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Direct drive wave energy conversion systems have been identified as a potentially major contributor to the world’s energy demands, forecasting shares of up to 25 % of the energy mix. Anders Hagnestål conducts research at the Royal Institute of Technology where a novel linear transverse flux permanent magnet generator is developed. This concept machine is particularly well-suited for the pertaining operating conditions in marine environments, producing large forces at low speeds with outstandingly low resistive losses. However, it exhibits severe magnetic saturation and draws unsymmetrical phase currents at nominal operation. In addition, it possesses a low power factor. All in all, this places stern requirements on the power electronic system and control algorithms. The aim of this thesis has been to design a functioning power conditioning system that connects the machine to the electric grid. For this purpose, a three-phase two-level voltage source converter is proposed to be back-to-back connected with two-level single-phase voltage source converters (active rectifiers) interfacing each and every machine phase. It is shown that the intermediate DC link can be maintained at a constant voltage with restricted ripple while feeding power at unity power factor to the grid by appropriately sizing the DC capacitor and adopting a feedback linearization control scheme. The phase currents can be controlled effectively by means of a cascaded gain-scheduled PID controller. By including a low-pass filter the iron losses in the machine may be suppressed even at lower switching frequencies. A constrained cost optimization indicates that the converter consequently can reach 99.1 % efficiency. Finally, with this thesis as a background, it is suggested that the thermal stresses on the selected semiconductor modules and the iron losses of the machine are evaluated to further improve the design. If higher efficiency of the active rectifiers is strived for, more complex converter topologies could be considered. / Direktdrivna vågenergiomvandlingssystem har utpekats som en potentiellt starkt bidragande resurs för att tillgodose världens efterfrågan på energi med andelar på uppemot 25 % av energimixen förutspådda. Anders Hagnestål bedriver forskning och utveckling av en ny typ av linjär permanentmagnetiserad transversalflödesmaskin vid Kungliga Tekniska Högskolan. Konceptmaskinen är särskilt väl lämpad för de rådande marina förhållandena genom att kunna producera stora krafter vid låga hastigheter med utomordentligt låga resistiva förluster. Maskinen går emellertid i kraftig magnetisk mättnad och drar asymmetriska strömmar vid nominell drift. Dessutom är effektfaktorn låg i jämförelse med standardmaskiner. Alltsomallt inför detta hårda krav på det effektelektroniska systemet och kontrollalgoritmerna. Målet med detta examensarbete har varit att designa ett funktionellt effektkonditioneringssystem som sammanfogar maskinen med det angränsande elektriska nätet. För att åstadkomma detta föreslås att en tvånivås-trefasomriktare kopplas rygg-mot-rygg till tvånivås-enfasomvandlare (aktiva likriktare) som i sin tur är kopplade till varje maskinfas. Med den här konfigurationen visas det att spänningen på den mellanliggande DC-länken kan hållas konstant med begränsat rippel, alltmedan effekt tillförs nätet vid effektfaktor ett genom att dimensionera DC-kondensatorn på rätt sätt och använda en kontrollag baserad på exakt linjärisering. Maskinens fasströmmar kan kontrolleras effektivt med hjälp av en kaskadkopplad PID-regulator med schemalagda förstärkningsfaktorer. Genom att inkludera ett lågpassfilter förväntas det att järnförlusterna i maskinen kan begränsas även vid lägre switchfrekvenser. Genom att lösa ett kostnadsoptimeringsproblem visas det att den resulterande aktiva likriktaren kan uppnå en verkningsgrad på 99.1 %. Slutligen, med det här examensarbetet som grund, föreslås det att den termiska stressen på de valda halvledarkomponentsmodulerna och järnförlusterna i maskinen utvärderas för att ytterligare förbättra designen. Om högre verkningsgrad eftersträvas hos de aktiva likriktarna kan mer komplicerade omvandlartopologier övervägas.

