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A Smart WIFI Thermostat Data-Based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant TemperatureLou, Yisheng January 2021 (has links)
No description available.
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Indirekte modellprädiktive Regelung von Windenergieanlagen sowie deren energie-optimale und deren schädigungsarme KonfigurationSchwarz, Colin Maximilian 17 May 2023 (has links)
Die vorliegende Arbeit beschäftigt sich mit der Anwendung der indirekten Methoden zur automatisierten Lösung von einer bestimmten Klasse von Optimalen Steuerungsproblemen im Rahmen einer modellprädiktiven Regelung für Windenergieanlagen. In einem zweiten Teil wird der Einfluss dieser Regelungsmethode auf die Festigkeit des Triebstranges untersucht. Diese führt zu einer überproportionalen Beanspruchung und damit zu einer Reduktion der Betriebsfestigkeit. Es gilt entsprechende Randbedingungen für die der Regelung zugrunde liegenden Optimalen Steuerungsprobleme zu finden, so dass weiterhin die Energieausbeute maximiert werden kann, gleichzeitig jedoch die Beanspruchung durch die Regelung begrenzt wird.
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Model-Based versus Data-Driven Control Design for LEACH-based WSNKarlsson, Axel, Zhou, Bohan January 2020 (has links)
In relation to the increasing interest in implementing smart cities, deployment of widespread wireless sensor networks (WSNs) has become a current hot topic. Among the application’s greatest challenges, there is still progress to be made concerning energy consumption and quality of service. Consequently, this project aims to explore a series of feasible solutions to improve the WSN energy efficiency for data aggregation by the WSN. This by strategically adjusting the position of the receiving base station and the packet rate of the WSN nodes. Additionally, the low-energy adaptive clustering hierarchy (LEACH) protocol is coupled with the WSN state of charge (SoC). For this thesis, a WSN was defined as a two dimensional area which contains sensor nodes and a mobile sink, i.e. a movable base station. Subsequent to the rigorous analyses of the WSN data clustering principles and system-wide dynamics, two different developing strategies, model-based and data-driven designs, were employed to develop two corresponding control approaches, model predictive control and reinforcement learning, on WSN energy management. To test their performance, a simulation environment was thus developed in Python, including the extended LEACH protocol. The amount of data transmitted by an energy unit is adopted as the index to estimate the control performance. The simulation results show that the model based controller was able to aggregate over 22% more bits than only using the LEACH protocol. Whilst the data driven controller had a worse performance than the LEACH network but showed potential for smaller sized WSNs containing a fewer amount of nodes. Nonetheless, the extension of the LEACH protocol did not give rise to obvious improvement on energy efficiency due to a wide range of differing results. / I samband med det ökande intresset för att implementera så kallade smart cities, har användningen av utbredda trådlösa sensor nätverk (WSN) blivit ett intresseområde. Bland applikationens största utmaningar, finns det fortfarande förbättringar med avseende på energiförbrukning och servicekvalité. Därmed så inriktar sig detta projekt på att utforska en mängd möjliga lösningar för att förbättra energieffektiviteten för dataaggregation inom WSN. Detta gjordes genom att strategiskt justera positionen av den mottagande basstationen samt paketfrekvensen för varje nod. Dessutom påbyggdes low-energy adaptive clustering hierarchy (LEACH) protokollet med WSN:ets laddningstillstånd. För detta examensarbete definierades ett WSN som ett två dimensionellt plan som innehåller sensor noder och en mobil basstation, d.v.s. en basstation som går att flytta. Efter rigorös analys av klustringsmetoder samt dynamiken av ett WSN, utvecklades två kontrollmetoder som bygger på olika kontrollstrategier. Dessa var en modelbaserad MPC kontroller och en datadriven reinforcement learning kontroller som implementerades för att förbättra energieffektiviteten i WSN. För att testa prestandan på dom två kontrollmetoderna, utvecklades en simulations platform baserat på Python, tillsamans med påbyggnaden av LEACH protokollet. Mängden data skickat per energienhet användes som index för att approximera kontrollprestandan. Simuleringsresultaten visar att den modellbaserade kontrollern kunde öka antalet skickade datapacket med 22% jämfört med när LEACH protokollet användes. Medans den datadrivna kontrollern hade en sämre prestanda jämfört med när enbart LEACH protokollet användes men den visade potential för WSN med en mindre storlek. Påbyggnaden av LEACH protokollet gav ingen tydlig ökning med avseende på energieffektiviteten p.g.a. en mängd avvikande resultat.
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Towards Spatio-temporally Integrated Design and Operations of Techno-Ecological Synergistic SystemsShah, Utkarsh Dinesh 13 September 2022 (has links)
No description available.
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Extended-Speed Finite Control Set Model Predictive Torque Control for Switched Reluctance Motor Drives with Adaptive Commutation AnglesTarvirdilu Asl, Rasul January 2020 (has links)
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)
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Predictive control of fuel cell hybrid construction machines / Prediktiv styrning av bränslecellshybridbyggmaskinerKumaraswamy, Aniroodh January 2023 (has links)
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.
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Dynamic Modeling, System Identification, and Control Engineering Approaches for Designing Optimized and Perpetually Adaptive Behavioral Health InterventionsJanuary 2021 (has links)
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
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Predictive Controllers for Load Transportation in Microgravity EnvironmentsPhodapol, Sujet January 2023 (has links)
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.
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Centralised MPC for Long-term Voltage Stability Control of Power System / Centraliserad MPC för långsiktig spänningsstabilitetskontroll av kraftsystemHallberg, Johan January 2023 (has links)
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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Perturbed Optimal Control for Connected and Automated VehiclesGupta, Shobhit January 2022 (has links)
No description available.
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