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In this thesis, after a comprehensive literature review on different conventional and predictive torque control strategies for switched reluctance motor (SRM) drives, two online methods and one offline multi-objective optimization-based method are proposed to extend the operating speed range of finite control set model predictive torque control (FCS-MPTC) for SRM by adaptively controlling the commutation angles in the entire speed range. Furthermore, a method is proposed to minimize the steady state torque tracking error of FCS-MPTC for SRM drives. The incapability of the conventional FCS-MPTC in controlling the commutation angles, which is considered as one of the main drawbacks of the conventional FCS-MPTC, limits its application for high-speed torque control of SRM drives. The phase turn-off angle is always selected to be close to the aligned position with the conventional FCS-MPTC regardless of the operating speed. However, commutation angle advancement is required for high-speed torque control of SRM drives to limit the negative phase torque resulting from the current tail after the turn-off angle in the generating region. Excessive negative torque with the conventional FCS-MPTC at higher speeds can result in a degraded performance with high rms current, low average torque, high torque ripple, and reduced efficiency. The phase turn-off angle can be adaptively controlled as speed changes with the first online commutation angle control strategy proposed in this thesis. This method is based on predicting the free-wheeling phase current in an extended time interval which is much bigger than the prediction horizon of FCS-MPTC. The second online turn-off angle control method is also proposed by improving the optimality condition defined for determining the optimal turn-off angle. The optimality condition is determined by calculating the work done by the conducting phase after the phase is turned off. The weighting factor of the objective function of FCS-MPTC is kept constant with both proposed online methods. An offline multi-objective optimization-based strategy is proposed to determine the globally optimal turn-off angle and the weighting factor in the entire operating torque and speed ranges. The effectiveness of both proposed online methods and the offline commutation angle control strategy is verified using simulations and experimental results. The results are also compared to the conventional FCS-MPTC and the indirect average torque control with optimized conduction angles which is considered as one of the main conventional torque control strategies for SRM drives. In order to minimize the torque tracking error as a result of either parameter uncertainties or tracking multiple objectives with a single objective function with weighting factors, a method is proposed which is based on updating the reference torque at each sample time by calculating the average torque tracking error in the previous sample times. The validity of the proposed method is verified using simulations. / Thesis / Doctor of Philosophy (PhD)

