The department REI/EP at DaimlerChrysler Research and Technology and the Laboratory for Efficient Energy Systems at Trier University of Applied Science, are developing control functions and fuel optimal strategies for low speed conditions. The goal of this thesis project was to further develop the fuel optimal operating strategies, and implement them into a test vehicle equipped with a dSPACE environment. This was accomplished by making optimal reference signals using dynamic programming. Optimal, in this case, means signals that results in low fuel consumption, comfortable driving, and a proper distance to the preceding vehicle. These reference signals for the velocity and distance are used by an MPC controller (Model Predictive Control) to control the car. In every situation a suitable reference path is chosen, depending on the velocities of both vehicles, and the distance. The controller was able to follow another vehicle in a proper way. The distance was kept, the driving was pleasant, and it also seems like it is possible to save fuel. When accepting some deviations in distance to the preceding car, a fuel reduction of 8 % compared to the car in front can be achieved.

Multimedia applications over the Internet are getting more and more popular. While non-real-time streaming services, such as YouTube and Megavideo, are attracting millions of visiting per day, real-time conferencing applications, of which some instances are Skype and Yahoo Voice Chat, provide an interesting experience of communication. Together, they make the fancy Internet world become more and more amusing. Undoubtedly, multimedia flows will eventually dominate the computer network in the future. As the population of multimedia flows increases gradually on the Internet, quality of their service (QoS) is more of a concern. At the moment, the Internet does not have any guarantee on the quality of multimedia services. To completely surpass this limitation, modifications to the network structure is a must. However, it will take years and billions of dollars in investment to achieve this goal. Meanwhile, it is essential to find alternative ways to improve the quality of multimedia services over the Internet. In the past few years, many endeavors have been carried on to solve the problem. One interesting approach focuses on the development of end-to-end congestion control strategies for UDP multimedia flows. Traditionally, packet losses and delays have been commonly used to develop many known control schemes. Each of them only characterizes some different aspects of network congestion; hence, they are not ideal as feedback signals alone. In this research, the flow accumulation is the signal used in feedback for flow control. It has the advantage of reflecting both packet losses and delays; therefore, it is a better choice. Using network simulations, the accumulations of real-time audio applications are collected to construct adaptive flow controllers. The reason for choosing these applications is that they introduce more control challenges than non-real-time services. One promising flow control strategy was proposed by Bhattacharya and it was based on Model Predictive Control (MPC). The controller was constructed from an ARX predictor. It was demonstrated that this control scheme delivers a good QoS while reducing bandwidth use in the controlled flows by 31 percent to 44 percent. However, the controller sometime shows erratic response and bandwidth usage jumps frequently between lowest and highest values. This is not desirable. For an ideal controller, the controlled bandwidth should vary near its mean. To eliminate the deficiency in the strategy proposed by Bhattacharya, it is proposed to introduce a feed forward term into the MPC formulation, in addition to the feedback terms. Simulations show that the modified MPC strategy maintains the benefits of the Bhattacharya strategy. Furthermore, it increases the probability of bandwidth savings from 58 percent for the case of Bhattacharya model to about 99 percent for this work.

One of the key challenges in the Oil & Gas industry is to best manage reservoirs under different conditions, constrained by production rates based on various economic scenarios, in order to meet energy demands and maximize profit. To address the energy demand challenges, a transformation in the paradigm of the utilization of "real-time" data has to be brought to bear, as one changes from a static decision making to a dynamical and data-driven management of production in conjunction with real-time risk assessment. The use of modern methods of computational modeling and simulation may be the only means to account for the two major tasks involved in this paradigm shift: (1) large-scale computations; and (2) efficient utilization of the deluge of data streams. Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Their applicability to a realistic field is also an open topic because of the large-scale nature of existing problems that generate complex models for controller design and real-time implementation, hindering its applicability. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework .The research will focus on System Identification (System-ID) (Jansen, 2009) and Model Predictive Control (MPC) (Gildin, 2008) to serve this purpose. A mathematical treatment of System-ID and MPC as applied to reservoir simulation will be presented. Linear MPC would be studied on two specific reservoir models after generating low-order reservoir models using System-ID methods. All the comparisons are provided from a set of realistic simulations using the commercial reservoir simulator called Eclipse. With the improvements in oil recovery and reductions in water production effectively for both the cases that were considered, we could reinforce our stance in proposing the implementation of MPC and System-ID towards the ultimate goal of "real-time" production optimization.

