Este trabalho tem como objetivo principal o desenvolvimento de uma estratégia de integração da otimização com o controle preditivo multivariável em uma camada. Os problemas de controle e otimização econômica são resolvidos simultaneamente em um mesmo algoritmo. A função objetivo econômica foi inserida no controlador na sua forma diferencial, ou seja, o gradiente da função objetivo econômica. O método foi testado por simulação para o caso do sistema reator regenerador da UFCC (Unit of Fluid Catalytic Cracker). Esta dissertação descreve a estratégia de otimização integrada ao controlador preditivo cuja função objetivo incorpora componentes dinâmicos e estáticos. Para a determinação das condições ótimas do processo no estado estacionário do conversor (unidade de craqueamento catalítico) foi utilizado um modelo empírico do processo. A melhor trajetória para conduzir o processo para o seu ponto ótimo de operação, maximizando lucro ou produto de maior valor agregado, desde que não sejam violadas as restrições de processo, é predita utilizando um modelo dinâmico, obtido através de dados de testes em degrau em um modelo rigoroso. Este modelo linear possibilitou a obtenção das funções de transferência do processo e o modelo em variáveis de estado. O ponto ótimo que é obtido na execução deste algoritmo, leva em consideração a não violação das restrições das variáveis manipuladas e controladas do processo, tanto para o estado estacionário como para o transiente do problema. O problema de otimização não linear resultante é resolvido através de uma rotina de programação quadrática da biblioteca do Matlab. Uma segunda alternativa apresentada para a estratégia de otimização deste trabalho, é a inclusão do gradiente reduzido na função objetivo do controlador quando são observadas violações das restrições das variáveis controladas. Os resultados simulados através de um modelo não linear rigoroso (Moro&Odloak,1995) mostram um bom desempenho dos algoritmos aqui desenvolvidos tanto com relação aos benefícios econômicos como na estabilização da unidade. / This dissertation aims to develop a strategy to integrate the optimization problem of the plant into the model predictive controller in a one layer strategy, for the real time optimization or online optimization. The control and the optimization of the process are computed simultaneously in the same algorithm. The gradient of the economic objective function is included in the cost function of the controller instead of in its regular form. Thereby, this work describes a predictive control strategy, which can be classified as a one layer strategy and whose objective function has to be optimized obeying constraints, which incorporates dynamic and static components. The optimal conditions of the process in the steady state are defined through the use of an empirical process model. Furthermore, the best trajectory to be followed in order to reach the optimal conditions, without violating the constraints, maximizing profit or the production of its more valuable product, is predicted through the use of the dynamic model, that can be obtained through a plant step test. As a result transfer function and state space models are obtained. The optimal operation point is achieved through the execution of the proposed algorithm. Therefore, the solution to the optimization/control problem will always be in a feasible region, in other words, without violating the process manipulated or controlled variable constraints for both stationary and transient states of the problem. The non-linear optimization problem resulted from the implementation of the proposed algorithm is solved through the quadratic programming routine from the Matlab library. The second online optimization strategy proposed in this work is one that considers the reduced gradient method algorithm modified to evaluate the predicted trajectory. As a result, any violation of the manipulated or controlled variable constraints is prevented and this variable is not considered in the next step of the calculation of the predicted trajectory or even in the search direction of the optimization. Finally the simulations results obtained through the use of a nonlinear rigorous model (Moro&Odloak,1995) presents good performance for the algorithms here proposed, not only related to economic benefits, but also in order to stabilize the unit.