Formation flying of satellites describes a mission in which a set of satellites arrange their position with respect to one another. In this paper, satellite formation flying guidance and control algorithms are investigated in terms of required velocity increment Delta-v, and tracking error for a Chief/Deputy satellite system. Different control methods covering continuous and impulsive laws are implemented and tested for Low Earth Orbit (LEO). Sliding Mode, Feedback Linearization and Model Predictive Controllers are compared to an Impulsive Feedback Law which tracks the mean orbital element differences. Sliding Mode and Feedback Linearization controllers use the same dynamic model which includes Earth Oblateness perturbations. On the other hand, Model Predictive Control with Multi-Objective Cost Function is based on the Clohessy–Wiltshire equations, which do not account for any perturbation and do not cover the eccentricity of the orbit. The comparison was done for two different missions both including Earth Oblateness effects only. A relative orbit mission, which was based on the Prisma Satellite Mission and a rendezvous mission, was implemented. The reference trajectory for the controllers was generated with Yamanaka and Ankersen’s state transition matrix, while a separate method was used for the Impulsive Law. In both of the missions, it was observed that the implemented Impulsive Law outperformed in terms of Delta-v, 1.2 to 3.5 times smaller than the continuous control approaches, while the continuous controllers had a smaller tracking error, 2 to 8.3 times less, both in terms of root mean square error and maximum error in the steady state. Finally, this study shows that the tracking error and Delta-v has inversely proportional relationship. / Formationsflygning av satelliter innebär att en grupp satelliter flyger tillsammans och anpassar sina relativa lägen i förhållande till varandra. I detta examensarbete studerades regleralgoritmer för formationsflygande satelliter med fokus på bränsleförbrukning och positionsavvikelse genom ”Chief & Deputy”-metoden. Olika reglermetoder har studerats, t.ex. Sliding Mode- och Feedback Linearization-reglering för formationsflygningsfall i låg jordbana med J2-störning samt en Model Predictive-reglering för fall med relativ rörelse baserad på Clohessy-Wiltshire-ekvationerna. Vidare studerades en reglermetod baserad på impulsframdrivning. De fyra reglermetoderna implementerades på två olika rymduppdrag. Först ett uppdrag baserat på Prisma-satelliterna för två satelliter i relativ omloppsbana och sedan ett Rendezvous-uppdrag. Referensbanan för alla reglermetoder, utom för implusmetoden, har tagits fram med hjälp av Yamanakas och Ankersens tillståndsmatris. Resultaten visar att den implementerade impulsmetoden presterar bättre med avseende på bränsleförbrukning, medan de kontinuerliga reglermetoderna producerade mindre relativ positionsavvikelse, både med avseende på kvadratiskt medelvärde och maximalt värde.

Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering, health care, communication systems, and supply chain management. On the flip side, stochastic programs are usually very difficult to solve, which is further compounded by the fact that in many of the aforementioned applications, we also have discrete decisions, thereby rendering these problems even more challenging. In this dissertation, we study the class of two-stage stochastic mixed-integer programs (SMIP), which, as its name suggests, lies at the confluence of two formidable classes of problems. We design a novel algorithm for this class of problems, and also explore specialized approaches for two related real-world applications. Although a number of algorithms have been developed to solve two-stage SMIPs, most of them deal with problems containing purely integer or continuous variables in either or both of the two stages, and frequently require the technology and/or recourse matrices to be deterministic. As a ground-breaking effort, in this work, we address the challenging class of two-stage SMIPs that involve 0-1 mixed-integer variables in both stages. The only earlier work on solving such problems (Carøe and Schultz (1999)) requires the optimization of several non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer variables in both stages. Since the second-stage problems contain binary variables, their value functions are in general nonconvex and discontinuous; hence, the classical Benders' decomposition approach (or the L-shaped method) for solving two-stage stochastic programs, which requires convex subproblem value functions, cannot be directly applied. This motivates us to relax the second-stage problems and accompany this relaxation with a convexification process. To make this process computationally efficient, we propose to construct a certain partial convex hull representation of the two-stage solution space, using the relaxed second-stage constraints and the restrictions confining the first-stage variables to lie within some hyperrectangle. This partial convex hull is sequentially generated using a convexification scheme, such as the Reformulation-Linearization Technique (RLT), which yields valid inequalities that are functions of the first-stage variables and, of noteworthy importance, are reusable in the subsequent subproblems by updating the values of the first-stage variables. Meanwhile, since the first stage contains