Bacterial adhesion is the first step of biofilm formation and in the focus of research interest since several decades. Biofilms cause many problems, sometimes dramatic, for example in health, food packing or waste water purification. Despite of high interest, bacterial adhesion process is only poorly understood yet. In this work, bacterial adhesion was investigated on well-organized and structured model surfaces with various chemistries at molecular scale. For that purpose a characterization methodology was developed to sufficiently analyze monolayers on silicon wafers, and controlled mixed monolayers surfaces with different densities of NH 2 backfilled with CH3 were developed and optimized. These controlled surfaces with different densities of 0 % NH2 up to 100% NH2 were eventually used as tool to study bacterial adhesion in batch and real time conditions. The results demonstrate a significant impact on bacterial adhesion of weak difference in the surface chemistry at molecular scale. In the batch experiments, two so-called "plateaus" zones were determined, in which bacterial adhesion is not significantly different despite the change of the amine concentration on the surface. On the contrary, one transition zone exists between the "plateaus" in which a slight chunge.in the amine concentration leads to a significant increase / decrease of the bacterial adhesion. The same trend of bacteria behavior was observed for different bacterial strains.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

L'adhésion bactérienne est la première étape du processus de formation d'un biofilm et est un enjeu majeur de la recherche depuis plusieurs dizaines d'années. Les biofilms ont des conséquences parfois dramatiques dans des domaines comme la santé, l'agroalimentaire ou la purification des eaux usées. Toutefois, l'adhésion bactérienne reste un phénomène mal compris. Dans cette thèse, l'adhésion bactérienne est étudiée sur des surfaces modèles très bien organisées et structurées, de chimie de surface variable à l'échelle moléculaire. Une méthodologie de caractérisation adaptée aux monocouches déposées sur wafers de silicium est proposée. Des surfaces modèles composées de monocouches mixtes auto-assemblées de densités variables de NH2 dans un continuum de CH, sont développées et optimisées. Ces surfaces contrôlées, de densités de 0% NH2 à 100% NH2 dans CH3, sont utilisées comme outil pour étudier l'adhésion bactérienne en conditions de culture « batch »et « temps réel ». Les résultats montrent un impact significatif sur l'adhésion bactérienne de faibles différences chimiques à l'échelle moléculaire. Les résultats des expériences menées en conditions « batch » permettent de déterminer deux zones « plateau » dans lesquelles l'adhésion bactérienne ne varie pas significativement malgré des variations importantes de la concentration en groupements amine sur la surface. Une zone de transition entre les zones « plateau » est mise en évidence, dans laquelle une faible modification de la concentration en groupement amine mène à l'augmentation / diminution significative du nombre de bactéries adhérées. Cette tendance est montrée pour deux souches différentes de bactérie. / Bacterial adhesion is the first step of biofilm formation and in the focus of research interest since several decades. Biofilms cause many problems, sometimes dramatic, for example in health, food packing or waste water purification. Despite of high interest, bacterial adhesion process is only poorly understood yet. In this work, bacterial adhesion was investigated on well-organized and structured model surfaces with various chemistries at molecular scale. For that purpose a characterization methodology was developed to sufficiently analyze monolayers on silicon wafers, and controlled mixed monolayers surfaces with different densities of NH 2 backfilled with CH3 were developed and optimized. These controlled surfaces with different densities of 0 % NH2 up to 100% NH2 were eventually used as tool to study bacterial adhesion in batch and real time conditions. The results demonstrate a significant impact on bacterial adhesion of weak difference in the surface chemistry at molecular scale. In the batch experiments, two so-called "plateaus" zones were determined, in which bacterial adhesion is not significantly different despite the change of the amine concentration on the surface. On the contrary, one transition zone exists between the "plateaus" in which a slight chunge.in the amine concentration leads to a significant increase / decrease of the bacterial adhesion. The same trend of bacteria behavior was observed for different bacterial strains.