The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.

The objective of this thesis is to develop algorithms for application-oriented input design. This procedure takes the model application into account when designing experiments for system identification. This thesis is divided into two parts. The first part considers the theory of application-oriented input design, with special attention to Model Predictive Control (MPC). We start by studying how to find a convex approximation of the set of models that result in acceptable control performance using analytical methods when controllers with no closed-form control law, for e.g., MPC are employed. The application-oriented input design is formulated in time domain to enable handling of signals constraints. The framework is extended to closed-loop systems where two cases are considered i.e., when the plant is controlled by a general but known controller and for the case of MPC. To this end, an external stationary signal is designed via graph theory. Different sources of uncertainty in application-oriented input design are investigated and a robust application-oriented input design framework is proposed. The second part of this thesis is devoted to the problem of estimating the number of occupants based on the information available to HVAC systems in buildings. The occupancy estimation is first formulated as a two-tier problem. In the first tier, the room dynamic is identified using temporary measurements of occupancy. In the second tier, the identified model is employed to formulate the problem as a fused-lasso problem. The proposed method is further developed to be used as a multi-room estimator using a physics-based model. However, since it is not always possible to collect measurements of occupancy, we proceed by proposing a blind identification algorithm which estimates the room dynamic and occupancy, simultaneously. Finally, the application-oriented input design framework is employed to collect data that is informative enough for occupancy estimation purposes. / <p>QC 20170620</p>

To manufacture safe, effective and affordable medicines with greater efficiency, process analytical technology (PAT) has been introduced by the Food and Drug Agency to encourage the pharmaceutical industry to develop and design well-understood processes. PAT requires chemical imaging techniques to be used to collect process variables for real-time process analysis. Multivariate statistical analysis tools and process control tools are important for implementing PAT in the development and manufacture of pharmaceuticals as they enable information to be extracted from the PAT measurements. Multivariate statistical analysis methods such as principal component analysis (PCA) and independent component analysis (ICA) are applied in this thesis to extract information regarding a pharmaceutical tablet. ICA was found to outperform PCA and was able to identify the presence of five different materials and their spatial distribution around the tablet.Another important area for PAT is in improving the control of processes. In the pharmaceutical industry, many of the processes operate in a batch strategy, which introduces difficult control challenges. Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that has been used extensively to extract chemical and physical information from a product sample based on the scattering effect of light. In this thesis, NIR measurements were incorporated as feedback information into several control strategies. Although these controllers performed reasonably well, they could only regulate the NIR spectrum at a number of wavenumbers, rather than over the full spectrum.In an attempt to regulate the entire NIR spectrum, a novel control algorithm was developed. This controller was found to be superior to the only comparable controller and able to regulate the NIR similarly. The benefits of the proposed controller were demonstrated using a benchmark simulation of a batch reactor.

On rencontre aujourd'hui la vision par ordinateur employée pour la réalisation de nombreuses applications de la robotique moderne. L'un des axes de recherche actuel qui tend à améliorer ces systèmes est centré sur la commande. L'objectif est de proposer des schémas de commande originaux permettant de lier efficacement les informations mesurées par les capteurs de vision aux actions que l'on souhaite réaliser avec les robots. C'est dans cet aspect que s'inscrit ce document en apportant de nouvelles méthodes à la commande robotique classique faisant intervenir la vision, l'asservissement visuel. Le cas de pertes d'informations visuelles pendant la réalisation d'une tâche d'asservissement visuel est étudié. Dans ce sens, deux méthodes de prédiction qui permettent à la tâche d'être réalisée malgré ces pertes sont présentées. Puis une méthode inédite de correction est proposée. Celle-ci permet d'obtenir de meilleurs résultats de prédiction qu'une méthode classique, comme le démontrent des résultats obtenus en simulation et en condition réelle. Enfin, dans le contexte de la réalisation de plusieurs tâches d'asservissement visuel successives, une nouvelle méthode est présentée. Celle-ci exploite les caractéristiques d'un schéma de commande utilisé depuis quelques dizaines d'années dans l'industrie et la recherche, la commande prédictive basée modèle. Des résultats obtenus en simulation proposent de visualiser les effets de cette méthode sur le comportement d'un drone qui embarque une caméra. / The computer vision is used for the achievement of many applications of modern robotics. One of the current research topics that aims to improve these systems is focused on command. The objective consists to propose original control schemes to effectively link the information measured by the vision sensor to the actions that are to be achieved with the robots. This document is part of this look by bringing new methods to classical robotic control involving vision, the visual servoing.The case of visual information losses during the achievement of a visual servoing task is studied. In this sense, two prediction methods that allow the task to be achieved despite these losses are presented. Then a new method of correction is proposed. This provides better prediction results than a conventional method, as shown by the results obtained in simulation and in real conditions.Finally, in the context of the achievement of several successive visual servoing tasks, a new method is presented. This exploits the characteristics of a control scheme used for several decades in industry and research, model based predictive control. The results obtained in simulation propose to see the effect of this method on the behavior of a drone that embeds a camera.

