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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

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>

This thesis deals with the problems of design and operation of process supply chains. Process supply chains face many challenges due to volatile market conditions, production and transportation delays, and stiff market competition, which ultimately affect their profitability. Supply chain management (SCM) is the process of managing the flow of materials and information within supply chain to optimize the SC performance. SCM is carried out using a hierarchical decision-making framework, where the top most layer looks at network design and the bottom-most layer deals with scheduling day-to-day activities. In this research, the systems engineering principles are applied to devise an improved methodology for supply chain optimization (SCO). First, we consider the design of supply chain in the presence of demand uncertainty. The representation of network topology plays an important role in deriving the optimal network design. In real practice, the shipping cost for transferring goods from one location to another is determined based on service time and quantity. More importantly, the cost associated with establishing a transportation linkage is relatively small for existing transportation infrastructure and can be changed if beneficial. The flexibility of changing the transportation routes is included in the network topology representation by the explicit inclusion of time limited transportation contract agreements. Further, the customer demand is volatile, and it is very difficult to predict accurately. To handle the demand uncertainty, a two-stage stochastic programming formulation is applied in the SC design approach. Next, we consider the problem of handling uncertainty in SC planning by applying a system engineering control principle, robust model predictive control (MPC). The uncertainty in model parameters (yield) and demand are captured by stochastic programming. In this approach, the planning activities are represented by a hybrid model with decisions governed by logical conditions/rulesets. An MPC based rolling horizon control framework is used to schedule the planning activities, where the SC performance is expressed using a multi-criterion objective comprising customer service and economics. The uncertainty in demand and yield are propagated by two mechanisms - an open-loop approach, and an approximate closed-loop strategy. Finally, we consider the problem of integration of SC planning and scheduling. Due to the use of different time scale models for planning and scheduling, the decision derived from the planning layer may result in infeasibility when those targets are implemented at the scheduling level, which ultimately affects the supply chain efficiency. To address this issue, we model tactical and operational planning activities using an integrated hybrid time modeling approach in which the first few planning periods are formulated using an operational planning model and the remaining time periods are modeled with a tactical planning model. The main rationale for formulating an integrated model is that customer demand forecast becomes less accurate for a future time, therefore making a detailed planning model unnecessary. A key benefit of using a hybrid modeling approach is that it avoids the problem of infeasibility encountered in the hierarchical decision framework, as well as the computational burden associated with the use of a detailed planning model over a long time horizon. We employ an MPC based rolling horizon framework as a tactical decision policy where the integrated model is used to predict the system behavior. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">Personalized thermal controls are beneficial for occupant comfort and productivity in office buildings. Recent research efforts on learning personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This Thesis presents the development and field implementation of personal preference-based thermal control in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles, a low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. The customized, preference-based HVAC control implemented in the offices indicated the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28-35% energy savings with personalized MPC (vs personalized static setpoint control).</p><p dir="ltr">Regarding the practical limitations in collecting sufficient data from occupants to train their thermal comfort model, we present a Bayesian meta-learning approach for developing reliable, data-driven personalized thermal comfort models using limited data from individuals. A high-dimensional neural network was developed, considering general thermal comfort impact factors (environmental variables, clothing level and metabolic rate) as well as personal thermal characteristics (expressed as a vector of continuous latent variables) as model inputs. The model parameters in the neural network were trained with subsets of ASHRAE RP-884 database. The trained neural network is transferrable, so that the thermal preferences of new individuals can be predicted by inferring their personal thermal characteristics using limited data. The results show that the developed Bayesian meta-learning approach to infer personal thermal comfort performs better than existing methods, especially when using limited data.</p><p dir="ltr">Moreover, this Thesis also discusses the potential of balancing thermal comfort and energy cost by setting dynamic temperature constraints in personalized MPC. A co-simulation framework of EnergyPlus and MPC is constructed using EnergyPlus Python API. Dynamic temperature constraints are selected based on personal thermal profile, weather conditions and utility rate variations. The performance of the personalized MPC with dynamic constraints demonstrates a balance between thermal comfort and energy cost in cooling season.</p>