Ce document synthétise mes travaux de thèse de doctorat en Automatique Productiqueà Grenoble INP (Institut National Polytechnique), thèse préparée au sein dudépartement automatique du laboratoire GIPSA-lab (Grenoble Image Parole Signal etAutomatique). Ce travail s’inscrit dans le cadre du contrôle local et de la supervisiondes systèmes de trafic routier. Les principales contributions portent sur la modélisation,la supervision et la commande locale des systèmes de trafic routier.La contribution apportée à la modélisation du trafic est l’ajout d’un modèle d’incertitudesur le modèle CTM (Cell Transmission Model [Daganzo, 1994]). Ce nouveaumodèle permet de prendre en compte les incertitudes sur différents paramètres dumodèle pour in-fine proposer de nouvelles stratégies de commandes commutées robustes.Outre cette approche de modélisation, nous proposons un niveau de supervisionpermettant d’une part d’estimer en temps réel le mode de fonctionnement et d’autrepart de détecter, localiser et estimer certaines fautes sur le système. L’estimation dynamiquede mode de fonctionnement nous permet de connaître l’état de congestion (ou denon-congestion) de l’aménagement routier considéré. Nous sommes en mesure de détecterdes fautes telles que des chutes de vitesse ou des chutes de capacité survenant sur la route.Enfin, nous proposons deux lois de commandes locales basées sur la théorie dessystèmes à commutations. Ainsi, le schéma de contrôle s’adaptera dynamiquementaux changements de propriétés du système. Ces lois de commande ont pour objet des’insérer dans un schéma de régulation hiérarchique. / This document synthesizes my Phd thesis work in Automatic Control in Grenoble-INP. This thesis has been prepared in the automatic control department of thelaboratory GIPSA-lab. This work is situated in the area of traffic systems control andsupervision. Our contributions are about modeling, supervision and local traffic control.The CTM traffic model has been extended with a model of uncertainties. Thisnews model allows us to take into account the uncertain parameters of the model, topropose new robust switched control law.In addition to this modeling approach, we propose some developments on supervisionof trafic systems. On one hand, we can estimate the operating mode of thesystem in real time and on the other hand to estimate some faults on the system. Thedynamical estimation of the operating mode allows us to know the state of congestion(or non congestion) of the road. We are able to estimate faults such as speed fall andcapacities drop that may appear.Finally, we propose two control laws based on switching systems control. The developedcontrollers adapt their geometry to the properties of the system. The purposeof these controllers is to be inserted in a hierarchic control scheme.

This thesis concerns the development of optimisation algorithms to determine the relative co-location, (localisation), of a number of freely-flying 'Smart Dust mote' sensor platform elements using a non-deterministic data-set derived from the duplex wireless transmissions between elements. Smart dust motes are miniaturised, microprocessor based, electronic sensor platforms, frequently used for a wide range of remote environmental monitoring applications; including specific climate synoptic observation research and more general meteorology. For the application proposed in this thesis a cluster of the notional smart dust motes are configured to imitate discrete 'Radio Drop Sonde' elements of the wireless enabled monitoring system in use by meteorological research organisations worldwide. This cluster is modelled in software in order to establish the relative positions during the 'flight' ; the normal mode of deployment for the Drop Sonde is by ejection from an aeroplane into an upper-air zone of interest, such as a storm cloud. Therefore the underlying research question is, how to track a number of these independent, duplex wireless linked, free-flying monitoring devices in 3-dimensions and time (to give the monitored data complete spatio-temporal validity). This represents a significant practical challenge, the solution applied in this thesis was to generate 3-dimensional geometries using the only 'real-time' data available; the Radio Signal Strength Indicator (RSSI) data is generated through the 'normal' duplex wireless communications between motes. Individual RSSI values can be considered as a 'representation of the distance magnitude' between wireless devices; when collated into a spatio-temporal data-set it 'encodes' the relative, co-locational, 3-dimensional geometry of all devices in the cluster. The reconstruction, (or decoding), of the 3-dimensional geometries encoded in the spatio-temporal data-set is a complex problem that is addressed through the application of various algorithms. These include, Random Search, and optimisation algorithms, such as the Stochastic Hill-climber, and various forms of Evolutionary Algorithm. It was found that the performance of the geometric reconstruction could be improved through identification of salient aspects of the modelled environment, the result was heuristic operators. In general these led to a decrease in the time taken to reach a convergent solution or a reduction in the number of candidate search space solutions that must be considered. The software model written for this thesis has been implemented to generalise the fundamental characteristics of an optimisation algorithm and to incorporate them into a generic software framework; this then provides the common code to all model algorithms used.