Sedan industriella revolutionen har hastigheten av global uppvärmning och föroreningar i miljön ökat betydligt. Företag i fordonsindustrin arbetar aktivt för att göra sina produkter mer hållbara genom att bland annat minska utsläppen, minimera användningen av icke-förnybara resurser samt att återvinna. En batteridriven elbil (BEV) är en möjlig lösning för renare transport och marknaden har ökat signifikant. Men med den nuvarande batteriteknologin skulle stora byggmaskiner som grävmaskiner behöva tunga batterier för att möta sina energibehov, vilket ökar den totala vikten. Bränslecellshybriddrivna fordon (FCHEV) med vätgas är en potentiell lösning för medelstora och stora byggmaskiner som kombinerar bränsleceller och batterier för att tillhandahålla energin. Byggmaskiner har en växlande effekt och utför vanligtvis upprepande arbetsmönster, men en bränslecell reagerar långsammare på grund av den kemiska processen. Därför behövs ett effektivt energihanteringssystem för att möta effektbehovet, uppfylla systembegränsningar, minska vätgasförbrukningen samt att begränsa bränslecell- och batteridegraderingen. Syftet med denna avhandling är att utveckla en kontrollenhet och ett estimeringsinstrument för maskinbelastning för ett sådant FCHEV system. En ny energihanteringsstrategi föreslås genom att formulera den som ett optimeringsproblem och använda modellprediktiv reglering (MPC) för att minimera målfunktionen som involverar vätgasförbrukning och hastighetsbegränsningar. Kontrollenheten ger en optimal fördelning av bränslecell- och batterikraft över en tidsperiod som uppfyller det efterfrågade effektbehovet och följer systembegränsningarna. Maskinbelastningsestimeringen är baserad på autokorrelation och integreras med kontrollenheten. Estimeringsinstrumentet fungerar som en ingång till kontrollenheten som optimerar fördelningen av kraften mellan batteriet och bränslecellen. Jämfört med den tidigare realtidsfördelningsfunktionen för effekt som användes av Volvo Construction Equipment AB (Volvo CE) visade det sig att MPC kombinerat med autokorrelationsbaserad belastningsestimering främst använde ett mycket smalare fönster för batteriets laddningstillstånd (SoC), vilket öppnar upp möjligheten att minska batteristorleken i maskinen. Transienter i bränslecellens effekt minskar också, vilket minskar dess nedbrytning och förbättrar livslängden. / Ever since the industrial evolution, the rate of global warming and pollution in the environment have gone up significantly. Automotive companies are actively working towards making their products more sustainable in terms of reducing emissions, minimizing resource utilization of non-renewables, recycling, and several other steps. A pure battery electric vehicle (BEV) is a possible solution for cleaner transport and has seen widespread adoption among users. However, with the current battery technology, large construction machines such as excavators would need heavy batteries to meet their energy demand, pushing up the overall weight. Hydrogen driven Fuel Cell Hybrid Electric Vehicles (FCHEV) are a potential solution for medium and large sized construction machines having both fuel cells and batteries to supply energy. Construction machines have a highly transient power and generally perform repeating patterns of work but a fuel cell is slow reacting device due to the chemistry involved. Hence there is a need for an efficient energy management system to meet the power demand, satisfy system constraints, reduce hydrogen consumption and limit fuel cell and battery degradation. This thesis aims to develop a controller and a machine load predictor for such a FCHEV. A novel energy management strategy is proposed by formulating it as an optimization problem and using Model Predictive Control (MPC) to minimize the objective function that involves hydrogen consumption and rate constraints. The controller yields an optimal fuel cell and battery power split over a time-horizon that fulfills the demanded power and obeys the system constraints. An auto-correlation-based machine load predictor is integrated with the controller. The predictor serves as an input to the controller that optimizes the power split between the battery and fuel cell. Compared to the previous real-time power-split function used by Volvo Construction Equipment AB (Volvo CE), the MPC combined with the auto-correlation-based load predictor was found to primarily use a much narrower battery State of Charge (SoC) window, thus opening up the potential to reduce battery size in the machine. Transients in the fuel cell power are also reduced, thus slowing down its degradation and improving the lifetime.

abstract: Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed. / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2021

In thiswork, we develop a learning model predictive controller (LMPC) for energy-optimaltracking of periodic trajectories for a quadcopter. The main advantage of this controller isthat it is “reference-free”. Moreover, the controller is able to improve its performance overiterations by incorporating learning from the previous iterations. The proposed learningmodel predictive controller aims to learn the “best” energy-optimal trajectory over timeby learning a terminal constraint set and a terminal cost from the history data of previousiterations. We have shown howto recursively construct terminal constraint set and terminalcost as a convex hull and a convex piece-wise linear approximation of state and inputtrajectories of previous iterations, respectively. These steps allow us to formulate theonline planning problem for the controller as a convex optimization program, therebyavoiding the complex combinatorial optimization problems that alternative formulationsin the literature need to solve. The data-driven terminal constraint set and terminal costnot only ensure recursive feasibility and stability of LMPC but also guarantee convergenceto the neighbourhood of the optimal performance at steady state. Our LMPC formulationincludes linear time-varying system dynamics which is also learnt from stored state andinput trajectories of previous iterations.To show the performance of LMPC, a quadcopter trajectory learning problem in thevertical plane is simulated in MATLAB/SIMULINK. This particular trajectory learningproblem involves non-convex state constraints, which makes the resulting optimal controlproblem difficult to solve. A tangent cut method is implemented to approximate the nonconvexconstraints by convex ones, which allows the optimal control problem to be solvedby efficient convex optimization solvers. Simulation results illustrate the effectiveness ofthe proposed control strategy. / Vi utvecklar en lärande modell-prediktiv regulator för energi-optimalt följande av periodiskatrajektorier för en quadkopter. Den huvudsakliga fördelen med denna regulator äratt den är “referensfri”. Dessutom så klarar regulatorn att förbättra sin prestanda medtiden genom att inkorporera inlärning från föregående iterationer. Syftet med den föreslagnalärande modell-prediktiva regulatorn är att över en viss tid lära sig den “bästa”energioptimala trajektorian genom att lära sig den terminala bivillkorsmängden och denterminala kostnaden från historiskt data från tidigare iterationer. Vi har visat hur man kanrekursivt konstruera terminala bivillkorsmängder och terminala kostnader som konvexahöljen respektive konvexa styckvis linjära approximationer av tillstånds- och insignalstrajektoriernafrån tidigare iterationer. Dessa steg gör det möjligt att formulera onlineplaneringsproblemet för regulatorn som ett konvext optimeringsproblem och på så visundvika de komplexa kombinatoriska optimeringsproblemen som ofta krävs för alternativametoder som kan hittas andra publikationer. Den datadrivna terminala bivillkorsmängdenoch terminala kostnaden garanterar inte bara rekursiv tillåtenhet och stabilitet av LMPC,utan även konvergens till en omgivning av den optimala prestandan efter att ha uppnåttjämvikt. Vår LMPC-formulering innehåller linjär och tidsvarierande systemdynamik, somockså lärs från lagrade tillstånds- och insignalstrajektorier från tidigare iterationer.För att visa prestandan av LMPC så simuleras iMATLAB/SIMULINK ett problem ominlärning av quadkopter-trajektorier i det vertikala planet. Just det trajektorieinlärningsproblemetinnehåller icke-konvexa tillståndsbivillkor, vilket gör det resulterande optimeringsproblemetsvårt att lösa. En tangentsnitt-metod är implementerad för att approximera deicke-konvexa bivillkoren med hjälp av konvexa bivillkor, vilket möjliggör lösningen avdet optimala regleringsproblemet med effektiva lösare för konvexa optimeringsproblem.Simuleringsresultaten visar effektivitet av den föreslagna regleringsmetoden.

