The interest in autonomous vehicles has never been higher and there are several components that need to function for a vehicle to be fully autonomous; one of which is the ability to perform a parking at the end of a mission. The objective of this thesis work is to develop and implement an automatic parking system (APS) for a heavy-duty vehicle (HDV). A delimitation in this thesis work is that the parking lot has a known structure and the HDV is a truck without any trailer and access to more computational power and sensors than today's commercial trucks. An automatic system for searching the parking lot has been developed which updates an occupancy grid map (OGM) based on measurements from GPS and LIDAR sensors mounted on the truck. Based on the OGM and the known structure of the parking lot, the state of the parking spots is determined and a path can be computed between the current and desired position. Based on a kinematic model of the HDV, a gain-scheduled linear quadratic (LQ) controller with feedforward action is developed. The controller's objective is to stabilize the lateral error dynamics of the system around a precomputed path. The LQ controller explicitly takes into account that there exist an input delay in the system. Due to minor complications with the precomputed path the LQ controller causes the steering wheel turn too rapidly which makes the backup driver nervous. To limit these rapid changes of the steering wheel a controller based on model predictive control (MPC) is developed with the goal of making the steering wheel behave more human-like. A constraint for maximum allowed changes of the controller output is added to the MPC formulation as well as physical restrictions and the resulting MPC controller is smoother and more human-like, but due to computational limitations the controller turns out less effective than desired. Development and testing of the two controllers are evaluated in three different environments of varying complexity; the simplest simulation environment contains a basic vehicle model and serves as a proof of concept environment, the second simulation environment uses a more realistic vehicle model and finally the controllers are evaluated on a full-scale HDV. Finally, system tests of the APS are performed and the HDV successfully parks with the LQ controller as well as the MPC controller. The concept of a self-parking HDV has been demonstrated even though more tuning and development needs to be done before the proposed APS can be used in a commercial HDV.

The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. / Dissertation (MSc)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted

Current research on HIV therapy is diverse and multi-disciplinary. Engineers however, were late in joining the research movement and as such, engineering literature related to HIV chemotherapy is limited. Control engineers in particular, should have risen to the challenge, as it is apparent that HIV chemotherapy and control engineering have a lot in common. From a control theoretic point of view, HIV chemotherapy is control of a time varying nonlinear dynamical system with constrained controls. Once a suitable model has been developed or identified, control system theoretical concepts and design principles can be applied. The adopted control approach or strategy depends primarily on the control objectives, performance specifications and the control constraints. In principle, the designed control system can then be validated with clinical data. Obtaining measurements of the controlled variables however, has the potential to hinder effective control. The first part of this research focused on the application of control system analytical tools to HIV/AIDS models. The intention was to gain some insights into the HIV infection dynamics from a control theoretic perspective. The issues that needed to be addressed are: Persistent virus replication under potent HAART, variability in response to therapy between individuals on the same regimen, transient rebounds of plasma viremia after periods of suppression, the attainment, or lack thereof, of maximal and durable suppression of the viral load. The questions to answer were: When are the above mentioned observed responses to therapy most likely to occur as the HIV infection progresses, and does attaining one necessarily imply the other? Furthermore, the prognostic markers of virologic success, the possibility of individualizing therapy and timing the initiation of antiretroviral therapy such that the benefits of therapy are maximized, are matters that were also investigated. The primary objective of this thesis was to analyze models for the eventual control of the HIV infection. HIV therapy has multiple and often conflicting objectives, and these objectives had to be prioritized. The intention of the proposed control strategy was to produce practical solutions to the current antiretroviral problems. To this end, the second part of the research focused on addressing the HIV/AIDS control issues of sampling for effective control given the invasive nature of drawing blood from a patient and the derivation of drug dosage sequences to strike a balance between maximal suppression and toxicity reduction, when multiple drugs are concomitantly used to treat the infection. / Thesis (PhD)--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / Unrestricted

