόγω της κενής τομής μεταξύ των εκτιμώμενου παραμετρικού συνόλου και του υπερχώρου δεδομένων. Τη χρονική στιγμή ανίχνευσης ενός σφάλματος, εφαρμόζεται μια διαδικασία επαναρύθμισης που σκοπεύει στον υπολογισμό του νέου παραμετρικού συνόλου, το οποίο περιέχει το μεταβεβλημένο ονομαστικό διάνυσμα παραμέτρων και τέμνεται με το υπερχώρο δεδομένων. Κατά τη διάρκεια της διαδικασίας απομόνωσης του σφάλματος, εκτελούνται τεστ συμβατότητας, τα οποία βασίζονται στις προβολές των νέων παραμετρικών συνόλων και στις προβολές των παραμετρικών συνόλων χείριστης περίπτωσης, ενώ η απόσταση των κέντρων των προβολών χρησιμοποιείται για αναγνώριση σφάλματος. Στην περίπτωση που η Ανίχνευση και Διάγνωση Σφαλμάτων πραγματοποιείται βασιζόμενη στην Αναγνώριση Συνόλου Συμμετοχής με ελλειψοειδή και θεωρώντας το ονομαστικό διάνυσμα παραμέτρων χρονικά αμετάβλητο, ορίζεται ένα νέο κριτήριο ανίχνευσης σφαλμάτων, χρησιμοποιώντας την τομή των περιβαλλόντων ορθοτόπων των ελλειψοειδών. Σε αυτή την περίπτωση, ένα σφάλμα ανιχνεύεται όταν η τομή αυτή είναι κενή. Ακόμη, προτείνεται μια πιο ακριβής εκτίμηση της χρονικής στιγμής εμφάνισης του σφάλματος, ενώ παρατίθενται οι συνθήκες υπό τις οποίες ένα σφάλμα μπορεί να μην ανιχνευθεί ποτέ με την εφαρμογή των προτεινόμενων μεθόδων. Η συγκεκριμένη διατριβή επίσης ερευνά την αποτελεσματικότητα της προτεινόμενης μεθοδολογίας Ανίχνευσης και Διάγνωσης Σφαλμάτων για τον εντοπισμό των τρόπων εκδήλωσης σφαλμάτων σε δύο μικροηλεκτρομηχανικά συστήματα (ΜΗΜΣ), έναν ηλεκτροστατικό μικροεπενεργητή παράλληλων πλακών και ένα ατομικό μικροσκόπιο συντονισμού στρέψης. Από πλευράς μηχανικής, οι τρόποι εκδήλωσης σφαλμάτων στα δομικά στοιχεία του μικροεπενεργητή ή του ατομικού μικροσκοποίου αντιμετωπίζονται ως απότομες παραμετρικές, οι οποίες εντοπίζονται και διαγιγνώσκονται από τις προτεινόμενες μεθόδους.

Cette thèse est consacrée à l'étude de diagnostic de pannes pour les systèmes pile à combustible de type PEMFC. Le but est d'améliorer la fiabilité et la durabilité de la membrane électrolyte polymère afin de promouvoir la commercialisation de la technologie des piles à combustible. Les approches explorées dans cette thèse sont celles du diagnostic guidé par les données. Les techniques basées sur la reconnaissance de forme sont les plus utilisées. Dans ce travail, les variables considérées sont les tensions des cellules. Les résultats établis dans le cadre de la thèse peuvent être regroupés en trois contributions principales.La première contribution est constituée d'une étude comparative. Plus précisément, plusieurs méthodes sont explorées puis comparées en vue de déterminer une stratégie précise et offrant un coût de calcul optimal.La deuxième contribution concerne le diagnostic online sans connaissance complète des défauts au préalable. Il s'agit d'une technique adaptative qui permet d'appréhender l'apparition de nouveaux types de défauts. Cette technique est fondée sur la méthodologie SSM-SVM et les règles de détection et de localisation ont été améliorées pour répondre au problème du diagnostic en temps réel.La troisième contribution est obtenue à partir méthodologie fondée sur l'utilisation partielle de modèles dynamiques. Le principe de détection et localisation de défauts est fondé sur des techniques d'identification et sur la génération de résidus directement à partir des données d'exploitation.Toutes les stratégies proposées dans le cadre de la thèse ont été testées à travers des données expérimentales et validées sur un système embarqué. / Aiming at improving the reliability and durability of Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems and promote the commercialization of fuel cell technologies, this thesis work is dedicated to the fault diagnosis study for PEMFC systems. Data-driven fault diagnosis is the main focus in this thesis. As a main branch of data-driven fault diagnosis, the methods based on pattern classification techniques are firstly studied. Taking individual fuel cell voltages as original diagnosis variables, several representative methodologies are investigated and compared from the perspective of online implementation.Specific to the defects of conventional classification based diagnosis methods, a novel diagnosis strategy is proposed. A new classifier named Sphere-Shaped Multi-class Support Vector Machine (SSM-SVM) and modified diagnostic rules are utilized to realize the novel fault recognition. While an incremental learning method is extended to achieve the online adaptation.Apart from the classification based diagnosis approach, a so-called partial model-based data-driven approach is introduced to handle PEMFC diagnosis in dynamic processes. With the aid of a subspace identification method (SIM), the model-based residual generation is designed directly from the normal and dynamic operating data. Then, fault detection and isolation are further realized by evaluating the generated residuals.The proposed diagnosis strategies have been verified using the experimental data which cover a set of representative faults and different PEMFC stacks. The preliminary online implementation results with an embedded system are also supplied.