Space activities have been increasing dramatically in the past decades. As a result, the number of space debris has also increased significantly. Therefore, it is necessary to clean up and remove them to prevent a collision between space debris and spacecraft. In this thesis, we focus on load transportation using tethers, which connect multiple robots and loads together with lightweight cables. We propose a generalized framework to model and calculate the interaction force for the tethered multi-robot system. Then, we develop centralized and decentralized non-linear Model Predictive Control (MPC) controllers to complete a transportation task. Two simulators, a numerical and physical simulator, are presented and used to evaluate the performance of the controllers. The numerical simulator is used to verify the proposed model and evaluate the controllers for the ideal case. The physical simulator is then used to validate the performance of both centralized and decentralized controllers in real-time settings. Finally, we demonstrate how the proposed controllers perform in two and three-dimensional experiments. / Rymdaktiviteter har ökat dramatiskt under de senaste årtiondena. Som en följd av detta har mängden rymdskräp också ökat avsevärt. Därför är det nödvändigt att rensa upp och avlägsna detta skräp för att förhindra kollisioner mellan rymdskräp och rymdfarkoster. I denna rapport fokuserar vi på transporter av rymdobjekt som är sammanbundna via en lätt kabel. Vi föreslår en allmän metod för att modellera och beräkna interaktionskraften för det förenade multirobotsystemet. Sedan utvecklar vi centraliserad och decentraliserad icke-linjär modell-prediktiv reglering, MPC (eng. Model Predictive Control), för att uppnå transportuppgiften. Två simulatorer, en numerisk och en fysisk simulator, presenteras och används för att utvärdera styrsystemets prestanda. Den numeriska simuleringen används för att verifiera den föreslagna modellen och utforma styrsystemet för det idealiska fallet. Den fysiska simuleringen används sedan för att validera prestandan för både det centraliserade och decentraliserade styrsystem i realtid. Slutligen demonstrerar vi hur de föreslagna styrsystemen utför sig i tre- respektive två-dimensionella experiment.