continuous variables, whenever we tentatively fix these variables at some given feasible values, the resulting constraints may not be facial with respect to the associated bounding constraints that are used to construct the partial convex hull. As a result, the constructed Benders' subproblems define lower bounds for the second-stage value functions, and likewise, the resulting Benders' master problem provides a lower bound for the original stochastic program defined over the same hyperrectangle. Another difficulty resulting from continuous first-stage variables is that when the given first-stage solution is not extremal with respect to its bounds, the second-stage solution obtained for a Benders' subproblem defined with respect to a partial convex hull representation in the two-stage space may not satisfy the model's binary restrictions. We thus need to be able to detect whether or not a Benders' subproblem is solved by a given fractional second-stage solution. We design a novel procedure to check this situation in the overall algorithmic scheme. A key property established, which ensures global convergence, is that these lower bounds become exact if the given first-stage solution is a vertex of the defining hyperrectangle, or if the second-stage solution satisfies the binary restrictions. Based on these algorithmic constructs, we design a branch-and-bound procedure where the branching process performs a hyperrectangular partitioning of the projected space of the first-stage variables, and lower bounds for the nodal problems are computed by applying the proposed modified Benders' decomposition method. We prove that, when using the least-lower-bound node-selection rule, this algorithm converges to a global optimal solution. We also show that the derived RLT cuts are not only reusable in subsequent Benders iterations at the same node, but are also inheritable by the subproblems of the children nodes. Likewise, the Benders' cuts derived for a given sub-hyperrectangle can also be inherited by the lower bounding master programs solved for its children nodes. Using these cut inheritance properties results in significant savings in the overall computational effort. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach. The sizes of the deterministic equivalent of our test problems range from having 386 continuous variables, 386 binary variables, and 386 constraints, up to 1795 continuous variables, 1539 binary variables, and 1028 constraints. The results reveal an average savings in computational effort by a factor of 9.5 in comparison with using a commercial mixed-integer programming package (CPLEX 8.1) on a deterministic equivalent formulation. We then explore an important application of SMIP to enhance the traditional airline fleet assignment models (FAM). Given a flight schedule network, the fleet assignment problem solved by airline companies is concerned with assigning aircraft to flight legs in order to maximize profit with respect to captured path- or itinerary-based demand. Because certain related crew scheduling regulations require early information regarding the type of aircraft serving each flight leg, the current practice adopted by airlines is to solve the fleet assignment problem using estimated demand data 10-12 weeks in advance of departure. Given the level of uncertainty, deterministic models at this early stage are inadequate to obtain a good match of aircraft capacity with passenger demands, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. From this viewpoint, the initial decision should embrace various market scenarios so that it incorporates a sufficient look-ahead feature and provides sufficient flexibility for the subsequent re-fleeting processes to accommodate the inevitable demand fluctuations. With this motivation, we propose a two-stage stochastic programming approach in which the first stage is concerned with the initial fleet assignment decisions and, unlike the traditional deterministic methodology, focuses on making only a family-level assignment to each flight leg. The second stage subsequently performs the detailed assignments of fleet types within the allotted family to each leg under each of the multiple potential scenarios that address corresponding path- or itinerary-based demands. In this fashion, the initial decision of what aircraft family should serve each flight leg accomplishes the purpose of facilitating the necessary crew scheduling decisions, while judiciously examining the outcome of future re-fleeting actions based on different possible demand scenarios. Hence, when the actual re-fleeting process is enacted several weeks later, this anticipatory initial family-level assignment will hopefully provide an improved overall fleet type re-allocation that better matches demand. This two-stage stochastic model is complemented with a secondary model that performs adjustments within each family, if necessary, to provide a consistent fleet type-assignment information for accompanying decision processes, such as yield management. We also propose several enhanced fleet assignment models, including a robust optimization model that controls decision variation among scenarios and a stochastic programming model that considers the recapture effect of spilled demand. In addition to the above modeling concepts and framework, we also contribute in developing effective solution approaches for the proposed