Neste trabalho foram desenvolvidas duas técnicas de sintonia para controladores preditivos por modelo. Ambas visam minimizar a soma do erro quadrático entre respostas do sistema em malha fechada e trajetórias de referência pré-definidas; a primeira resolve um problema de otimização lexicográfica enquanto a segunda resolve um problema de otimização de compromisso. As vantagens dos métodos apresentados são: maior automatização, definição de objetivos de sintonia intuitiva que considera especificações na dinâmica do processo, uma métrica no domínio do tempo e é capaz de incluir o conhecimento do engenheiro de controle em uma técnica de sintonia confiável. Um estudo de caso no sistema de craqueamento catalítico ilustrou a flexibilidade de definição dos objetivos da técnica lexicográfica. Um estudo de caso sobre uma coluna de fracionadora de óleo pesado em malha fechada com um controlador preditivo por modelo comparou ambas as estratégias de sintonia desenvolvidas aqui e pode-se concluir que a técnica lexicográfica dá prioridade aos objetivos importantes enquanto a técnica de compromisso calcula uma solução média, com respeito aos objetivos. A técnica de compromisso foi comparada a um método de sintonia da literatura quanto a aplicação em um controlador preditivo de horizonte infinito com targets para as entradas e controle por faixas das saídas com uma coluna de destilação. Observou-se que a técnica desenvolvida aqui é computacionalmente mais rápida e não requer a escolha de uma solução não-dominada dentre um conjunto de soluções de Pareto. Aplicações reais de controle preditivo são severamente afetadas por incerteza de modelo. Estendeu-se as técnicas desenvolvidas aqui para considerar o caso de incerteza multi-planta, calculando parâmetros de sintonia robustos para controladores nominais, visando tratar o compromisso entre performance e estabilidade e robustez da malha fechada. Um controlador preditivo de horizonte infinito foi sintonizado de forma robusta e comparado com um controlador preditivo robusto em malha fechada com um modelo de separadora C3/C4. Observou-se que este consegue controlar melhor o processo, entretanto, tem um tempo de computação duas ordens de grandeza maior que o controlador nominal, em operação on-line. / Two multi-objective optimization based tuning techniques for Model Predictive Control (MPC) were developed. Both take into account the sum of the squared errors between closed-loop trajectories and reference responses based on pre-defined goals as tuning objectives; one solves a lexicographic optimization to obtain an optimum set of tuning parameters (LTT), whereas the other solves a compromise optimization problem (CTT). The main advantages are an automated framework, and straightforward goal definition, which are capable of taking into account a specification on the process dynamics, a time-domain metrics, and of embedding the control engineers knowledge into a reliable approach. A fluid catalytic cracking tuning case study unveiled the goal definition flexibility of the LTT, with respect to output tracking and variable coupling. A heavy oil fractionator in closed-loop with a MPC case study compared both tuning techniques developed here, and it was observed that the LTT in fact prioritizes the main objectives, whereas the CTT yields an average solution, in terms of the tuning objectives. The CTT was compared to another multi-objective tuning technique from the literature, in the tuning of a MPC with input targets and output zone control in closed-loop with a crude distillation unit model. The simulation results showed that the CTT allows for faster results, regarding the computational time to compute the tuning parameters and there is no need of a posteriori decisions to select the best non-dominated solution. Real MPC applications are strongly hindered by model uncertainty. This limitation was addressed by the extension of the tuning techniques to account for multi-plant model uncertainty, thus obtaining optimum robustly tuned parameters for nominal controllers, addressing the trade-off between robustness and performance. A robustly tuned Infinite Horizon MPC (IHMPC) was compared to a Robust IHMPC, in closed-loop with a C3/C4 splitter system model. It was observed in a simulation that even though the latter yields better output responses, it is two orders of magnitude slower than the former in online operation.

L'étendue des mouvements que les robots humanoïdes peuvent réaliser est fortement limitée par des contraintes dynamiques. Une loi de commande qui ne prend pas en compte ses res- trictions, d'une manière ou autre, ne va pas réussir d'éviter une chute. La Commande Prédictive est capable de considérer les contraintes sur l'état et le contrôle de manière explicite, ce qui la rend particulièrement appropriée pour le contrôle des mouvements des robots marcheurs.Nous commençons par dévoiler la structure spécifique de ces contraintes, démontrant notamment l'importance des appuis au sol. Nous développons ensuite une condition suffisante pour l'évitement d'une chute et nous proposons une loi de commande prédictive qui y réponde. Cette formulation nous sert ensuite pour la conception des contrôleurs pratiques, capables d'un contrôle plus efficace et plus robuste des robots marcheurs humanoïdes. / The range of motions that humanoid robots are able to realize is strongly limited by inherent dynamical constraints so that any control law that does not consider these limitations, in one way or another, will fail to avoid falling. The Model Predictive Control (MPC) technique is capable of handling constraints on the state and the control explicitly, which makes it highly apt for the control of walking robots.We begin by unveiling the specific structure of these constraints, stressing especially the impor- tance of the supports on the ground. We give thereupon a sufficient condition for keeping balance and formulate an MPC law that complies with it. This formulation serves us then for the design of practicable controllers capable of more efficient and more robust control of humanoid robots.

Dans l'objectif de réduire les consommations énergétiques des bâtiments et de diminuer leur impact sur le réseau électrique, il est utile de disposer de stratégies de gestion énergétique en temps réel. Il s'agit en effet d'un verrou clé dans la perspective des réseaux intelligents (« smart grids ») et des programmes de gestion de la demande (« demand response »). Cette thèse propose ainsi le développement de stratégies de gestion en temps réel du chauffage électrique d'un bâtiment énergétiquement performant en période de pointe électrique. Tout d'abord, ces stratégies nécessitent l'utilisation et le développement de plusieurs modèles, à savoir un modèle de prévision météorologique, un modèle d'occupation et un modèle énergétique dynamique du bâtiment. Ensuite, dans l'objectif d'un suivi fiable des performances énergétiques et pour un pilotage optimal des installations, le calibrage du modèle de bâtiment à partir de relevés in situ est préférable. Une nouvelle méthodologie, basée sur un criblage des paramètres incertains et sur l'utilisation d'une méthode d'inférence bayésienne (calcul bayésien approché) a ainsi été développée. Enfin, deux méthodes d'optimisation ont été étudiées pour le développement de stratégies de régulation adaptées au temps réel. La première repose sur une méthode d'optimisation hors-ligne dont l'objectif est d'approximer les résultats d'une stratégie optimale calculée par une méthode d'optimisation exacte et ainsi identifier des lois de commandes simplifiées. La deuxième méthode repose quant à elle sur la commande prédictive et l'adaptation au temps réel de la commande optimale sous contraintes d'état et de commande utilisant la pénalisation intérieure. Une maison de la plateforme INCAS de l'Institut National de l'Énergie Solaire (INES) a été utilisée comme cas d'application pour étudier par simulation les différentes stratégies développées. / To reach the objectives of reducing the energy consumption of buildings and decreasing their impact on the electrical grid, it is necessary to elaborate real time control strategies in view of smart grids and demand response programs. In this context, this thesis aims at developing real time control strategies for electric load shifting in energy efficient buildings. First, these strategies require appropriate models regarding weather forecast, occupants' behaviour and building energy simulation. Then, in order to improve the reliability of building energy simulation and to ensure optimal control of facilities, a calibration process of the model based on on-site measurements is recommended. In this way a new methodology was developed , based on a screening technique and a bayesian inference method (approximate bayesian computation). Finally, two optimisation techniques were studied to develop real time control strategies. The first technique was based on offline optimisation methods. The principle is to approximate optimisation results (and more specifically model based predictive controllers results) and to extract simplified control strategies. The second method consisted in using model predictive control and, more precisely, in solving in real time a state and input constrained optimal control problem by interior penalty methods. An actual experimental passive house being part of the INCAS platform built by the National Solar Energy Institute (INES) was used to study by numerical simulation the different strategies developed.

Garantir le confort thermique des bâtiments est directement lié à la consommation d'énergie. Dans les zones tropicales, les systèmes de refroidissement représentent l'un des postes les plus gourmands en énergie. Afin de réduire la consommation d'énergie mondiale, il est primordial d'améliorer l'efficacité de ces systèmes ou bien de développer de nouvelles méthodes de production de froid. Une installation de refroidissement solaire basé sur le cycle à absorption est une alternative pour réduire les émissions de gaz à effet de serre et la consommation d'électricité. Contrairement aux systèmes classiques de refroidissement à compression mécanique, la production de froid par absorption est un système complexe composé de plusieurs composants comme des panneaux solaires, un ballon de stockage, une tour de refroidissement et une machine à absorption. Outre le dimensionnement des composants, ce système complexe nécessite des actions de contrôle pour être efficace parce que la coordination entre le stockage d'eau chaude, la production et la consommation du froid est nécessaire. Le but de cette thèse est de proposer une structure producteur-consommateur d'énergie basée sur la commande prédictive (MPC). Le système de refroidissement par absorption solaire est considéré comme faisant partie de ce système de production-consommation d'énergie, le système de stockage d'eau chaude est le producteur et la machine à absorption qui distribue de l'eau froide au bâtiment est l'un des consommateurs. Pour que la structure de commande soit modulaire, la coordination entre les sous-systèmes est réalisée en utilisant une approche de partitionnement où des contrôleurs prédictifs locaux sont conçus pour chacun des sous-systèmes. Les contrôleurs des consommateurs calculent un ensemble de profils de demande d'énergie. Ces profils sont ensuite envoyés au contrôleur du producteur qui sélectionne le profil qui minimise le coût global. Dans une première partie, l'approche proposée est testée sur un modèle linéaire simplifié composé d'un producteur et plusieurs consommateurs. Dans une deuxième partie, un cas plus complexe est étudié. Un modèle simplifié d'un système de refroidissement à absorption est évaluée en utilisant l'outil de simulation TRNSYS. Le modèle de production n'est plus linéaire, il est décrit par un modèle non linéaire hybride qui augmente la complexité du problème d'optimisation. Les résultats des simulations montrent que la sous-optimalité induite par la méthode est faible. De plus, la performance de l'approche atteint les objectifs de commande tout en respectant les contraintes. / To guarantee thermal comfort in buildings is directly related to energy consumption. In tropical climates, cooling systems for buildings represent one of the largest energy consumers. Therefore, as energy consumption is a major concern around the world, it is important to improve the systems efficiency or seeking new methods of cooling production. A solar cooling installation based on the absorption cycle is an alternative to mitigate greenhouse gas emissions and electricity consumption. In contrast to conventional vapor-compression based cooling systems, the absorption cooling production involves a complex system composed of several components as collector panel, storage tank, cooling tower and absorption chiller. Besides the sizing of the components, this complex system requires control actions to be efficient as a coordination between hot water storage, cooling water production and consumption is necessary. The aim of this research is to propose a management approach for a production-consumption energy system based on Model Predictive Control (MPC). The solar absorption cooling system is seen as part of this production-consumption energy system where the hot water storage system is the producer and the chiller-building system is one of the consumers. In order to provide modularity to the control structure, the coordination between the subsystems is achieved by using a partitioning approach where local predictive controllers are developed for each of the subsystems. The consumer controllers compute a set of energy demand profiles sent to the producer controller which selects the profile that better minimize the global optimization cost. In a first part, the proposed approach is tested on a simplified linear model composed of one producer and several consumers. In a second part, a more complex case is studied. A simplified model of an absorption cooling system is evaluated using the simulation tool TRNSYS. The producer model is no longer linear, instead it is described by a nonlinear hybrid model which increases the complexity of the optimization problem. The simulations results show that the suboptimality induced by the method is low and the control strategy fulfills the objectives and constraints while giving good performances.

The concepts of engine downsizing and down-speeding offer reductions in CO2 emissions from passenger cars. These reductions are achieved by reducing pumping and friction losses at part-load operation. Conventionally, rated torque and power for downsized units are recovered by means of turbocharging. The transient response of such engines is, however, affected by the static and dynamic characteristics of the turbo-machinery. Recent advances in engine simulation and control tools have been employed for the purpose of the research reported in this thesis to identify and verify possible air-path enhancements. A systematic method for evaluating various turbocharger assistance concepts is proposed and discussed in this thesis. To ensure a fair comparison of selected candidate systems, an easily reconfigurable controller providing a close-to-optimal operation, while satisfying physical limits, is formulated. This controller is based on the Model Predictive Control framework and uses a linearised mean value model to optimise the predicted behaviour of the engine. Initially, the controller was applied to a 1D simulation model of a conventional light-duty Diesel engine, for which the desired closed-loop features were verified. This procedure was subsequently applied to various air-path enhancement systems. In this thesis, a turbocharger electric assistance and various concepts based on compressed gas injection were considered. The capability of these systems to improve engine response during third gear tip-in manoeuvre was quantified. This investigation was also complemented with a parametric study of how effectively each of the considered methods used its available resources. As a result, injecting compressed gas into the exhaust manifold was identified as an effective method, which to date has attracted limited attention from engine research community. The effectiveness of the exhaust manifold assistance was experimentally verified on a light-duty Diesel engine. The sensitivity of the improvements to compressed gas supply parameters was also investigated. This led to the development of the BREES system: a low component count, compressed gas based system for reducing turbo-lag. It was shown that during braking manoeuvres a tank can be charged to the level sufficient for a subsequent boost assistance event. Such a functionality was implemented with a very limited set of additional components and only minor changes to the standard engine control.

Distributed parameter systems (DPS) comprise an important class of engineering systems ranging from "traditional" such as tubular reactors, to cutting edge processes such as nano-scale coatings. DPS have been studied extensively and significant advances have been noted, enabling their accurate simulation. To this end a variety of tools have been developed. However, extending these advances for systems design is not a trivial task . Rigorous design and operation policies entail systematic procedures for optimisation and control. These tasks are "upper-level" and utilize existing models and simulators. The higher the accuracy of the underlying models, the more the design procedure benefits. However, employing such models in the context of conventional algorithms may lead to inefficient formulations. The optimisation and control of DPS is a challenging task. These systems are typically discretised over a computational mesh, leading to large-scale problems. Handling the resulting large-scale systems may prove to be an intimidating task and requires special methodologies. Furthermore, it is often the case that the underlying physical phenomena span various temporal and spatial scales, thus complicating the analysis. Stiffness may also potentially be exhibited in the (nonlinear) models of such phenomena. The objective of this work is to design reliable and practical procedures for the optimisation and control of DPS. It has been observed in many systems of engineering interest that although they are described by infinite-dimensional Partial Differential Equations (PDEs) resulting in large discretisation problems, their behaviour has a finite number of significant components , as a result of their dissipative nature. This property has been exploited in various systematic model reduction techniques. Of key importance in this work is the identification of a low-dimensional dominant subspace for the system. This subspace is heuristically found to correspond to part of the eigenspectrum of the system and can therefore be identified efficiently using iterative matrix-free techniques. In this light, only low-dimensional Jacobians and Hessian matrices are involved in the formulation of the proposed algorithms, which are projections of the original matrices onto appropriate low-dimensional subspaces, computed efficiently with directional perturbations.The optimisation algorithm presented employs a 2-step projection scheme, firstly onto the dominant subspace of the system (corresponding to the right-most eigenvalues of the linearised system) and secondly onto the subspace of decision variables. This algorithm is inspired by reduced Hessian Sequential Quadratic Programming methods and therefore locates a local optimum of the nonlinear programming problem given by solving a sequence of reduced quadratic programming (QP) subproblems . This optimisation algorithm is appropriate for systems with a relatively small number of decision variables. Inequality constraints can be accommodated following a penalty-based strategy which aggregates all constraints using an appropriate function , or by employing a partial reduction technique in which only equality constraints are considered for the reduction and the inequalities are linearised and passed on to the QP subproblem . The control algorithm presented is based on the online adaptive construction of low-order linear models used in the context of a linear Model Predictive Control (MPC) algorithm , in which the discrete-time state-space model is recomputed at every sampling time in a receding horizon fashion. Successive linearisation around the current state on the closed-loop trajectory is combined with model reduction, resulting in an efficient procedure for the computation of reduced linearised models, projected onto the dominant subspace of the system. In this case, this subspace corresponds to the eigenvalues of largest magnitude of the discretised dynamical system. Control actions are computed from low-order QP problems solved efficiently online.The optimisation and control algorithms presented may employ input/output simulators (such as commercial packages) extending their use to upper-level tasks. They are also suitable for systems governed by microscopic rules, the equations of which do not exist in closed form. Illustrative case studies are presented, based on tubular reactor models, which exhibit rich parametric behaviour.