In a power system it is important to keep voltages at specific levels at network buses. Deviations from that can lead to reduced efficiency of transferred power or, in more severe cases, widespread power outages affecting large parts of society. There exists a variety of power system devices that have the ability to regulate the voltage levels. These devices have maximum and minimum control capacities and may have additional operational constraints. It is desired to keep the control capacity of these actuators close to neutral operation so that they have the ability to respond to future disturbances. Due to the nature of such a control problem, a suitable tool is Model Predictive Control. In this thesis, a centralised model predictive control is designed for long-term voltage stability control of a power system. The system model employed is a two-area power system model, where each area includes a network of generators and loads. The model predictive control regulates the tap position of a tap-changing transformer and the reactive power compensation provided by two capacitor banks. In this thesis, it is shown that a centralised model predictive controller successfully maintains voltages within the desired range for a 3.5 % longer duration compared to a decentralised control approach when facing a voltage collapse scenario. Additionally, thanks to its predictive capabilities, it efficiently dampened oscillations in the post-transient steadystate scenario, leading to a 6.6 % shorter settling time than that observed with the decentralised control approach. / I ett kraftsystem är det viktigt att hålla spänningen på specifika nivåer vid nätverksbussarna. Avvikelser från detta kan leda till nedsatt effektöverföringseffektivitet eller, i allvarligare fall, omfattande strömavbrott som påverkar stora delar av samhället. Det finns en mängd olika kraftsystemsenheter som har förmågan att reglera spänningsnivåerna. Dessa enheter har maximala och minimala kontrollkapaciteter och kan ha ytterligare driftbegränsningar. Det är önskvärt att hålla kontrollkapaciteten hos dessa enheter nära neutral drift så att de har förmågan att svara på framtida störningar. På grund av arten av ett sådant kontrollproblem är ett lämpligt verktyg Model Predictive Control. I den här avhandlingen är en centraliserad modellprediktiv reglering utformad för långsiktig spänningsstabilitetskontroll av ett kraftsystem. Systemmodellen som används är en två-area kraftsystemmodell, där varje område inkluderar ett nätverk av generatorer och belastningar. Kontrollen reglerar varvtalet hos en lindningskopplare och den reaktiva effektkompensationen som tillhandahålls av två kondensatorbanker. I denna avhandling visas det att en centraliserad modell-prediktiv reglering framgångsrikt kan upprätthålla spänningar inom det önskade intervallet under en 3.5 % längre varaktighet jämfört med en decentraliserad styrmetod under ett spänningskollapsscenario. Dessutom, tack vare dess prediktiva kapacitet, dämpade den effektivt svängningar i det post-transienta steady-state-scenariot, vilket ledde till en 6.6 % kortare insvängningstid än den som observerades med den decentraliserade styrmetoden.

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The main topic of this thesis is integer quadratic programming with applications to problems arising in the areas of automatic control and communication. One of the most widespread modern control principles is the discrete-time method Model Predictive Control (MPC). The main advantage with MPC, compared to most other control principles, is that constraints on control signals and states can easily be handled. In each time step, MPC requires the solution of a Quadratic Programming (QP) problem. To be able to use MPC for large systems, and at high sampling rates, optimization routines tailored for MPC are used. In recent years, the range of application of MPC has been extended from constrained linear systems to so-called hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem. Generally, for this type of optimization problems, the computational complexity is exponential in the number of binary optimization variables. In modern communication systems, multiple users share a so-called multi-access channel, where the information sent by different users is separated by using almost orthogonal codes. Since the codes are not completely orthogonal, the decoded information at the receiver is slightly correlated between different users. Further, noise is added during the transmission. To estimate the information originally sent, a maximum likelihood problem involving binary variables is solved. The process of simultaneously estimating the information sent by multiple users is called multiuser detection. In this thesis, the problem to efficiently solve MIQP problems originating from MPC is addressed. Two different algorithms are presented. First, a polynomial complexity preprocessing algorithm for binary quadratic programming problems is presented. By using the algorithm, some, or all, binary variables can be computed efficiently already in the preprocessing phase. In simulations, the algorithm is applied to unconstrained MPC problems with a mixture of real and binary control signals. It has also been applied to the multiuser detection problem, where simulations have shown that the bit error rate can be significantly reduced by using the proposed algorithm as compared to using common suboptimal algorithms. Second, an MIQP algorithm tailored for MPC is presented. The algorithm uses a branch and bound method where the relaxed node problems are solved by a dual active set QP algorithm. In this QP algorithm, the KKT-systems are solved using Riccati recursions in order to decrease the computational complexity. Simulation results show that both the QP solver and the MIQP solver proposed have lower computational complexity than corresponding generic solvers. / <p>Report code: LiU-TEK-LIC-2005:71.</p>

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