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.

Orientadores: Flávio Vasconcelos da Silva, Luís Cláudio Oliveira Lopes / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-24T13:32:43Z (GMT). No. of bitstreams: 1 Costa_ThiagoVazda_D.pdf: 18980163 bytes, checksum: 01508b929c51cbd2ad88d0be37d75084 (MD5) Previous issue date: 2014 / Resumo: A consideração sistemática relacionada ao tratamento de falhas na estrutura de controle é essencial para se atingir as metas de um processo ao mesmo tempo seguro e produtivo. Nesse sentido, este trabalho propõe o estudo e a implementação de técnicas baseadas no controle reconfigurável com aplicações em processos químicos. Essa técnica está relacionada à etapa que lida com a falha no sistema em malha fechada, por meio de sua reconfiguração, evitando a evolução dos seus efeitos na estrutura de controle. Nesse estudo, a atenção é voltada ao rastreamento do comportamento nominal do sistema considerando as técnicas baseadas nos atuadores virtuais. O método considera o desvio da planta em relação ao seu comportamento nominal na presença de falhas em seus atuadores. Deste modo, se o distanciamento em relação ao comportamento nominal pode ser estabilizado por meio das redundâncias físicas e analíticas presentes no processo, o comportamento da planta sujeita à falha também pode ser estabilizado. Neste sentido, uma consideração sobre esse desvio no futuro é proposta, permitindo que técnicas baseadas no controle ótimo e preditivo sejam utilizadas. São consideradas especificamente falhas que comprometam o desempenho do processo quando existe a perda total ou parcial de suas variáveis de entrada. Com os componentes comprometidos devidamente localizados, a redistribuição dos sinais de controle da planta é encaminhada para que a estabilidade e desempenho do processo sejam assegurados. A concepção do atuador virtual com horizonte móvel possibilita o uso de técnicas de otimização, permitindo que as informações referentes às características físicas das redundâncias do processo sejam incluídas, evitando a saturação dos componentes utilizados durante o tratamento da falha. A validação da técnica proposta é avaliada experimentalmente em um processo de neutralização, no qual ferramentas de injeção de falhas são capazes de simular os efeitos de falha dos atuadores no processo real. As estratégias estudadas foram implementadas em um software em código aberto e foram testadas experimentalmente a partir do uso de comunicação OPC (Object Linking and Embedding for Process Control). Cenários com diferentes falhas afetando os atuadores do processo foram concebidos e avaliados. Em todos os estudos, observou-se a capacidade da ferramenta em efetuar a reconfiguração do sistema em malha fechada para mitigar os efeitos indesejáveis da falha. Com base nos resultados, conclui-se que a utilização das técnicas em cenários industriais é viável e pode garantir a operação estável do processo mesmo quando surjam falhas em seus componentes, consideradas as condições de controlabilidade e estabilizabilidade do sistema / Abstract: A systematic approach to fault handling in control loops is important to achieve safe and productive process goals. Therefore, the present work proposes the study and implementation of reconfigurable control methods with applications in chemical processes. This technique is related to the fault handling stage in the closed loop system by means of reconfiguration, preventing the progression of fault effects in the control structure. In this study, particular focus is given to virtual actuator based techniques for tracking nominal system behavior. The method considers the deviation of the plant in relation to its nominal behavior when there are faults in its actuators. Therefore, if the nonconformity from the nominal response can be stabilized using the process physical and analytical redundancies, the faulty plant behavior can also be stabilized. Hence, it is proposed the use of the deviation from the nominal system response in the future, enabling the use of optimal and predictive control techniques. Faults affecting process performance due to total or partial loss of input variables are particularly considered. Once the faulty components are located, input signal redistribution is determined in order to ensure process performance and stability. The moving horizon virtual actuator proposal provides the use of optimization techniques allowing information about physical characteristics of the process redundancies to be included, avoiding component saturation during the fault handling approach. The validation of the technique is performed experimentally in a neutralization process, in which fault injection tools are able to simulate the effects of actuators faults in the real process. The studied strategies were implemented using open-source software and tested experimentally by means of an OPC (Object Linking and Embedding for Process Control) communication. Several fault scenarios considering actuator faults were planned and fulfilled for evaluation. The experimental tests showed that the proposed framework is capable of performing closed loop system reconfiguration to mitigate undesirable fault effects. Based on the results, it is concluded that using such a technique in industrial scenarios is feasible and can guarantee stable process operation even when there are faults in its components, considering system controllability and stabilizability conditions / Doutorado / Sistemas de Processos Quimicos e Informatica / Doutor em Engenharia Química

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.