Les travaux de recherches sur la commande, le diagnostic et la tolérance aux défauts appliqués aux drones deviennent de plus en plus populaires. Il est judicieux de concevoir des lois de commande qui garantissent la stabilité et les performances du drone, non seulement dans le cas nominal, mais également en présence de fortes perturbations et de défauts.Dans cette thèse, un nouvel algorithme bio-inspiré adapté pour la recherche de solutions dans des problèmes d’optimisation est développé. Cet algorithme est utilisé pour trouver les gains des différents contrôleurs conçus pour les drones. La commande par mode glissant est utilisée pour développer deux contrôleurs passifs tolérants aux défauts pour les quadrirotors: un contrôleur par mode glissant augmentée avec un intégrateur, et un contrôleur par mode glissant implémenté en cascade. Parce que les commandes passives ont une robustesse réduite, une commande active par mode glissant est développée. Pour traiter les défauts extrêmes, un contrôleur d’urgence basé sur la conversion du quadrirotor en trirotor est développé. Les commandes actives, passives, et le contrôleur d’urgences sont ensuite intégrés pour former un contrôleur tolérant aux défauts capable de gérer un grand nombre de défaillances tout en garantissant les ressources actionneur et en limitant la charge de calcul du processeur. Finalement, des contrôleurs tolérants aux défauts, actifs et passifs, basés sur des méthodes par mode glissant du premier et deuxième ordre sont développées pour les octorotors. La commande active utilise des méthodes d’allocation de contrôles pour redistribuer les efforts sur les actionneurs sains, réduisant ainsi l’effet du défaut. / Unmanned Aerial Vehicles (UAV) are more and more popular for their civil and military applications. Classical control laws usually show weaknesses in the presence of parameter uncertainties, environmental disturbances, and actuator and sensor faults. Therefore, it is judicious to design a control law capable of stabilizing the UAV not only in the fault-free nominal cases, but also in the presence of disturbances and faults. In this thesis, a new bio-inspired search algorithm called Ecological Systems Algorithm (ESA) suitable for engineering optimization problems is developed. The algorithm is used over the thesis to find optimal gains for the fault tolerant controllers. Sliding Mode Control theory is used to develop two Passive Fault Tolerant Controllers for quadrotor UAVs: Regular and Cascaded SMC. Because Passive Controllers handle a few numbers of faults, an Active Sliding Mode Fault Tolerant Controller using Kalman Filter is developed. To overcome severe faults and failures, an emergency controller based on the Quadrotor-to-Trirotor conversion maneuver is developed. The Controllers developed so far (Passive, Active, and emergency controllers) are then integrated to form the Integrated Fault Tolerant Controller (IFTC). The IFTC is a powerful controller that is able to handle a wide number of faults, and save actuator resources as well as processor computational effort. Finally, Passive and Active Fault Tolerant Controllers are designed for octorotor UAVs based on First Order and Second Order Sliding Mode Control. The AFTC uses Dynamic and Pseudo-Inverse Control Allocation methods to redistribute the control effort among healthy actuators reducing the effect of fault.