The aim of this research is to affirm the application of closed-loop optimal control for load shifting in plants with electricity tariffs that include time-of-use (TOU) and maximum demand (MD) charges. The water pumping scheme of the Rietvlei water purification plant in the Tshwane municipality (South Africa) is selected for the case study. The objective is to define and simulate a closed-loop load shifting (scheduling) strategy for the Rietvlei plant that yields the maximum potential cost saving under both TOU and MD charges. The control problem is firstly formulated as a discrete time linear open loop optimal control model. Thereafter, the open loop optimal control model is converted into a closedloop optimal control model using a model predictive control technique. Both the open and closed-loop optimal control models are then simulated and compared with the current (simulated) level based control model. The optimal control models are solved with integer programming optimization. The open loop optimal control model is also solved with linear programming optimization and the result is used as an optimal benchmark for comparisons. Various scenarios with different simulation timeouts, switching intervals, control horizons, model uncertainty and model disturbances are simulated and compared. The effect of MD charges is also evaluated by interchangeably excluding the TOU and MD charges. The results show a saving of 5.8% to 9% for the overall plant, depending on the simulated scenarios. The portion of this saving that is due to a reduction in MD varies between 69% and 92%. The results also shows that the closed-loop optimal control model matches the saving of the open loop optimal control model, and that the closed-loop optimal control model compensates for model uncertainty and model disturbances whilst the open loop optimal control model does not. AFRIKAANS : Die doel van hierdie navorsing is om die applikasie van geslote-lus optimale beheer vir las verskuiwing in aanlegte met elektrisiteit tariewe wat tyd-van-gebruik (TVG) en maksimum aanvraag (MA) kostes insluit te bevestig. Die water pomp skema van die Rietvlei water reiniging aanleg in die Tshwane munisipaliteit (Suid-Afrika) is gekies vir die gevalle studie. Die objektief is om 'n geslote-lus las verskuiwing (skedulering) strategie vir die Rietvlei aanleg te definieer en te simuleer wat die maksimum potensiaal vir koste besparing onder beide TVG en MA kostes lewer. Die beheer probleem is eerstens gevormuleer as 'n diskreet tyd lineêre ope-lus optimale beheer model. Daarna is die ope-lus optimale beheer model aangepas na ‘n geslote-lus optimale beheer model met behulp van 'n model voorspellende beheer tegniek. Beide die ope- en geslote-lus optimale beheer modelle is dan gesimuleer en vergelyk met die huidige (gesimuleerde) vlak gebaseerde beheer model. Die optimisering van optimale beheer modelle is opgelos met geheeltallige programmering. Die optimisering van die ope-lus optimale beheer model is ook opgelos met lineêre programmering en die resultaat is gebruik as 'n optimale doelwit vir vergelykings. Verskeie scenarios met verskillende simulasie stop tye, skakel intervalle, beheer horisonne, model onsekerheid en model versteurings is gesimuleer en vergelyk. Die effek van MA kostes is ook geevalueer deur inter uitruiling van die TVG en MA kostes. Die resultate toon 'n besparing van 5. 8% tot 9% vir die algehele aanleg, afhangend van die gesimuleerde scenarios. Die deel van die besparing wat veroorsaak is deur 'n vermindering in MA wissel tussen 69% en 92%. Die resultate toon ook dat die geslote-lus optimale beheer model se besparing dieselfde is as die besparing van die ope-lus optimale beheer model, en dat die geslote-lus optimale beheer model kompenseer vir model onsekerheid en model versteurings, terwyl die ope-lus optimale beheer model nie kompenseer nie. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