model, which is a large-scale two-stage stochastic 0-1 mixed-integer program. Because the most pertinent information needed from the initial fleet assignment is at the family level, and the type-level assignment is subject to change at the re-fleeting stage according to future demand realizations, our solution approach focuses on assigning aircraft families to the different legs in the flight network at the first stage, while finding relaxed second-stage solutions under different demand scenarios. Based on a polyhedral study of a subsystem extracted from the original model, we derive certain higher-dimensional convex hull as well as partial convex hull representations for this subsystem. Accordingly, we propose two variants for the primary model, both of which relax the binary restrictions on the second-stage variables, but where the second variant then also accommodates the partial convex hull representations, yielding a tighter, albeit larger, relaxation. For each variant, we design a suitable solution approach predicated on Benders' decomposition methodology. Using certain realistic large-scale flight network test problems having 900 flight legs and 1,814 paths, as obtained from United Airlines, the proposed stochastic modeling approach was demonstrated to increase daily expected profits by about 3% (which translates to about $160 million per year) in comparison with the traditional deterministic model in present usage, which considers only the expected demand. Only 1.6% of the second-stage binary variables turn out to be fractional in the first variant, and this number is further reduced to 1.2% by using the tighter variant. Furthermore, when attempting to solve the deterministic equivalent formulation for these two variants using a commercial mixed-integer programming package (CPLEX 8.1), both the corresponding runs were terminated after reaching a 25-hour cpu time limit. At termination, the software was still processing the initial LP relaxation at the root node for each of these runs, and no feasible basis was found. Using the proposed algorithms, on the other hand, the solution times were significantly reduced to 5 and 19 hours for the two variants, respectively. Considering that the fleet assignment models are solved around three months in advance of departure, this solution time is well acceptable at this early planning stage, and the improved quality in the solution produced by considering the stochasticity in the system is indeed highly desirable. Finally, we address another practical workforce planning problem encountered by a global financial firm that seeks to manage multi-category workforce for functional areas located at different service centers, each having office-space and recruitment-capacity constraints. The workforce demand fluctuates over time due to market uncertainty and dynamic project requirements. To hedge against the demand fluctuations and the inherent uncertainty, we propose a two-stage stochastic programming model where the first stage makes personnel recruiting and allocation decisions, while the second stage, based on the given personnel decision and realized workforce demand, decides on the project implementation assignment. The second stage of the proposed model contains binary variables that are used to compute and also limit the number of changes to the original plan. Since these variables are concerned with only one quality aspect of the resulting workforce plan and do not affect feasibility issues, we replace these binary variables with certain conservative policies regarding workforce assignment change restrictions in order to obtain more manageable subproblems that contain purely continuous variables. Numerical experiments reveal that the stochastic programming approach results in significantly fewer alterations to the original workforce plan. When using a commercial linear programming package CPLEX 9.0 to solve the deterministic equivalent form directly, except for a few small-sized problems, this software failed to produce solutions due to memory limitations, while the proposed Benders' decomposition-based solution approach consistently solved all the practical-sized test problems with reasonable effort. To summarize, this dissertation provides a significant advancement in the algorithmic development for solving two-stage stochastic mixed-integer programs having 0-1 mixed-integer variables in both stages, as well as in its application to two important contemporary real-world applications. The framework for the proposed solution approaches is to formulate tighter relaxations via partial convex hull representations and to exploit the resulting structure using suitable decomposition methods. As decision robustness is becoming increasingly relevant from an economic viewpoint, and as computer technological advances provide decision-makers the ability to explore a wide variety of scenarios, we hope that the proposed algorithms will have a notable positive impact on solving stochastic mixed-integer programs. In particular, the proposed stochastic programming airline fleet assignment and the workforce planning approaches studied herein are well-poised to enhance the profitability and robustness of decisions made in the related industries, and we hope that similar improvements are adapted by more industries where decisions need to be made in the light of data that is shrouded by uncertainty. / Ph. D.