Cette thèse porte sur l'optimisation et la commande prédictive des centrales de production d'énergie en utilisant des modèles physiques des installations. Les modèles sont réalisés à l'aide du langage Modelica, un langage équationnel adapté à la modélisation de systèmes multi-physiques. La modélisation de systèmes physiques dans ce langage est présentée dans une première partie, ainsi que les traitements symboliques réalisés par les compilateurs Modelica pour mettre les modèles sous une forme adaptée à l'optimisation. On présente dans une seconde partie le développement d'une méthode d'optimisation dynamique hybride pour les centrales de production d'énergie, qui fournit une trajectoire optimisée de l'installation sur un horizon long. Les trajectoires calculées incluent les trajectoires des commandes continues ainsi que les décisions d'engagement des différents équipements. L'algorithme d'optimisation combine la méthode de collocation et une méthode nommée Sum Up Rounding (SUR) pour la prise en compte des décisions d'engagement. Un algorithme de commande prédictive (MPC) est enfin introduit afin de garantir le suivi des trajectoires optimales et de prendre en compte en temps réel la présence de perturbations et les erreurs du modèle d'optimisation. L'algorithme MPC utilise des modèles linéarisés tangents générés automatiquement à partir du modèle non linéaire. / This thesis deals with hybrid optimal control and Model Predictive Control (MPC) of power plants by use of physical models. Models of the facilities are developped with Modelica, an equation based language tailored for modelling multi-physics systems. Modeling of physical systems with Modelica is introduced in a first part, as well as some of the symbolic processing done by Modelica compilers that transform the original model to a form suited for optimization. Then, a method to solve optimal control problems on hybrid systems (such as power plants) is presented. This methods provides an optimal trajectory for the power plant on a long horizon. The optimal trajectory computed by the method includes the trajectories of continuous inputs as well as switching decisions for components in the plant. The optimization algorithm combines the collocation method and a method named Sum Up Rounding (SUR) for dealing with switches. Finally, a Model Predictive Controller is developped in order to follow this optimal trajectory in real time, and to cope with disturbances on the actual system and modelling errors. The proposed MPC uses tangent linear models of the plant that are derived automatically from the nonlinear model.

Les travaux présentés dans ce manuscrit portent sur l’optimisation d’une chaufferie collective multi-énergie en l’équipant d’un système de stockage thermique de type hydro-accumulation. L’efficacité de ce dernier dépend de sa capacité à conserver son énergie initiale. Ainsi, pour minimiser les pertes thermiques, le système de stockage doit être correctement isolé. Un modèle de ballon d’hydro-accumulation stratifié est développé et validé expérimentalement. Une étude paramétrique est menée afin d’étudier l’impact, sur les pertes thermiques, de paramètres géométriques et météorologiques. Ensuite, une approche de dimensionnement, fondée sur une stratégie de gestion séquentielle et une étude paramétrique est proposée. Plusieurs critères énergétiques et économiques peuvent ainsi être évalués pour différents volumes de stockage thermique. L’approche de dimensionnement proposée a été appliquée à plusieurs sites exploités par Cofely GDF-Suez, notre partenaire industriel. Les résultats obtenus montrent que le dimensionnement optimal du système de stockage et la gestion intelligente du procédé permettent d'optimiser le fonctionnement d’un site. Des économies énergétiques et financières importantes peuvent ainsi être réalisées. La stratégie de gestion séquentielle proposée n’anticipe pas les besoins du réseau de chaleur. C’est pourquoi une stratégie fondée sur la commande prédictive (MPC pour Model Predictive Control) est susceptible d’améliorer le fonctionnement et les performances d’une chaufferie collective équipée d’un système d’hydro-accumulation. Afin de mettre en œuvre un tel contrôleur, la puissance thermique demandée par le réseau de chaleur doit être correctement prédite. Par conséquent, une méthode de prédiction a été développée. Elle s’appuie sur une analyse multi-résolution par transformée en ondelettes discrète et sur l’utilisation de réseaux de neurones artificiels de type perceptron multicouche. La stratégie séquentielle (non prédictive) et la stratégie prédictive ont été appliquées à une chaufferie collective mixte située dans une commune du nord de la France. Pour ce cas d’étude, la stratégie prédictive est plus efficace. De plus, les résultats montrent que, quelle que soit la stratégie utilisée, équiper la chaufferie d’un système d’hydro-accumulation correctement dimensionné est un investissement rentable. Il permet de minimiser la consommation d’énergie fossile et, par conséquent, le coût économique et les émissions de CO2. / The present work deals with optimizing a multi-energy district boiler by adding to the plant a thermal water storage tank. The effectiveness of such a system depends on how long the stored energy can be kept without considerable degradation. The storage tank should be properly insulated to reduce the rate of heat loss. Thus, firstly, a stratified water thermal storage model is developed and experimentally validated. A parametric study is carried out to determine the influence of geometric and meteorological parameters on heat loss. Next, a reliable sizing method based on a sequential management strategy and a parametric study is proposed. Various energy and economic criteria have been evaluated for a range of thermal storage sizes. The proposed methodology has been applied to many plants managed by Cofely GDF-Suez, our industrial partner. Results highlight the ability of a thermal storage tank (optimally sized and managed) to improve the operation of a multi-energy district boiler and realize significant energy and economic savings. The main drawback of the proposed sequential management strategy lies in not taking into account the future power demand. That is why a strategy based on a Model Predictive Controller (MPC) is likely to improve operation and performance. In order to implement such a controller, the power demand has to be accurately forecasted. As a consequence, a short-term forecast method, based on wavelet-based Multi-Resolution Analysis (MRA) and multilayer Artificial Neural Networks (ANN) is proposed. Both the sequential and the predictive strategies are applied to a northeast France multi-energy district boiler selected as a case study. The main result to retain is that the efficiency of water thermal storage tank is mainly related to its design and the way it is managed. For this case study, the predictive strategy regardless the size of the storage tank, the predictive strategy is more reliable. Furthermore, in all cases an adequately sized and managed thermal storage tank is a profitable investment. It allows the fossil energy consumption to be significantly reduced. The same remark applies to the functioning costs and CO2 emissions.