Cette thèse concerne l'élaboration de stratégies de commande prédictive pour certaines classes de systèmes dynamiques continus, discrets et hybrides modélisés par des extensions de réseaux de Petri ad hoc. Pour les systèmes continus et en vue de limiter la complexité de calcul inhérente à la forme standard de la commande prédictive, plusieurs améliorations sont proposées. Celles-ci permettent de surmonter le problème de "hill climbing" caractéristique des trajectoires obtenues avec certains réseaux de Petri. Elles assurent également la possibilité d'implémenter la commande en temps réel en adaptant l'horizon de prédiction pour réduire la complexité algorithmique. Enfin, elles permettent de limiter la sollicitation des actionneurs tout en garantissant la stabilité asymptotique du système commandé. Pour les systèmes discrets temporisés et pour éviter l'exploration exhaustive du graphe d'atteignabilité, une méthode de commande est proposée, basée sur la commande prédictive appliquée à une approximation continue du système discret. Enfin pour les systèmes hybrides, une commande prédictive hybride est développée, inspirée de la commande prédictive continue. Les performances de ces différentes stratégies de commande sont évaluées et comparées avec différentes simulations numériques / This thesis concerns the development of predictive control strategies for some classes of continuous, discrete and hybrid dynamic systems modeled by specific extensions of Petri nets. For continuous systems and in order to limit the computational complexity inherent to the standard form of the predictive control, several improvements are proposed. These improvements allow overcoming the problem of hill climbing that characterizes trajectories obtained with some Petri nets. They also ensure the possibility to implement real-time control by adapting the prediction horizon in order to reduce the algorithmic complexity. Finally, they limit actuators solicitation while ensuring the asymptotic stability of the controlled system. For timed discrete systems and in order to avoid the exhaustive exploration of the reachability graph, a control method is proposed, based on the predictive control applied to a continuous approximation of the discrete system. Finally for hybrid systems, hybrid predictive control is developed, inspired by the continuous predictive control. The performance of these different control strategies are evaluated and compared to different numerical simulations.

In South Africa, where 75% of the worlds platinum is produced, electricity tariffs have increased significantly over recent years. This introduces challenges to the energy intensive mineral processing industry. Within the mineral processing chain, run-of-mine ore milling circuits are the most energy-intensive unit processes. Opportunities to reduce the operating costs associated with power consumption through process control are explored in this work. In order to reduce operating costs, demand side management was implemented on a milling circuit using load shifting. Time-of-use tariffs were exploited by shifting power consumption of the milling circuit from more expensive to cheaper tariff periods in order to reduce overall costs associated with electricity consumption. Reduced throughput during high tariff periods was recovered during low tariff periods in order to maintain milling circuit throughput over a week long horizon. In order to implement and evaluate demand side management through process control, a load shifting controller was developed for the non-linear Hulbert model. Implementation of the load shifting controller was achieved through a multi-layered control approach. A regulatory linear MPC controller was developed to address technical control requirements such as milling circuit stability. A supervisory real-time optimizer was developed to meet economic control requirements such as reducing electricity costs while maintaining throughput. Scenarios, designed to evaluate the sensitivities of the load shifting controller, showed interesting results. Mill power set-point optimization was found to be proportionally related to the mineral price. Set-points were not sensitive to absolute electricity costs but rather to the relationships between peak, standard, and off-peak electricity costs. The load shifting controller was most effective at controlling the milling circuit where weekly throughput was between approximately 90% and 100% of the maximum throughput capacity. From an economic point of view, it is shown that for milling circuits that are not throughput constrained, load shifting can reduce operating costs associated with electricity consumption. Simulations performed indicate that realizable cost savings are between R16.51 and R20.78 per gram of unrefined platinum processed by the milling circuit. This amounts to a potential annual cost saving of up to R1.89 m for a milling circuit that processes 90 t/h at a head grade of 3 g/t. / Dissertation (MEng)--University of Pretoria, 2015. / Electrical, Electronic and Computer Engineering / Unrestricted

This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. HVAC on/off behaviors are simulated by using Mathematical Program with Complementary Constraints (MPCCs) and solved in near real-time with a nonlinear solver. Removing HVAC on/off as a discrete variable decreases potential solutions and consequently reduces solve time and increases the probability of reaching a more optimal solution. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day compared to a home with regular temperature control. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. Homes using this home energy optimization will contribute less to the grid peak load and therefore, improve grid stability and reduce the amplitude of load following cycles for utilities. This case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real-time, and mixed empirical / first principles machine learning methods to guide the model structure.

