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.

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.

This thesis explores the possibility of using an optimal control scheme called Model Predictive Control (MPC), to control climatization systems for electric vehicles. Some components of electric vehicles, for example the batteries and power electronics, are sensitive to temperature and for this reason it is important that their temperature is well regulated. Furthermore, like all vehicles, the cab also needs to be heated and cooled. One of the weaknesses of electric vehicles is their range, for this reason it is important that the temperature control is energy efficient. Once the range of electric vehicles is increased the down sides compared to traditional combustion engine vehicles decrease, which could lead to an increase in the usage of electric vehicles. This could in turn lead to a decrease of greenhouse gas emission in the transportation sector. With the help of MPC it is possible for the controller to take more factors into consideration when controlling the system than just temperature and in this thesis the power consumption and noise are also taken into consideration. A simple model where parts of the climate system’s circuits were seen as point masses was developed, with nonlinear heat transfers occurring between them, which in turn were controlled by actuators such as fans, pumps and valves. The model was created using Simulink and MATLAB, and the MPC toolbox was used to develop nonlinear MPC controllers to control the climate system. A standard nonlinear MPC, a nonlinear MPC with custom cost functions and a PI controller where all developed and compared in simulations of a cooling scenario. The controllers were designed to control the temperatures of the battery, power electronics and the cab of an electric vehicle. The results of the thesis indicate that MPC could reduce power consumption for the climate control system, it was however not possible to draw any final conclusions as the PI controller that the MPC controllers were compared to was not well optimized for the system. The MPC controllers could benefit from further work, most importantly by applying a more sophisticated tuning method to the controller weights. What was certain was that it is possible to apply this type of centralized controller to very complex systems and achieve robustness without external logic. Even with the controller keeping track of six different temperatures and controlling 15 actuators, the control loop runs much faster than real time on a modern computer which shows promise with regard to implementing it on an embedded system.

The propulsion of the spark-ignition engine has been investigated and developed during the past century to improve driveability, minimize fuel consumption and emissions, resulting in highly engineered and computerized powertrains. Well balanced engine maps containing coordinated set-points and model-based information sharing have solved the cross-coupling between different control loops. During transitions between the operating conditions a disadvantageous transient behavior that affects the engine performance may occur. By implementing an MPC as a superior controller a nearly optimal control solution was accomplished. A digital twin of the SI engine was designed through collected measurements and system modeling. The twin made it possible to investigate and elaborate different cost functions in a simulation environment before applying the controller in real-time. By utilizing MPC together with the engine maps a strong relationship between the throttle and iVVT actuator was achieved, which removed the cross-coupling between the actuator control loops and reduced the unfavorable transient behavior.