Permanent magnet synchronous machine drives are used more often. Although, synchronous machines drive also suffer from possible faults. This thesis is focused on the detection of the three-phase synchronous motor winding faults and the detection of the drive control loop sensors' faults. Firstly, a model of the faulty winding of the motor is presented. Effects of the inter-turn short fault were analyzed. The model was experimentally verified by fault emulation on the test bench with an industrial synchronous motor. Inter-turn short fault detection algorithms are summarized. Three existing conventional winding fault methods based on signal processing of the stator voltage and stator current residuals were verified. Three new winding fault detection methods were developed by the author. These methods use a modified motor model and the extended Kalman filter state estimator. Practical implementation of the algorithms on a microcontroller is described and experimental results show the performance of the presented algorithms in different scenarios on test bench measurements. Highly related motor control loop sensors fault detection algorithms are also described. These algorithms are complementary to winding fault algorithms. The decision mechanism integrates outputs of sensor and winding fault detection algorithms and provides an overall drive fault diagnosis concept.

Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that are otherwise used for simulation and control applications. A downside of model-based diagnosis is the modeling effort needed when no accurate models are available, which can take a large amount of time. This has motivated the use of data-driven diagnosis. Data-driven methods do not require as much system knowledge and modeling effort though they require large amounts of data and data from faults that can be hard to gather. Hybrid fault diagnosis methods combining models and training data can take advantage of both approaches decreasing the amount of time needed for modeling and does not require data from faults. In this thesis work a combined data-driven and model-based fault diagnosis system has been developed and evaluated for the exhaust treatment system in a heavy-duty diesel engine truck. The diagnosis system combines physical insights and neural networks to detect and isolate faults for the exhaust treatment system. This diagnosis system is compared with another system developed during this thesis using only model-based methods. Experiments have been done by using data from a heavy-duty truck from Scania. The results show the effectiveness of both methods in an industrial setting. It is shown how model-based approaches can be used to improve diagnostic performance. The hybrid method is showed to be an efficient way of developing a diagnosis system. Some downsides are highlighted such as the performance of the system developed using data-driven and model-based methods depending on the quality of the training data. Future work regarding the modularity and transferability of the hybrid method can be done for further evaluation.

This work is dedicated to faulty induction motors. These motors are often used in industrial applications thanks to their usability and their robustness. However, nowadays optimisation of production becomes so critical that the conceptual reliability of the motor is not sufficient anymore. Motor condition monitoring is expanding to serve maintenance planning and uptime maximisation. Moreover, the use of drive control sensors (namely stator current and voltage) can avoid the installation and maintenance of dedicated sensors for condition monitoring.<p><p>Many authors are working in this field but few approach the diagnosis from a detailed and clear physical understanding of the localised phenomena linked to the faults. Broken bars are known to modulate stator currents but it is shown in this work that it also changes machine saturation level in the neighbourhood of the bar. Furthermore, depending on the voltage level, this change in local saturation affects the amplitude and the phase of the modulation. This is of major importance as most diagnosis techniques use this feature to detect and quantify broken bars. For stator short-circuits, a high current is flowing in the short-circuited coil due to mutual coupling with the other windings and current spikes are flowing in the rotor bars as they pass in front of the short-circuited conductors. In the case of rotor eccentricities, the number of pole-pairs and the connection of these pole-pairs greatly affect the airgap flux density distribution as well as the repartition of the line currents in the different pole-pairs.<p><p>These conclusions are obtained through the use of time-stepping finite element models of the faulty motors. Moreover, circuit models of faulty machines are built based on the conclusions of previously explained fault analysis and on classical Park models. A common mathematical description is used which allows objective comparison of the models for representation of the machine behaviour and computing time.<p><p>The identifiability of the parameters of the models as well as methods for their identification are studied. Focus is set on the representation of the machine behaviour using these parameters more than the precise identification of the parameters. It is shown that some classical parameters can not be uniquely identified using only stator measurements.<p><p>Fault detection and identification using computationally cheap models are compared to advanced detection through motor stator current spectral analysis. This last approach allows faster detection and identification of the fault but leads to incorrect conclusions in low load conditions, in transient situations or in perturbed environments (i.e. fluctuating load torque and unideal supply). Efficient quantification of the fault can be obtained using detection techniques based on the comparison of the process to a model.<p><p>Finally, the work provides guidelines for motor supervision strategies depending on the context of motor utilisation. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished