Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The development of fast and efficient processors, programmable devices and high power semiconductors has led to the increased use of semiconductors directly in the power supply path in order to achieve strict power quality standards. New and advanced algorithms are used in the process and calculated on-line to bring about the required fast response to voltage variations. Losses in high voltage semiconductors increase with increased operating frequencies. A balance between semiconductor power losses and power quality is achieved through control of power semiconductor switching frequencies. A predictive control algorithm to achieve high power quality and limit the power losses in the high power semiconductor switches through switching frequency control is discussed for a tap switched voltage regulator. The quality of power, voltage regulator topology and the control algorithm are discussed. Simulation results of output voltage and current are shown when the control algorithm is used to control the regulator. These results are verified by practical measurements on a synchronous buck converter. / AFRIKAANSE OPSOMMING: Die ontwikkeling van vinnige en doeltreffende verwerkers, programmeerbare toestelle en hoëdrywings halfgeleiers het gelei tot 'n groter gebruik van halfgeleiers direk in die kragtoevoer pad om streng elektriese toevoer kwaliteit standaarde te bereik. Nuwe en gevorderde algoritmes word gebruik in die proses en word aan-lyn bereken om die nodige vinnige reaksie tot spanningswisselinge te gee. Verliese in hoë-spannings halfgeleiers verhoog met hoër skakel frekwensies. 'n Balans tussen die halfgeleier drywingsverliese en spanningskwalteit is behaal deur die skakel frekwensie in ag te neem in die beheer. 'n Voorspellinde-beheer algoritme om ‘n hoë toevoerkwaliteit te bereik en die drywingsverliese in die hoëdrywingshalfgeleier te beperk, deur skakel frekwensie te beheer, is bespreek vir 'n tap-geskakelde spanning reguleerder. Die toevoerkwaliteit, spanningsreguleerder topologie en die beheer algoritme word bespreek. Simulasie resultate van die uittree-spanning en stroom word getoon wanneer die beheer algoritme gebruik word om die omsetter te beheer. Hierdie resultate is deur praktiese metings op 'n sinkrone afkapper.

Nous observons ces dernières années un besoin grandissant dans l’industrie pour des robots capables d’interagir et de coopérer dans des environnements confinés. Cependant, aujourd’hui encore, la définition de trajectoires sûres pour les robots industriels doit être faite manuellement par l’utilisateur et le logiciel ne dispose que de peu d’autonomie pour réagir aux modifications de l’environnement. Cette thèse vise à produire une structure logicielle innovante pour gérer l’évitement d’obstacles en temps réel pour des robots manipulateurs évoluant dans des environnements dynamiques. Nous avons développé pour cela un algorithme temps réel de génération de trajectoires qui supprime de façon automatique l’étape fastidieuse de définition d’une trajectoire sûre pour le robot.La valeur ajoutée de cette thèse réside dans le fait que nous intégrons le problème de contrôle optimal dans le concept de hiérarchie de tâches pour résoudre un problème d’optimisation non-linéaire efficacement et en temps réel sur un système embarqué aux ressources limitées. Notre approche utilise une commande prédictive (MPC) qui non seulement améliore la réactivité de notre système mais présente aussi l’avantage de pouvoir produire une bonne approximation linéaire des contraintes d’évitement de collision. La stratégie de contrôle présentée dans cette thèse a été validée à l’aide de plusieurs expérimentations en simulations et sur systèmes réels. Les résultats démontrent l’efficacité, la réactivité et la robustesse de cette nouvelle structure de contrôle lorsqu’elle est utilisée dans des environnements dynamiques. / In the field of industrial robots, there is a growing need for having cooperative robots that interact with each other and share work spaces. Currently, industrial robotic systems still rely on hard coded motions with limited ability to react autonomously to dynamic changes in the environment. This thesis focuses on providing a novel framework to deal with real-time collision avoidance for robots performing tasks in a dynamic environment. We develop a reactive trajectory generation algorithm that reacts in real time, removes the fastidious optimization process which is traditionally executed by hand by handling it automatically, and provides a practical way of generating locally time optimal solutions.The novelty in this thesis is in the way we integrate the proposed time optimality problem in a task priority framework to solve a nonlinear optimization problem efficiently in real time using an embedded system with limited resources. Our approach is applied in a Model Predictive Control (MPC) setting, which not only improves reactivity of the system but presents a possibility to obtain accurate local linear approximations of the collision avoidance constraint. The control strategies presented in this thesis have been validated through various simulations and real-world robot experiments. The results demonstrate the effectiveness of the new control structure and its reactivity and robustness when working in dynamic environments.

L'énergie est une ressource clé dans les réseaux de capteurs sans fil (WSNs), en particulier lorsque les nœuds capteurs sont alimentés par des batteries. Cette thèse s'inscrit dans le contexte de la réduction de la consommation de l'énergie d'un réseau de capteurs au niveau application construite au-dessus de ce réseau, grâce à des stratégies de contrôle, en temps réel et de façon dynamique. La première stratégie de gestion de l'énergie considérée s'appuie sur le contrôle prédictif (MPC). Le choix de MPC est motivé par les objectifs globaux qui sont de réduire la consommation d'énergie de l'ensemble des nœuds capteurs tout en assurant un service donné, nommé mission, pour le réseau de capteurs. En outre, un ensemble de contraintes sur les variables de contrôle binaires et sur les nœuds capteur doit être rempli. La deuxième stratégie de gestion de l'énergie au niveau de l'application utilise une approche de contrôle hybride (HDS). Ce choix est motivé par la nature inhérente du système WSN qui est par essence hybride, en particulier lorsque l'on s'intéresse à la gestion de l'énergie. La nature hybride vient essentiellement de la combinaison de processus physiques continus tels la charge et décharge des batteries des nœuds; tandis que la partie discrète est liée à la modification des modes de fonctionnement et l'état Inaccessible des nœuds. Les stratégies proposées sont évaluées et comparées en simulation sur des différents scenarios réalistes. Elles ont aussi \'et\'e mises en œuvre sur un banc d'essai réel et les résultats obtenus ont été discutés. / Energy is a key resource in Wireless Sensor Networks (WSNs), especially when sensor nodes are powered by batteries. This thesis is investigates how to save energy of the whole WSN, at the application level, thanks to control strategies, in real time and in a dynamic way. The first energy management strategy investigated is based on Model Predictive Control (MPC). The choice of MPC is motivated by the global objectives that are to reduce the energy consumption of the set of sensor nodes while ensuring a given service, named mission, for the sensor network. Moreover, a set of constraints on the binary control variables and on the sensor modes must be fulfilled. The second energy management strategy at the application level is based on a Hybrid Dynamical System (HDS) approach. This choice is motivated by the hybrid inherent nature of the WSN system when energy management is considered. The hybrid nature basically comes from the combination of continuous physical processes, namely, the charge / discharge of the node batteries; while the discrete part is related to the change in the functioning modes and the Unreachable condition of the nodes. The proposed strategies are evaluated and compared in simulation on a realistic test-case. Lastly, they have been implemented on a real test-bench and the results obtained have been discussed.

Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of ‘sample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.

Esta dissertação de mestrado apresenta um novo esquema de controle de taxa hierárquico – HRC – para o padrão MVC – extensão para vídeos de múltiplas vistas do padrão H.264 – com objetivo de melhorar o aproveitamento da largura de banda oferecida por um canal entregando o vídeo comprimido com a melhor qualidade possível. Este esquema de controle de taxa hierárquico foi concebido para controlar de forma conjunta os níveis de quadro e de unidades básicas (BU). O esquema proposto explora a correlação existente entre as distribuições das taxas de bits em quadros vizinhos para predizer de forma eficiente o comportamento dos futuras bitrates através da aplicação de um controle preditivo baseado em modelos – MPC – que define uma ação de controle apropriada sobre as ações de adaptação do parâmetro de quantização (QP). Para prover um ajuste em granularidade fina, o QP é adicionalmente adaptado internamente para cada quadro por um processo de decisão de Markov (MDP) implementado em nível de BU capaz de considerar mapas com Regiões de Interesse (RoI). Um retorno acoplado aos dois níveis supracitados é realizado para garantir a consistência do sistema. Aprendizagem por Reforço é utilizada para atualizar os parâmetros do Controle Preditivo baseado em Modelos e do processo de decisão de Markov. Resultados experimentais mostram a superioridade da utilização do esquema de controle proposto, comparado às soluções estado-da-arte, tanto em termos de precisão na alocação de bits quanto na otimização da razão taxa-distorção, entregando um vídeo de maior qualidade visual nos níveis de quadros e de BUs. / This master thesis presents a novel Hierarchical Rate Control – HRC – for the Multiview Video Coding standard targeting an increased bandwidth usage and high video quality. The HRC is designed to jointly address the rate control at both framelevel and Basic Unit (BU)-level. This scheme is able to exploit the bitrate distribution correlation with neighboring frames to efficiently predict the future bitrate behavior by employing a Model Predictive Control that defines a proper control action through QP (Quantization Parameter) adaptation. To provide a fine-grained tuning, the QP is further adapted within each frame by a Markov Decision Process implemented at BU-level able to take into consideration a map of the Regions of Interest. A coupled frame/BU-level feedback is performed in order to guarantee the system consistency. A Reinforcement Learning method is responsible for updating the Model Predictive Control and the Markov Decision Process parameters. Experimental results show the superiority of the Hierarchical Rate Control compared to state-of-the-art solutions, in terms of bitrate allocation accuracy and rate-distortion, while delivering smooth video quality at both frame and Basic Unit levels.

abstract: Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2014

abstract: Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations. A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion.