We consider the solution of nonlinear optimal control problems subject to stochastic perturbations with incomplete observations. In particular, we generalize results obtained by Ito and Kunisch in [8] where they consider a receding horizon control (RHC) technique based on linearizing the problem on small intervals. The linear-quadratic optimal control problem for the resulting time-invariant (LTI) problem is then solved using the linear quadratic Gaussian (LQG) design. Here, we allow linearization about an instationary reference trajectory and thus obtain a linear time-varying (LTV) problem on each time horizon. Additionally, we apply a model predictive control (MPC) scheme which can be seen as a generalization of RHC and we allow covariance matrices of the noise processes not equal to the identity. We illustrate the MPC/LQG approach for a three dimensional reaction-diffusion system. In particular, we discuss the benefits of time-varying linearizations over time-invariant ones.

Missile guidance and autopilot have been active fields of research since the second world war. There are lots of literature on the subjects, but the bulk of which are confined to overly simplified models, and therefore the publications of the methods applied to more realistic models are scarce. In this report a nonlinear 6 DOF simulation model of a tail-controlled air-to-air missile is considered. Through several assumptions and simplifications a linearized approximation of the plant is obtained, which then is used in the implementation of 5 guidance laws and 2 autopilots. The guidance laws are all based on a linearized collision geometry, and the autopilots are based on model predictive control (MPC). Both autopilots use linear quadratic MPC (LQMPC), and one is more robust to modelling errors than the conventional LQMPC. The guidance laws and autopilots are then evaluated with respect to performance in terms of miss distance in 4 interception scenarios with a moving target. The results show that the in this model the autopilots perform equally well, and that the guidance laws with more information about the target generally exhibit smaller miss distances, but at the cost of a considerably larger flight time for some scenarios. The conclusions are that the simplifying assumptions in the modelling are legitimate and that the challenges of missile control probably does not lie in the guidance or autopilot, but rather in the target tracking. Therefore it is suggested that future work include measurement noise and process disturbances in the model. / Det har forskats kring styrlagarna och styrautomaterna för robotar sedan an-dra världskrigets. Det finns mycket litteratur på områdena, men merparten av de publicerade resultaten behandlar enbart grovt förenklade modeller, och därför är tillgången på publikationer där metoderna applicerats i en mer realistisk modell begränsat. I denna rapport behandlas en olinjär simuleringsmodell av en jaktrobot som styrs med stjärtfenor och har sex frihetsgrader. Genom en rad antaganden och förenklingar erhålls en linjäriserad modell av missilen, vilket sedan används för implementering av fem styrlagar och två styrautomater. Styr-lagarna är alla baserade på en linjäriserad kollisionsgeometri och styrautomaterna är baserade på modellprediktiv styrning (MPC). Båda styrautomaterna använder linjärkvadratisk MPC, där den ena påstås vara mer robust gentemot modellfel. Styrlagarna och -automaterna utvärderas ur ett prestandaperspektiv med fokus på bomavstånd i fyra realistiska genskjutningsscenarier med ett rörligt mål. Resultaten visar att båda styrautomaterna presterar lika bra, och att de styrlagar med mer information om målets position/hastighet/acceleration generellt presterar bättre, men att de för vissa skjutfall får en väsentligt längre flygtid. Slutsatserna är att förenklingarna och antagandena i linjäriseringen är välgrundade, och att utmaningarna i missilstyrning inte ligger i utformning av styrlag/-automat, utan förmodligen i målsökningen. Därför föreslås det slutligen att framtida arbete bl. a. inkluderar mätbrus och störningar.

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In this paper, a novel approach for tracking moving structures in multiphase flows over larger axial ranges is presented, which at the same time allows imaging the tracked structures and their environment. For this purpose, ultrafast electron beam X-ray computed tomography (UFXCT) is being extended by an image-based position control. Application is scanning and tracking of, for example, bubbles, particles, waves and other features of multiphase flows within vessels and pipes. Therefore, the scanner has to be automatically traversed with the moving structure basing on real-time scanning, image reconstruction and image data processing. In this paper, requirements and different strategies for reliable object tracking in dual image plane imaging mode are discussed. Promising tracking strategies have been numerically implemented and evaluated.