Air transportation offers both passenger and freight services that are essential for economic growth and development. In a highly competitive environment, airline companies have to control their operating costs by managing their flights, aircraft, and crews effectively. This motivates the extensive use of analytical techniques to solve complex problems related to airline operations planning, which includes schedule design, fleet assignment, aircraft routing, and crew scheduling. The initial problem addressed by airlines is that of schedule design, whereby a set of flights having specific origin and destination cities as well as departure and arrival times is determined. Then, a fleet assignment problem is solved to assign an aircraft type to each flight so as to maximize anticipated profits. This enables a decomposition of subsequent problems according to the different aircraft types belonging to a common family, for each of which an aircraft routing problem and a crew scheduling or pairing problem are solved. Here, in the aircraft routing problem, a flight sequence or route is built for each individual aircraft so as to cover each flight exactly once at a minimum cost while satisfying maintenance requirements. Finally, in the crew scheduling or pairing optimization problem, a minimum cost set of crew rotations or pairings is constructed such that every flight is assigned a qualified crew and that work rules and collective agreements are satisfied. In practice, most airline companies solve these problems in a sequential manner to plan their operations, although recently, an increasing effort is being made to develop novel approaches for integrating some of the airline operations planning problems while retaining tractability. This dissertation formulates and analyzes three different models, each of which examines a composition of certain pertinent airline operational planning problems. A comprehensive fourth model is also proposed, but is relegated for future research. In the first model, we integrate fleet assignment and schedule design by simultaneously considering optional flight legs to select along with the assignment of aircraft types to all scheduled legs. In addition, we consider itinerary-based demands pertaining to multiple fare-classes. A polyhedral analysis of the proposed mixed-integer programming model is used to derive several classes of valid inequalities for tightening its representation. Solution approaches are developed by applying Benders decomposition method to the resulting lifted model, and computational experiments are conducted using real data obtained from a major U.S. airline (United Airlines) to demonstrate the efficacy of the proposed procedures as well as the benefits of integration. A comparison of the experimental results obtained for the basic integrated model and for its different enhanced representations reveals that the best modeling strategy among those tested is the one that utilizes a variety of five types of valid inequalities for moderately sized problems, and further implements a Benders decomposition approach for relatively larger problems. In addition, when a heuristic sequential fixing step is incorporated within the algorithm for even larger sized problems, the computational results demonstrate a less than 2% deterioration in solution quality, while reducing the effort by about 21%. We also performed an experiment to assess the impact of integration by comparing the proposed integrated model with a sequential implementation in which the schedule design is implemented separately before the fleet assignment stage based on two alternative profit maximizing submodels. The results obtained demonstrate a clear advantage of utilizing the integrated model, yielding an 11.4% and 5.5% increase in profits in comparison with using the latter two sequential models, which translates to an increase in annual profits by about $28.3 million and $13.7 million, respectively. The second proposed model augments the first model with additional features such as flexible flight times (i.e., departure time-windows), schedule balance, and demand recapture considerations. Optional flight legs are incorporated to facilitate the construction of a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Moreover, network effects and realistic demand patterns are effectively represented by examining itinerary-based demands as well as multiple fare-classes. Allowing flexibility on the departure times of scheduled flight legs within the framework of an integrated model increases connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs. A provision is also made for airlines to capture an adequate market share by balancing flight schedules throughout the day. Furthermore, demand recapture considerations are modeled to more realistically represent revenue realizations. For this proposed mixed-integer programming model, which integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, itinerary-based demands, and multiple fare-classes, we perform a polyhedral analysis and utilize the Reformulation-Linearization Technique in concert with suitable separation routines to generate valid inequalities for tightening the model representation. Effective solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented to demonstrate the efficacy of the proposed procedures. Using real data obtained from United Airlines, when flight times were permitted to shift by up to 10 minutes, the estimated increase in profits was about $14.9M/year over the baseline case where only original flight legs were used. Also, the computational results indicated a 1.52% and 0.49% increase in profits, respectively, over the baseline case, while considering two levels of schedule balance restrictions, which can evidently also enhance market shares. In addition, we measured