The increasing penetration of wind power into the power system dominated by variable-speed wind turbines among the installed wind turbines will require further development of control methods. Power electronic converters are widely used to improve power quality in conjunction with the integration of variable speed wind turbines into the grid. In this thesis, a detailed model of the Predictive Current Control (PCC) method will be descripts for the purpose of control of the grid-connected converter. The injected active and reactive power to the grid will be controlled to track their reference value. The PCC model predicts the future grid current by using a discrete-time model of the system for all possible voltage vectors generated by the inverter. The voltage vector that minimizes the current error at the next sampling time will be selected and the corresponding switching state will be the optimal one. The PCC is implemented in Matlab/Simulink and simulation results are presented.

Indoor climate control is responsible for a substantial amount of the world's total energy expenditure. In a time of climate crisis where a reduction of energy consumption is crucial to avoid climate disaster, indoor climate control is a ripe target for eliminating energy waste. The conventional method of adjusting the indoor climate with the use of setpoint curves, based solely on outdoor temperature, may lead to notable inefficiencies. This project evaluates the possibility to replace this method of regulation with a system based on model predictive control (MPC) in one of Uppsala University Hospitals office buildings. A prototype of an MPC controller using Artificial Neural Networks (ANN) as its system model was developed. The system takes several data sources into account, including indoor and outdoor temperatures, radiator flowline and return temperatures, current solar radiation as well as forecast for both solar radiation and outdoor temperature. The system was not set in production but the controller's predicted values correspond well to the buildings current thermal behaviour and weather data. These theoretical results attest to the viability of using the method to regulate the indoor climate in buildings in place of setpoint curves. / Bibehållande av inomhusklimat står för en avsevärd del av världens totala energikonsumtion. Med dagens klimatförändringar där minskad energikonsumtion är viktigt för den hållbara utvecklingen så är inomhusklimat ett lämpligt mål för att förhindra slösad energi. Konventionell styrning av inomhusklimat använder sig av börvärdeskurvor, baserade enbart på utomhustemperatur, vilket kan leda till anmärkningsvärt energispill. Detta projekt utvärderar möjligheten att ersätta denna styrmetod med ett system baserat på model predictive control (MPC) och använda detta i en av Akademiska sjukhusets lokaler i Uppsala. En MPC styrenhet som använder Artificiella Neurala Nätverk (ANN) som sin modell utvecklades. Systemet använder sig av flera datakällor däribland inomhus- och utomhustemperatur, radiatorslingornas framlednings- och returtemperatur, rådande solinstrålning såväl som prognoser för solinstrålning och utomhustemperatur. Systemet sattes inte i produktion men dess prognos stämmer väl överens med tillgänglig väderdata och husets termiska beteende. De presenterade resultaten påvisar metoden vara ett lämpligt substitut för styrning med börvärdeskurvor.

This thesis deals with control of linear bridge cranes using three selected methods. In theoretical part, it gives information about basic structure of each selected algorithm and basic mathematical relations. In the middle, control of algorithms is simulated using created simulation programs in MATLAB. After that, the algorithms are applied on laboratory model of linear crane and in the end all of them are evaluated according to achieved results.