Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

Natural populations may be managed by humans for a number of reasons, with mathematical modelling playing an increasing role in the planning of such management and control strategies. In an increasingly heterogeneous, or `patchy' landscape, the interactions between distinct groups of individuals must be taken into account to predict meaningful management strategies. Invasive control strategies, involving reduction of populations, such as harvesting or culling have been shown to cause a level of disturbance, or spatial perturbation, to these groups, a factor which is largely ignored in the modelling literature. In this thesis, we present a series of deterministic, differential equation models which are used to investigate the impact of this disturbance in response to control. We address this impact in two scenarios. Firstly, in terms of a harvested population, where extinction must be prevented whilst maximising the yield obtained. Secondly, we address the impact of disturbance in an epidemic model, where the aim of the control strategy is to eradicate an endemic pathogen, or to prevent the invasion of a pathogen into a susceptible population. The movement of individuals between patches is modelled as both a constant rate, and a function which is increasing with population density. Finally, we discuss the 'optimal' control strategy in this context. We find that, whilst a population harvested from a coupled system is able to produce an inflated yield, this coupling can also cause the population to be more resistant to higher harvesting efforts, increasing the effort required to drive the population to extinction. Spatial perturbation raises this extinction threshold further still, providing a survival mechanism not only for the individuals that avoid being killed, but for the population as a whole. With regards to the eradication of disease, we show that disturbance may either raise or lower the pathogen exclusion threshold depending on the particular characteristics of the pathogen. In certain cases, we have shown that spatial perturbation may force a population to be susceptible to an infectious invasion where its natural carrying capacity would prevent this.

Genetic Algorithms (GAs) are a search heuristic modeled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches are illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of bio-control application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a bio-control agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to maximise their effectiveness. This work demonstrates that significant advantages are gained in terms of both fitness function evaluations required and fitness scores found using the proposed approaches when compared with traditional GA crossover approaches for the production of optimal control schedules.

Le travail présenté ici s'intéresse à la résolution numérique de problèmes de commande optimale stochastique de grande taille. Nous considérons un système dynamique, sur un horizon de temps discret et fini, pouvant être influencé par des bruits exogènes et par des actions prises par le décideur. L'objectif est de contrôler ce système de sorte à minimiser une certaine fonction objectif, qui dépend de l'évolution du système sur tout l'horizon. Nous supposons qu'à chaque instant des observations sont faites sur le système, et éventuellement gardées en mémoire. Il est généralement profitable, pour le décideur, de prendre en compte ces observations dans le choix des actions futures. Ainsi sommes-nous à la recherche de stratégies, ou encore de lois de commandes, plutôt que de simples décisions. Il s'agit de fonctions qui à tout instant et à toute observation possible du système associent une décision à prendre. Ce manuscrit présente trois contributions. La première concerne la convergence de méthodes numériques basées sur des scénarios. Nous comparons l'utilisation de méthodes basées sur les arbres de scénarios aux méthodes particulaires. Les premières ont été largement étudiées au sein de la communauté "Programmation Stochastique". Des développements récents, tant théoriques que numériques, montrent que cette méthodologie est mal adaptée aux problèmes à plusieurs pas de temps. Nous expliquons ici en détails d'où provient ce défaut et montrons qu'il ne peut être attribué à l'usage de scénarios en tant que tel, mais plutôt à la structure d'arbre. En effet, nous montrons sur des exemples numériques comment les méthodes particulaires, plus récemment développées et utilisant également des scénarios, ont un meilleur comportement même avec un grand nombre de pas de temps. La deuxième contribution part du constat que, même à l'aide des méthodes particulaires, nous faisons toujours face à ce qui est couramment appelé, en commande optimale, la malédiction de la dimension. Lorsque la taille de l'état servant à résumer le système est de trop grande taille, on ne sait pas trouver directement, de manière satisfaisante, des stratégies optimales. Pour une classe de systèmes, dits décomposables, nous adaptons des résultats bien connus dans le cadre déterministe, portant sur la décomposition de grands systèmes, au cas stochastique. L'application n'est pas directe et nécessite notamment l'usage d'outils statistiques sophistiqués afin de pouvoir utiliser la variable duale qui, dans le cas qui nous intéresse, est un processus stochastique. Nous proposons un algorithme original appelé Dual Approximate Dynamic Programming (DADP) et étudions sa convergence. Nous appliquons de plus cet algorithme à un problème réaliste de gestion de production électrique sur un horizon pluri-annuel. La troisième contribution de la thèse s'intéresse à une propriété structurelle des problèmes de commande optimale stochastique : la question de la consistance dynamique d'une suite de problèmes de décision au cours du temps. Notre but est d'établir un lien entre la notion de consistance dynamique, que nous définissons de manière informelle dans le dernier chapitre, et le concept de variable d'état, qui est central dans le contexte de la commande optimale. Le travail présenté est original au sens suivant. Nous montrons que, pour une large classe de modèles d'optimisation stochastique n'étant pas a priori consistants dynamiquement, on peut retrouver la consistance dynamique quitte à étendre la structure d'état du système / This work is intended at providing resolution methods for Stochastic Optimal Control (SOC) problems. We consider a dynamical system on a discrete and finite horizon, which is influenced by exogenous noises and actions of a decision maker. The aim is to minimize a given function of the behaviour of the system over the whole time horizon. We suppose that, at every instant, the decision maker is able to make observations on the system and even to keep some in memory. Since it is generally profitable to take these observations into account in order to draw further actions, we aim at designing decision rules rather than simple decisions. Such rules map to every instant and every possible observation of the system a decision to make. The present manuscript presents three main contributions. The first is concerned with the study of scenario-based solving methods for SOC problems. We compare the use of the so-called scenario trees technique to the particle method. The first one has been widely studied among the Stochastic Programming community and has been somehow popular in applications, until recent developments showed numerically as well as theoretically that this methodology behaved poorly when the number of time steps of the problem grows. We here explain this fact in details and show that this negative feature is not to be attributed to the scenario setting, but rather to the use of a tree structure. Indeed, we show on numerical examples how the particle method, which is a newly developed variational technique also based on scenarios, behaves in a better way even when dealing with a large number of time steps. The second contribution starts from the observation that, even with particle methods, we are still facing some kind of curse of dimensionality. In other words, decision rules intrisically suffer from the dimension of their domain, that is observations (or state in the Dynamic Programming framework). For a certain class of systems, namely decomposable systems, we adapt results concerning the decomposition of large-scale systems which are well known in the deterministic case to the SOC case. The application is not straightforward and requires some statistical analysis for the dual variable, which is in our context a stochastic process. We propose an original algorithm called Dual Approximate Dynamic Programming (DADP) and study its convergence. We also apply DADP to a real-life power management problem. The third contribution is concerned with a rather structural property for SOC problems: the question of dynamic consistency for a sequence of decision making problems over time. Our aim is to establish a link between the notion of time consistency, that we loosely define in the last chapter, and the central concept of state structure within optimal control. This contribution is original in the following sense. Many works in the literature aim at finding optimization models which somehow preserve the "natural" time consistency property for the sequence of decision making problems. On the contrary, we show for a broad class of SOC problems which are not a priori time-consistent that it is possible to regain this property by simply extending the state structure of the model

This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.

This dissertation concerns the self-organization behaviors in different types of multi-agent systems, and possible ways to apply interventions on top ofthat to achieve certain goals. A bounded confidence opinion dynamics modelis considered for the first two papers. Theoretical analysis of the model isperformed and modifications of the model are given so that it will have better properties in some aspect. Leader-follower based models are studied in the third to fifth papers where various optimal control problems are considered. Different methods such as Pontryagin minimum principle and dynamic programming are used to solve those optimal control problem. For complex problems, one may only get approximate solutions or suboptimal solutions.In Paper A and Paper B, we consider the continuous-time Hegselmann-Krause (H-K) model and its variations and target the problem of reaching consensus. A sufficient condition on the initial opinion distribution is givento guarantee consensus for the original continuous-time H-K model. A modified model is provided and proven to be able to lead a larger range of initial opinions to synchronization. An H-K model with an exo-system is also studied where sufficient conditions on the exo-system are given for the purpose of consensus.In Paper C and Paper D, optimal control problems with leader-followerbased multi-agent systems are discussed. Analytic solutions are derived if the dynamics is linear by applying Pontryagin minimum principle. For generalnon-linear leader-follower interactions, we provide a method that use sstatistic moments of the follower crowd to approximate the optimal control.The dynamic programming approach is used and certain approximation ofthe Hamilton-Jacobi-Bellman equations is needed. The computational burdenis so heavy that model predictive control method is required in practical applications.In Paper E, we apply a similar method to the approach used in PaperD to target a pollutant elimination problem. It implies that we can use themethod to attack optimal control problem with partial differential equation constraints by discretization in space. The dimension of the discretization is not related to the computational complexity since only the statistic moments are needed. / <p>QC 20161201</p>