the effect of recaptured demand with respect to the parameter that penalizes switches in itineraries. Using values of the parameter that reflect 1, 50, 100, or 200 dollars per switched passenger, this yielded increases in recaptured demand that induced additional profits of 2.10%, 2.09%, 2.02%, and 1.92%, respectively, over the baseline case. Overall, the results obtained from the two schedule balance variants of the proposed integrated model that accommodate all the features of flight retiming, schedule balance, and demand recapture simultaneously, demonstrated a clear advantage by way of $35.1 and $31.8 million increases in annual profits, respectively, over the baseline case in which none of these additional features is considered. In the third model, we integrate the schedule design, fleet assignment, and aircraft maintenance routing decisions, while considering optional legs, itinerary-based demands, flexible flight retimings, recapture, and multiple fare-classes. Instead of utilizing the traditional time-space network (TSN), we formulate this model based on a flight network (FN) that provides greater flexibility in accommodating integrated operational considerations. In order to consider through-flights (i.e., a sequence of flight legs served by the same aircraft), we append a set of constraints that matches aircraft assignments on certain inbound legs into a station with that on appropriate outbound legs at the same station. Through-flights can generate greater revenue because passengers are willing to pay a premium for not having to change aircraft on connecting flights, thereby reducing the possibility of delays and missed baggage. In order to tighten the model representation and reduce its complexity, we apply the Reformulation-Linearization Technique (RLT) and also generate other classes of valid inequalities. In addition, since the model possesses many equivalent feasible solutions that can be obtained by simply reindexing the aircraft of the same type that depart from the same station, we introduce a set of suitable hierarchical symmetry-breaking constraints to enhance the model solvability by distinguishing among aircraft of the same type. For the resulting large-scale augmented model formulation, we design a Benders decomposition-based solution methodology and present extensive computational results to demonstrate the efficacy of the proposed approach. We explored four different algorithmic variants, among which the best performing procedure (Algorithm A1) adopted two sequential levels of Benders partitioning method. We then applied Algorithm A1 to perform several experiments to study the effects of different modeling features and algorithmic strategies. A summary of the results obtained is as follows. First, the case that accommodated both mandatory and optional through-flight leg pairs in the model based on their relative effects on demands and enhanced revenues achieved the most profitable strategy, with an estimated increase in expected annual profits of $2.4 million over the baseline case. Second, utilizing symmetry-breaking constraints in concert with compatible objective perturbation terms greatly enhanced problem solvability and thus promoted the detection of improved solutions, resulting in a $5.8 million increase in estimated annual profits over the baseline case. Third, in the experiment that considers recapture of spilled demand from primary itineraries to other compatible itineraries, the different penalty parameter values (100, 50, and 1 dollars per re-routed passenger) induced average respective proportions of 3.2%, 3.4%, and 3.7% in recaptured demand, resulting in additional estimated annual profits of $3.7 million, $3.8 million, and $4.0 million over the baseline case. Finally, incorporating the proposed valid inequalities within the model to tighten its representation helped reduce the computational effort by 11% on average, while achieving better solutions that yielded on average an increase in estimated annual profits of $1.4 million. In closing, we propose a fourth more comprehensive model in which the crew scheduling problem is additionally integrated with fleet assignment and aircraft routing. This integration is important for airlines because crew costs are the second largest component of airline operating expenses (after fuel costs), and the assignment and routing of aircraft plus the assignment of crews are two closely interacting components of the planning process. Since crews are qualified to typically serve a single aircraft family that is comprised of aircraft types having a common cockpit configuration and crew rating, the aircraft fleeting and routing decisions significantly impact the ensuing assignment of cockpit crews to flights. Therefore it is worthwhile to investigate new models and solution approaches for the integrated fleeting, aircraft routing, and crew scheduling problem, where all of these important inter-dependent processes are handled simultaneously, and where the model can directly accommodate various work rules such as imposing a specified minimum and maximum number of flying hours for crews on any given pairing, and a minimum number of departures at a given crew base for each fleet group. However, given that the crew scheduling problem itself is highly complex because of the restrictive work rules that must be heeded while constructing viable duties and pairings, the formulated integrated model would require further manipulation and enhancements along with the design of sophisticated algorithms to render it solvable. We therefore recommend this study for future research, and we hope that the modeling, analysis, and algorithmic development and implementation work performed in this dissertation will lend methodological insights into achieving further advances along these lines. / Ph. D.