Neste trabalho investiga-se a aplicação de um algoritmo genético multiobjetivo, ferramenta que se destaca por sua flexibilidade e interpretabilidade, na obtenção de fatores de ponderação para aplicação no controle preditivo de torque do motor de indução, ou Model Predictive Torque Control (MPTC). O MPTC busca minimizar a cada instante de atuação uma função custo que representa o sistema, destacando-se pela rápida resposta de torque, facilidade de incorporar restrições e ausência de modulador de tensão. No entanto, essa técnica apresenta fatores de ponderação em sua estrutura de cálculo que não dispõem de métodos analíticos de projeto. Utilizou-se o algoritmo genético de classificação nãodominada, ou Non-dominated Sorting Genectic Algorithm II (NSGA-II), projetado de forma a obter soluções que busquem o compromisso entre o desempenho dinâmico do motor, via minimização das oscilações de torque e fluxo, e a eficiência energética do sistema por meio da minimização da frequência média de chaveamento da eletrônica de potência. Resultados simulados e experimentais mostraram que o conjunto de soluções fornecido pelo NSGA-II é factível e contrapõe as oscilações de torque e de fluxo e a frequência média de chaveamento, cabendo à aplicação desejada a escolha da solução. Com isso, tem-se uma ferramenta de projeto dos fatores de peso do MPTC capaz de incorporar restrições e ajustar vários fatores ao mesmo tempo. / This work investigates the application of a multi-objective genetic algorithm to obtain a set of weighting factors suitable for use in Model Predictive Torque Control (MPTC) of a induction motor variable speed drive. MPTC approach aims at minimizing a cost function at each step, and is highlighted for its fast torque response, facility to incorporate system constraints and the absence of voltage modulators. Nevertheless, MPTC structure presents weighting factors in the cost function which lack of an analytical design procedure. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was designed for a trade-off between torque and flux ripples minimization and minimization of the average switching frequency of the system. Simulated and experimental results showed NSGA-II offered a Pareto set of feasible solutions, so that torque ripple, flux ripple or average switching frequency can be minimized, depending on the solution chosen according to project demand. Thereby, there is a project tool for MPTC weighting factors able to adjust several factor at the same time, incorporating desired restrictions.

The world's energy demand has been rapidly increasing and is projected to continue growing for at least the next two decades. With increasing global energy demand and competition from renewable energy, the oil and gas industry is striving for more efficient petroleum production. Many technical breakthroughs have enabled the drilling industry to expand the exploration to more difficult drilling such as deepwater drilling and multilateral directional drilling. For example, managed pressure drilling (MPD) offers ceaseless operation with multiple manipulated variables (MV) and wired drill pipe (WDP) provides two-way, high-speed measurements from bottom hole and along-string sensors. These technologies have maximum benefit when applied in an automation system or as a real-time advisory tool. The objective of this study is to investigate the benefit of nonlinear model-based control and estimation algorithms with various types of models. This work presents a new simplified flow model (SFM) for bottomhole pressure (BHP) regulation in MPD operations. The SFM is embedded into model-based control and estimation algorithms that use model predictive control (MPC) and moving horizon estimation (MHE), respectively. This work also presents a new Hammerstein-Wiener nonlinear model predictive controller for BHP regulation. Hammerstein-Wiener models employ input and output static nonlinear blocks before and after linear dynamics blocks to simplify the controller design. The control performance of the new Hammerstein-Wiener nonlinear controller is superior to conventional PID controllers in a variety of drilling scenarios. Conventional controllers show severe limitations in MPD because of the interconnected multivariable and nonlinear nature of drilling operations. BHP control performance is evaluated in scenarios such as drilling, pipe connection, kick attenuation, and mud density displacement and the efficacy of the SFM and Hammerstein-Wiener models is tested in various control schemes applicable to both WDP and mud pulse systems. Trusted high-fidelity drilling simulators are used to simulate well conditions and are used to evaluate the performance of the controllers using the SFM and Hammerstein-Wiener models. The comparison between non-WDP (semi-closed loop) and WDP (full-closed loop) applications validates the accuracy of the SFM under the set of conditions tested and confirms comparability with model-based control and estimation algorithms. The SFM MPC maintains the BHP within ± 1 bar of the setpoint for each investigated scenario, including for pipe connection and mud density displacement procedures that experience a wider operation range than normal drilling.