Les travaux présentés dans ce mémoire de thèse s’inscrivent dans le cadre des Systèmes de TransportIntelligents (STI). Bien que les premières études sur ces systèmes ont commencé dans les années 60, leurdéveloppement reposant sur les techniques de l’information et de la communication, a atteint sa maturitédans le début des années 80. Les STI, sont composés de différents systèmes et intègrent différents concepts(systèmes embarqués, capteurs intelligents, autoroutes intelligentes, . . .) afin d’optimiser le rendementdes infrastructures routières et répondre aux problèmes quotidiens des congestions. Ce mémoire présentequatre contributions dans le cadre du trafic routier et aborde les problèmes de l’estimation et de lacommande afin d’éliminer les problèmes de congestions « récurrentes ». Le premier point traite unproblème crucial dans le domaine des STI qui est celui de l’estimation. En effet, la mise en oeuvre delois de commande pour réguler le trafic impose de disposer de l’ensemble des informations concernantl’évolution de l’état du trafic. Dans ce contexte, deux algorithmes d’estimation sont proposés. Le premierrepose sur l’emploi du modèle METANET et les techniques de modes de glissement d’ordre supérieur. Lesecond est basé sur les CTM (Cell Transmission Models). Plusieurs études comparatives avec les filtresde Kalman sont proposées. La seconde contribution concerne la régulation du trafic. L’accent est mis surle contrôle d’accès isolé en utilisant les algorithmes issus du mode de glissement d’ordre supérieur. Cettecommande est enrichie en introduisant une commande intégrée combinant le contrôle d’accès et le routagedynamique. L’ensemble des résultats, validé par simulation, est ensuite comparé aux stratégies classiquesnotamment le contrôle d’accès avec l’algorithme ALINEA. La troisième contribution traite des problèmesde coordination. En effet, l’objectif est d’appliquer le principe de la commande prédictive pour contrôlerplusieurs rampes d’accès simultanément. L’ensemble des contributions ont été validées en utilisant desdonnées réelles issues en grande partie de mesures effectuées sur des autoroutes françaises. Les résultatsobtenus ont montré un gain substantiel en termes de performances tels que la diminution du trajet, dutemps d’attente, de la consommation énergétique, ainsi que l’augmentation de la vitesse moyenne. Cesrésultats permettent d’envisager plusieurs perspectives nouvelles de développement des recherches dansce domaine susceptibles d’apporter des solutions intéressantes. / The works presented in this PhD dissertation fit into the framework of Intelligent TransportationSystems. Although the beginnings of these systems have started since the 60s, their development, basedon information and communication technologies, has reached maturity during the early 80s. The ITS usesthe intelligence of different systems (embedded systems, intelligents sensors, intelligents highways, etc.)in order to optimize road infrastructures performances and respond to the daily problems of congestions.The dissertation presents four contributions into the framework of road traffic flow and tackles theestimation and control problems in order to eliminate or at least reduce the “recurrent" congestionsphenomena. The first point treats the problem of traffic state estimation which is of most importance inthe field of ITS. Indeed, the implementation and performance of any control strategy is closely relatedto the ability to have all needed information about the traffic state describing the dynamic behavior ofthe studied system. Two estimation algorithms are then proposed. The first one uses the “metanet"model and high order sliding mode techniques. The second is based on the so-called Cell TransmissionModels. Several comparative studies with the Kalman filters, which are the most used in road traffic flowengineering, are established in order to demonstrate the effectiveness of the proposed approaches. Thethree other contributions concern the problem of traffic flow control. At first, the focus is on the isolatedramp metering using an algorithm based on the high order sliding mode control. The second contributiondeals with the dynamic traffic routing problem based on the high order sliding mode control. Such controlstrategy is enriched by introducing the concept of integration, in the third contribution. Indeed, integratedcontrol consists of a combination of several traffic control algorithms. In this thesis the proposed approachcombines an algorithm of on-ramp control with a dynamic traffic routing control. The obtained results arevalidated via numerical simulations. The validated results of the proposed isolated ramp metering controlare compared with the most used ramp metering strategy : ALINEA. Finally, the last contributiontreats the coordination problems. The objective is to coordinate several ramps which cooperate andchange information in order to optimize the highway traffic flow and reduce the total travel time in theapplied area. All these contributions were validated using real data mostly from French freeways. Theobtained results show substantial gains in term of performances such as travel time, energetic consumptiondecreasing, as well as the increasing in the mean speed. These results allow to consider several furtherworks in order to provide more interesting and efficient solutions in the ITS field.