Cette thèse porte principalement sur la coordination des systèmes distribués, avec une attention particulière pour les systèmes de production d'électricité multi-énergiques. Aux fins de l'optimalité, ainsi que l'application des contraintes, la commande prédictive (MPC-Model Predictive Control) est choisi comme l'outil sous-jacent, tandis que les éoliennes, piles à combustible, panneaux photovoltaïques et les centrales hydroélectriques sont considérés comme les sources d'énergie a être contrôlées et coordonnées. En premier lieu, une application de la commande MPC dans un microréseau électrique est proposée, illustrant comment assurer une performance appropriée pour chaque unité de génération et de soutien. Dans ce contexte, une attention particulière est accordée à la production de puissance maximale par une éolienne, en prenant une commande basée sur un observateur quand la mesure de la vitesse du vent est disponible. Ensuite, les principes de contrôle distribué coordonnés, en considérant une formulation à base de la commande MPC, sont pris en considération pour le contexte des systèmes à grande taille. Ici, une nouvelle approche pour la coordination par prix avec des contraintes est proposée pour la gestion des contrôleurs MPC locaux, chacun d'eux étant typiquement associé à une unité de génération. En outre, le calcule des espace invariants a été utilisé pour l'analyse de la performance pour le système à boucle fermée, à la fois pour les schémas MPC centralisée et coordination par prix. Finalement, deux cas d'études dans le contexte des systèmes de génération d'électricité sont inclus, en illustrant la pertinence de la stratégie de commande coordonnée proposée. / This thesis is mainly about coordination of distributed systems, with a special attention to multi-energy electric power generation ones. For purposes of optimality, as well as constraint enforcement, Model Predictive Control (MPC) is chosen as the underlying tool, while wind turbines, fuel cells, photovoltaic panels, and hydroelectric plants are mostly considered as power sources to be controlled and coordinated. In the first place, an application of MPC to a micro-grid system is proposed, illustrating how to ensure appropriate performance for each generator and support units. In this context, a special attention is paid to the maximum power production by a wind turbine, via an original observer-based control when no wind speed measurement is available. Then, the principles of distributed-coordinated control, when considering an MPC-based formulation, are considered for the context of larger scale systems. Here, a new approach for price-driven coordination with constraints is proposed for the management of local MPC controllers, each of them being associated to one power generation unit typically. In addition, the computation of invariant sets is used for the performance analysis of the closed- loop control system, for both centralized MPC and price-driven coordination schemes. Finally, a couple of case studies in the field of power generation systems is included, illustrating the relevance of the proposed coordination control strategy.

In recent years, rules and regulations regarding fuel consumption of vehicles and the amount of emissions produced by them are becoming stricter. This has led the automotive industry to develop more advanced solutions to propel vehicles to meet the legal requirements. The Hybrid Electric Vehicle is one of the solutions that is becoming more popular in the automotive industry. It consists of an electrical driveline combined with a conventional powertrain, propelled by either a diesel or petrol engine. Two power sources create the possibility to choose when and how to use the power sources to propel the vehicle. The strategy that decides how this is done is referred to as an energy management strategy. Today most energy management strategies only try to reduce fuel consumption using models that describe the steady state behaviour of the engine. In other words, no reduction of emissions is achieved and all transient behaviour is considered negligible.  In this thesis, an energy management strategy incorporating engine dynamics to reduce fuel consumption and nitrogen oxide emissions have been designed. First, the models that describe how fuel consumption and nitrogen oxide emissions behave during transient engine operation are developed. Then, an energy management strategy is developed consisting of a model predictive controller that combines the equivalent consumption minimization strategy and convex optimization. Results indicate that by considering engine dynamics in the energy management strategy, both fuel consumption and nitrogen oxide emissions can be reduced. Furthermore, it is also shown that the major reduction in fuel consumption and nitrogen oxide emissions is achieved for short prediction horizons.