Stability and sustainability of operation of interconnected power converter systems has been an important focus of study in the field of power electronics and power systems. With ever-increasing application of electrical machines by means of electrification of vehicles, airplanes and shipboards, detailed study of the relating dynamics is very important to ensure the proper implementation and stable behavior of the overall system. In this work, the application of the black box approach study of the power converters has been expanded to the electrical machines. Using this modeling method, it is possible of have accurate behavior of electrical and mechanical terminals of the machine without the detailed information about the internal structure of the machine, material characteristics or topology of the machine. Instead, accurate model of electrical and mechanical terminals of the machine are achieved by measuring specific frequency responses of the machine to distinguish dynamic relation of the various electrical and mechanical quantities of the machine. The directly measured frequency responses, are coupled with the dynamics of the source and load in the electrical and mechanical terminals of the machine thus in order to decoupled the described couplings a mathematical process is used that results in decoupling of the controller and drive on the electrical side and the dynamics of the mechanical load and mechanical shaft at the mechanical terminal of the machine. Resulting model is the linear time invariant representation of the electrical machine at a specific operating point. Additionally, this work represents the application of this modeling method for accurate measurement of internal parameters of the machine such as inductances and mechanical inertia and characterization of the mechanical shaft coupler. Resulting unterminated model of the machine is a very important matter of information for system integrators and electrical and mechanical designs related to the application of the machine, to ensure the stable and sustainable operation of the machine. This work for the first time, represents the experimental implementation of this terminal behavioral modeling method for studying electrical machines as well as describes some of the practical limitations of this methodology. By incorporating and integrating a combination of commercially available devices such as frequency response analyzer, Hardware-In-The-Loop (HIL), Power-Hardware-In-The-Loop (PHIL), a test setup has been developed that is capable of control, operate and study arbitrary frame small-signal related measurements required for terminal behavioral study of the electrical machines. Resulting model of the machine that has been extracted from this modeling method is then used to compare in time domain with the real machine in the case of transient change in the mechanical load on the shaft to discover the validity of this modeling procedure. / Master of Science / According to the data from the International Energy Agency, around half of the electricity used globally is consumed by electric motors. Moreover, the growth in the electrical vehicle industry will increase their application even further, hence the development of high-fidelity models of electric machines for real-time emulation, system-level analyses, and stability studies still stands out as an important and needed research focus. New modeling concepts that go beyond the standard industry practice can be used at the design and integration stage to ensure the stable behavior of the overall system. Furthermore, convenient testing and identification pressures can help ensure the long-term operation of the system. Aligned with this trend, this thesis is studying permanent magnet synchronous machines (PMSM) using small-signal terminal-behavioral three-port networks. Having such a behavioral model of the machine available provides many opportunities for system integrators, and even enables an in-situ system observation and stability assessment at both the machine's electrical and mechanical interfaces. This capability can undoubtedly be of high importance in practice, as it is offering new insights into dynamic interactions of the electro-mechanical systems, the governor or turbine control design in ships, aircrafts, electrical vehicles, and even large synchronous machines in power plants. A so-called characterization testbed has been built that combines Hardware-In-The-Loop (HIL) and Power-Hardware-In-The-Loop (PHIL) environments, with sensor-interface boards that are used to properly scale measured signals for machine control. The Frequency-Response-Analyzer is used to sweep the proper electrical or mechanical terminal of the machine by perturbing the proper control signal within the machine controller running in PHIL and reading d-q currents, voltages, torque, and speed variables whose dynamic ratios are then obtained without the need for interrupting the normal operation of the electrical machine. The capability of acquiring such a detailed model of the machine while the machine is in operation is an important benefit of this modeling method, in comparison to the conventional identification methods widely applied in the industry. The resulting model is a linearized time invariant representation of the electrical machine at a specific operating point of interest, and can be used by system integrators to ensure the stability of the system using well known stability assessment methodologies. Furthermore, this modeling strategy has been experimentally verified for the first time on electrical machines, and the resulting model has been compared with the transient behavior of the machine in the presence of a step change in the mechanical load of the machine.