The focus of this thesis is the development and implementation of a neural network model predictive controller to be used for controlling the integrated recycle heat exchanger (Intrex) in a 300MW circulating fluidized bed (CFB) boiler. Discussion of the development of the controller will include data collection and preprocessing, controller design and controller tuning. The controller will be programmed directly into the plant distributed control system (DCS) and does not require the continuous use of any third party software. The intrexes serve as the loop seal in the CFB as well as intermediate and finishing superheaters. Heat is transferred to the steam in the intrex superheaters from the circulating ash which can vary in consistency, quantity and quality. Fuel composition can have a large impact on the ash quality and in turn, on intrex performance. Variations in MW load and airflow settings will also impact intrex performance due to their impact on the quantity of ash circulating in the CFB. Insufficient intrex heat transfer will result in low main steam temperature while excessive heat transfer will result in high superheat attemperator sprays and/or loss of unit efficiency. This controller will automatically adjust to optimize intrex ash flow to compensate for changes in the other ash properties by controlling intrex air flows. The controller will allow the operator to enter a target intrex steam temperature increase which will cause all of the intrex air flows to adjust simultaneously to achieve the target temperature. The result will be stable main steam temperature and in turn stable and reliable operation of the CFB.

ABSTRACT The repair and strengthening of concrete bridge members with CFRP has become increasingly popular over recent years. However, significant research is still needed in order to develop more robust guidelines and specifications. The research project aims to assist with improving design prosedures for damaged concrete members with the use of CFRP. This document summarizes the analysis and testing of full-scale 40’ foot long prestressed concrete (PSC) bridge girders exposed to simulated impact damage and repaired with carbon fiber reinforced polymers (CFRP) materials. A total of five AASHTO type II bridge girders fabricated in the 1960’s were taken from an existing bridge, and tested at the Florida Department of Transportation FDOT structures lab in Tallahassee, Florida. The test specimens were tested under static loading to failure under 4-point bending. Different CFRP configurations were applied to each of the girders. Each of the test girders performed very well as each of them held a higher capacity than the control girder. The repaired girders 5, 6 and 7 surpassed the control girder’s capacity by 10.88%, 15.9% and 11.39%. These results indicate that repairing laterally damaged prestressed concrete bridge girders with CFRP is an effective way to restore the girders flexural capacity.

L'objectif de cette thèse est de réaliser des missions diverses de navigation autonome en environnement intérieur et encombré avec des robots terrestres. La perception de l'environnement est assurée par un banc stéréo embarqué sur le robot et permet entre autres de calculer la localisation de l'engin grâce à un algorithme d'odométrie visuelle. Mais quand la qualité de la scène perçue par les caméras est faible, la localisation visuelle ne peut pas être calculée de façon précise. Deux solutions sont proposées pour remédier à ce problème. La première solution est l'utilisation d'une méthode de fusion de données multi-capteurs pour obtenir un calcul robuste de la localisation. La deuxième solution est la prédiction de la qualité de scène future afin d'adapter la trajectoire du robot pour s'assurer que la localisation reste précise. Dans les deux cas, la boucle de commande est basée sur l'utilisation de la commande prédictive afin de prendre en compte les différents objectifs de la mission : ralliement de points, exploration, évitement d'obstacles. Une deuxième problématique étudiée est la navigation par points de passage avec évitement d'obstacles mobiles à partir des informations visuelles uniquement. Les obstacles mobiles sont détectés dans les images puis leur position et vitesse sont estimées afin de prédire leur trajectoire future et ainsi de pouvoir anticiper leur déplacement dans la stratégie de commande. De nombreuses expériences ont été réalisées en situation réelle et ont permis de montrer l'efficacité des solutions proposées. / The aim of this thesis is to perform various autonomous navigation missions in indoor and cluttered environments with mobile robots. The environment perception is ensured by an embedded stereo-rig and a visual odometry algorithm which computes the localization of the robot. However, when the quality of the scene perceived by the cameras is poor, the visual localization cannot be computed with a high precision. Two solutions are proposed to tackle this problem. The first one is the data fusion from multiple sensors to perform a robust computation of the localization. The second solution is the prediction of the future scene quality in order to adapt the robot's trajectory to ensure that the localization remains accurate. In the two cases, the control loop is based on model predictive control, which offers the possibility to consider simultaneously the different objectives of the mission : waypoint navigation, exploration, obstacle avoidance. A second issue studied is waypoint navigation with avoidance of mobile obstacles using only the visual information. The mobile obstacles are detected in the images and their position and velocity are estimated in order to predict their future trajectory and consider it in the control strategy. Numerous experiments were carried out and demonstrated the effectiveness of the proposed solutions.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.