Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia. / During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.

Dans cette thèse nous proposons des algorithmes de contrôle commande optimaux ayant pour but d’aider au bon choix des systèmes multi sources et leur utilisation optimale dans les bâtiments. L'estimation des charges de chauffage est transformée en un problème de contrôle où le régulateur calcule la charge de chauffage optimale du bâtiment. Le régulateur proposé pour ce but est de type Model Predictive Programming (MPP), qui est obtenu en modifiant l’algorithme de type Model Predictive Control (MPC). Comme le MPP requiert un modèle du bâtiment d'ordre réduit, nous proposons une méthode de modélisation par projection des paramètres sur une structure fixe obtenue à partir des connaissances physiques. Pour le contrôle du système multi sources, nous proposons un système de gestion technique du bâtiment (GTB) qui est divisé en deux : un régulateur de la température du bâtiment et un contrôleur des sources. Pour la régulation thermique on utilise l’algorithme MPC, pour lequel nous proposons une nouvelle fonction de coût, car la fonction classique ne minimise pas la consommation d'énergie. La fonction de coût proposée permet de maintenir le confort thermique avec une consommation d'énergie minimale. Nous la formulons de telle façon qu’elle puisse être optimisée en utilisant la Programmation Linéaire (PL). Pour pouvoir utiliser la PL, nous proposons une linéarisation du modèle, basée sur des connaissances physiques, qui permet d'utiliser le modèle sur toute la plage de fonctionnement. Pour le contrôle des sources, nous proposons une solution qui prend en compte la commande MPC afin d'utiliser les ressources d'énergie plus efficacement. La GTB proposée est évaluée en émulation sur la maison Mozart et comparée avec deux GTB basées sur des régulateurs PID. Les résultats obtenus montrent que la GTB proposée a toujours maintenu le confort thermique dans le bâtiment, a réduit la consommation d'énergie et l'usure des pompes hydrauliques et de la pompe à chaleur. / This thesis proposes methods and solutions to improve the choice and the optimal use of renewable energies in buildings. The heating load assessment is transformed into a control problem where the regulator calculates the optimal heating load of the building. The proposed regulator for this aim is Model Predictive Programming (MPP), which is obtained by modifying Model Predictive Control (MPC). The required information by MPP is a low order building model and data records of the local weather. Therefore, we propose a modelling method in which the detailed model of the building is projected on a reduced order model having its structure obtained from physical knowledge. For the control of the multi source system, we proposed a Building Energy Management System (BEMS) which is divided in two parts: the first for the building temperature control and the second for the source control. For building thermal control we utilize MPC, for which we propose a new cost function because the classical one does not minimize the energy consumption. The proposed cost function permits to maintain the thermal comfort with minimal energy consumption. We formulate this function such that it can be optimized by using Linear Programming (LP) algorithm. To be able to use LP we give a solution to linearization of the building model based on the physical knowledge, which permits to use the model on the entire operating range. For the source control, we propose a solution which takes into account the command given by MPC in order to use the energy resources more effectively. The proposed control system is evaluated and compared with two PID based BEMS, against comfort and energetic criteria. The evaluation is performed in emulation on a reference detached house. The obtained results show that the proposed control system always maintains the thermal comfort in the building, reduces the energy consumption and the wear and tear of the hydraulic and heat pumps from the heating system.

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.

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

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.

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.

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