Engine control systems aim to ensure satisfactory output performance whilst adhering to requirements on emissions, drivability and fuel efficiency. Model predictive control (MPC) has shown promising results when applied to multivariable and nonlinear systems with operational constraints, such as diesel engines. This report studies the torque generation from a mean-value heavy duty diesel engine with exhaust gas recirculation and variable-geometry turbocharger using state feedback linearization based MPC (LMPC). This is accomplished by first introducing a fuel optimal reference generator that converts demands on torque and engine speed to references on states and control signals for the MPC controller to follow. Three different MPC controllers are considered: a single linearization point LMPC controller and two different successive LMPC (SLMPC) controllers, where the controllers are implemented using the optimization tool CasADi. The MPC controllers are evaluated with the World Harmonized Transient Cycle and the results show promising torque tracking using a SLMPC controller with linearization about reference values.

Student Number : 8315331 - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / In this dissertation an efficient algorithm to calculate the differential of the network output with respect to its inputs is derived for axis orthogonal Local Model (LMN) and Radial Basis Function (RBF) Networks. A new recursive Singular Value Decomposition (SVD) adaptation algorithm, which attempts to circumvent many of the problems found in existing recursive adaptation algorithms, is also derived. Code listings and simulations are presented to demonstrate how the algorithms may be used in on-line adaptive neurocontrol systems. Specifically, the control techniques known as series inverse neural control and instantaneous linearization are highlighted. The presented material illustrates how the approach enhances the flexibility of LMN networks making them suitable for use in both direct and indirect adaptive control methods. By incorporating this ability into LMN networks an important characteristic of Multi Layer Perceptron (MLP) networks is obtained whilst retaining the desirable properties of the RBF and LMN approach.

Ce mémoire propose une stratégie de contrôle sans commutation d’algorithme pour un système hybride constituée d’une pile à combustible comme source principale et d’un pack de supercondensateurs comme source auxiliaire. Trois structures de système hybride ont été étudiées. Après avoir évoqué les différentes structures des systèmes hybrides électriques et des techniques utilisées pour les contrôler, deux approches sont traitées. La première est basée sur la notion de platitude permettant d’assurer la gestion des flots d’énergie dans une source hybride et plus généralement dans un système multi sources/multi charges. La stratégie proposée repose sur la génération d’un modèle d’ordre réduit du système et la gestion des flots d’énergie via des trajectoires de référence de certaines grandeurs énergétiques du système. L’impact de ce mode de contrôle sur le dimensionnement des éléments passifs (inductances, condensateurs) de la source hybride a été expliqué. Dans la deuxième stratégie, l’énergie totale stockée dans les hacheurs est prise en compte dans l’élaboration de la commande du système multi sources/multi charges en utilisant une linéarisation entrée/sortie sur les convertisseurs des charges. Un observateur non linéaire a été proposé pour estimer la variation de la caractéristique statique de pile à combustible et permet de garantir un fonctionnement optimal du système hybride. Les architectures de puissance et les modes de commande proposés ont été validés par des résultats simulés et/ou expérimentaux / This work deals with a nonlinear control strategy of an electrical hybrid system which is composed of a fuel cell as the main source and a supercapacitor bank as the auxiliary source. Any algorithm commutation is not used in the proposed control strategy whereas the system works in different operating modes. After a review of various structures of the electrical hybrid systems and different control methods of these systems, two new approaches are developed. The first one is flatness-based method to ensure the energy management in the proposed hybrid systems and generally in a multi source / multi loads system. The proposed strategy is based on generation of a reduced-order model of the system. The energy management is carried out through the reference trajectories of the stored electrostatic energy of the system. The effect of the proposed control method on design of the system components (inductors and capacitors) is explained. In the second approach, the total energy stored in the choppers is taken into account to control the load converters of a multi-source/multi load system by use of the input/output linearization method. A nonlinear observer is proposed to estimate the variation of voltage-power output characteristic of the fuel cell which leads to an optimal performance of the hybrid system. The simulation and experimental results prove validity of the proposed control strategy