Waterflooding is a common type of oil recovery techniques where water is pumped into the reservoir for increased productivity. Reservoir states change with time, as such, different injection and production settings will be required to lead the process to optimal operation which is actually a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is not appropriate for reservoir production due to numerous uncertain properties involved. Models that are updated through the current industrial practice of ‘history matching’ may fail to predict reality correctly and therefore, solutions based on history-matched models may be suboptimal or non-optimal at all. Due to its ability in counteracting the effects uncertainties, direct feedback control has been proposed recently for optimal waterflooding operations. In this work, two feedback approaches were developed for waterflooding process optimization. The first approach is based on the principle of receding horizon control (RHC) while the second is a new dynamic optimization method developed from the technique of self-optimizing control (SOC). For the SOC methodology, appropriate controlled variables (CVs) as combinations of measurement histories and manipulated variables are first derived through regression based on simulation data obtained from a nominal model. Then the optimal feedback control law was represented as a linear function of measurement histories from the CVs obtained. Based on simulation studies, the RHC approach was found to be very sensitive to uncertainties when the nominal model differed significantly from the conceived real reservoir. The SOC methodology on the other hand, was shown to achieve an operational profit with only 2% worse than the true optimal control, but 30% better than the open-loop optimal control under the same uncertainties. The simplicity of the developed SOC approach coupled with its robustness to handle uncertainties proved its potentials to real industrial applications.

Dans une première partie, nous étudions la stabilité des solutions d'une inégalité variationnelle de la forme cône normal perturbé par une fonction. Pour ce faire, nous généralisons la méthode de S. Robinson, basée sur le degré topologique, aux espaces de Hilbert et à une classe de multi-applications non nécessairement convexes, appelées multi-applications prox-régulières.  Dans une deuxième partie, nous étudions des problèmes de contrôle optimal liés à la modélisation de problèmes de bio-procédés, et l'on s'intéresse à des contraintes périodiques sur l'état. Ainsi, nous étendons les résultats d'existence de solutions périodiques des EDOs de Kolmogorov au cadre du contrôle en rajoutant un paramètre contrôlé à ces équations. Ceci nous permet d'étudier par la suite un problème de commande optimale d'un chemostat sous forçage périodique, et d'en déduire la synthèse optimale pour ce problème. / In the first part, we study stability of solutions of a variational inequality of the form normal cone perturbed by a mapping. To do so, we generalize the method introduced by S. Robinson, based on the topological degree, to the general Hilbert setting on the class of non-necessarily convex set-valued mapping, called prox-regular set-valued mapping. In the second part, we study optimal control problems connected to the modelization of bio-processes and we consider periodic constraints on the state variable. We first extend the existence result of periodic solutions of Kolmogorov ODEs to the setting of control by adding a controlled parameter to those ODEs. This allows us to study an optimal control problem modeling a chemostat under a periodic forcing for which we give the optimal synthesis.

Today, hybrid electric propulsion technology provides a promising and practical solution for improving vehicle performance, increasing energy efficiency, and reducing harmful emissions, due to the additional flexibility that the technology has provided in the optimal power control and energy management, which are the keys to its success. In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for the Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for development of the next generation HEVs and PHEVs. / Graduate / laindeece@gmail.com

L’objectif de cette thèse est d’appliquer des méthodes de contrôle quantique pour manipuler la dynamique rotationnelle de molécules et améliorer l’efficacité de processus en résonance magnétique nucléaire.Ces techniques ont été utilisées théoriquement et expérimentalement pour contrôler l’orientation d’une molécule toupie symétrique à l’aide de champ THz. Cette étude a été généralisée à une grande distance d’interaction entre le champ et l’échantillon. Dans ce cas, la molécule ne peut plus être considérée comme isolée. Nous avons également montré jusqu'à quel point l’évolution temporelle du degré d’orientation pouvait être mise en forme. Des méthodes de contrôle optimal ont permis de déterminer le champ THz pour atteindre cet état à la fois à températures nulle et non-nulle. Un autre chapitre présente un nouvel algorithme d’optimisation pour les dynamiques périodiques. Cet algorithme est appliqué à la maximisation du SNR en RMN. Un dernier chapitre est dédié à un article de vulgarisation sur l’effet de la raquette de tennis. Cet effet géométrique peut être observé dans tout corps rigide suffisamment asymétrique. / The goal of this thesis is to apply quantum control techniques to manipulate molecular rotation and to enhance the efficiency of processes in Nuclear Magnetic Resonance.These techniques have been used theoretically and experimentally to control the orientation of a symmetric top molecule by means of THz laser fields. This study has been extended to the case of a long interaction distance between the field and the sample. In this case, the molecule cannot be approximated as isolated. We have also shown the extend to which the time evolution of the degree of orientation can be shaped. Optimal control techniques were used to design the THz field which allows to reach the corresponding dynamics, both at zero and non zero temperatures. Another chapter proposes a new optimization algorithm in the case of periodic quantum dynamics. We apply this algorithm to the maximization of the SNR in NMR. A last chapter is dedicated to a popular paper about the tennis racket effect. This geometric effect can be observed in any asymetric rigid body.