Poly(ethylene terephthalate), PET, which is commonly used as a packaging material, is not degradable in nature. As an issue of sustainable development it must be recycled and converted into other products. During this process, extrusion is an important unit operation. In extrusion process, if the operating conditions are not controlled, PET can go under degradation, which results in the loss of some mechanical properties. In order to overcome the degradation of recycled PET (RPET), this study aims the control of the extrusion process. Dynamic models of the system for control purposes are obtained by experimental studies. In the experimental studies, screw speed, feed rate and barrel temperatures are taken as process variables in the ranges of 50 &ndash / 500 rpm, 3.85 &ndash / 8.16 g/min and 270 &ndash / 310 oC respectively. Singular value decomposition (SVD) technique is used for the best pairing between the manipulated &ndash / controlled variables, where screw speed is taken as the manipulated variable and molecular weight of the product is taken as the controlled variable. PID and model predictive controller (MPC) are designed utilizing the dynamic models in the feedback inferential control algorithm. In the simulation studies, the performance of the designed inferential control system, where molecular weight (Mv) of the product is estimated from the measured intrinsic viscosity ([&amp / #951 / ]) of the product, is investigated. The controller utilizing PID and MPC control algorithms are found to be robust and satisfactory in tracking the given set points and eliminating the effects of the disturbances.

<p>Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt.</p><p>Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet.</p><p>För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga.</p><p>De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.</p>

<p>Modern air battles are very dynamic and fast, and put extreme pressure on pilots. In some unpredictable situations, like new discovered threats or mission plan deviation because of enemy aircraft, the pilots might need to replan their predefined flight route. This is very difficult, if not impossible, to do since numerous factors affect it. A system that can help the pilots to do such a thing is needed. P</p><p>revious work in this field has involved methods from artificial intelligence like A*-search. In this master thesis, implementation of a replanning system based on a control theory method, Model Predictive Control (MPC), is examined. Different factors influencing the path, such as terrain and threats, are included in the algorithm. </p><p>The results presented in this thesis show that MPC solves the problem. As with every method there are some drawbacks and advantages, but as a summary the method is a very promising one and is worth further development. </p><p>Proposals of future work and different improvements of the algorithms used here are presented in this report as well.</p>

<p>There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. </p><p>This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.</p>

<p>The purpose of this thesis is to investigate an alternative control strategy for a multi-variate non-linear process in a paper machine called the headbox. The proposed solution was intended to be able to be adopted on two different headbox types, currently controlled by different concepts. </p><p>The methodology was to first create black-box models of the two different systems based on measurements, at one working point. Secondly, various control strategies were investigated. A more sophisticated multi-input multi-output controller MPC, or model predictive control, and a less sophisticated one, a single-input single-output, decentralised PI-controller. With help of simulations the performances of the both strategies were tested. Finally, only the decentralised control solution was implemented and evaluated through trial runs on a pilot machine. </p><p>The main issue regarding the decentralised controller was the input-ouput pairing. Since the multi-variate system had four outputs and only three inputs, analysis had to be made in order to select three of those four, to form a square system. This analysis was based on the relative gain array (RGA). </p><p>The resulting performance of the decentralised controller showed stability and adequate response times, surpassing the older system and making one component obsolete through the pairing changes. The MPC controller showed even better performance during simulations and shall also be taken into account if further investigatin is possible.</p>

<p>This master’s thesis describes the development of a model of the catalystsystem aiming at control by an MPC. A well functioning model, which is suitable in control purpose, is important while emission legislation become more and more hard to fulfill for the car manufacturers. Much research has been done in the field of physical modeling of the system, but in this work a linear adaptive time discrete ARX-model is developed and validated.</p><p>The systems tendency to change its dynamic during usage implies that the model must be adaptive. The developed model proved to be well functioning and shows promising conditions for the MPC design. The system and the model are analyzed in the time- and frequency domains and the model is both implemented and validated in a Saab 9-5.</p><p>The work has been performed both at Saab Automobile Powertrain AB in Södertälje and in Vehicular Systems Dept. of Electrical Engineering at Linköpings University.</p>

The topic of this thesis is the theoretical and numerical research of optimal control problems for uncertain nonlinear systems, described by semilinear parabolic differential equations with additive noise, where the state is not completely available. Based on a paper by Kazufumi Ito and Karl Kunisch, which was published in 2006 with the title "Receding Horizon Control with Incomplete Observations", we analyze a Model Predictive Control (MPC) approach where the resulting linear problems on small intervals are solved with a Linear Quadratic Gaussian (LQG) design. Further we define a performance index for the MPC/LQG approach, find estimates for it and present bounds for the solutions of the underlying Riccati equations. Another large part of the thesis is devoted to extensive numerical studies for an 1+1- and 3+1-dimensional problem to show the robustness of the MPC/LQG strategy. The last part is a generalization of the MPC/LQG approach to infinite-dimensional problems.