<p>To reduce greenhouse gas emissions, shifting the energy sources used in buildings, transportation, industry, etc., from fossil fuels to clean electricity is a trend. The increasing electricity demand stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions.  Residential and commercial buildings' space heating/cooling system consumes considerable electricity. Integrating thermal energy storage (TES) in building heating/cooling systems can mitigate the challenge of electric grids. Applying TES to existing air-source heat pump (ASHP) systems is the most studied for residential buildings. However, the high-quality thermal energy requirement for charging the TES tank results in low thermal performance of the ASHP system. Moreover, the failure of ASHP in cold climates requires a supplemental electric heater that significantly reduces the system efficiency and may lead to a higher annual peak for the grids.</p> <p>This study proposes integrating TES with ground-source heat pump (GSHP) systems as a more effective solution for building decarbonization due to the high efficiency of renewable-energy-based GSHPs year-round. This study focuses on proving the effectiveness of TES-integrated GSHPs for building decarbonization.  A dual-source heat pump (DSHP) with a hybrid TES and ground heat exchanger (GHE) named dual-purpose underground thermal battery (DPUTB) is investigated. The study uses modeling and experiments to verify the system's energy efficiency, decarbonization potential, and demand response capability. The modeling process involves developing various models, from component-level to system-level, and investigating advanced control strategies. A first-of-this-kind dynamic model of the DPUTB is developed to enable high-resolution system simulation for the GSHP system. The simulation is conducted using Modelica with rule-based control (RBC). A model predictive control (MPC) is also developed based on dynamic building envelope and heating, ventilation, and air conditioning (HVAC) system models. A cutting-edge co-simulation testbed integrates Modelica physical models with a MATLAB MPC controller model for advanced control evaluation. A prototype system of the DPUTB+DSHP is tested in a flexible research platform (FRP) at Oak Ridge National Laboratory (ORNL), which allows for component and system-level testing and remote automation controls. </p> <p>The study highlights the importance of proper insulation in the performance of the DPUTB, which consists of a TES tank enclosed by an outer tank functioning as a GHE. With appropriate insulation, a full-size DPUTB can store 1-ton cooling (3.5 kW) for four hours after eight hours of charging. Simulation results suggest that decoupling the TES with the GHE could reduce energy consumption by 27%.  System-level simulations confirm that the DSHP+DPUTB system, with a customized RBC, outperforms the conventional ASHP. The proposed system can reduce the annual HVAC electricity cost by up to 50% while saving 45% on electricity consumption. In the Northern areas of the United States, the annual peak load of the HVAC system can be reduced by 60%.  However, this reduction is less in the Southern parts of the as the system's higher efficiency in winter dominates the overall decrease. The application of MPC can further reduce the cost and energy consumption of the system by 35% theoretically. However, the accuracy of model prediction affects its performance in practical applications, which can be mitigated by employing technologies such as machine learning and reinforcement learning. Further research is required to verify these technologies.</p> <p>The DSHP+DPUTB system, a type of TES-integrated GSHP, has been well-designed and demonstrated superior performance to conventional systems, with greater flexibility and thermal efficiency. As a result, this system can enable electrification in the space heating sector without requiring an escalation in the grid. Moreover, alternative controls can be utilized to exploit its decarbonization potential fully.</p>

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<p>Secure computation allows mutually distrusting parties to compute over private data. Such collaborations have widespread applications in social, scientific, commercial, and security domains. However, the overhead of achieving security is a major bottleneck to the adoption of such technologies. In this context, this thesis aims to design the most secure protocol within budgeted computational or network resources by mathematically formulating it as an optimization problem. </p> <p>With the rise in CPU power and cheap RAM, the offline-online model for secure computation has become the prominent model for real-world security systems. This thesis investigates the above-mentioned optimization problem in the information-theoretic offline-online model. In particular, this thesis presents the following selected sample of our research in greater detail. </p> <p>Round and Communication Complexity: Chor-Kushilevitz-Beaver characterized the round and communication complexity of secure two-party computation. Since then, the case of functions with randomized output remained unexplored. We proved the decidability of determining these complexities. Next, if such a protocol exists, we construct the optimal protocol; otherwise, we present an obstruction to achieving security. </p> <p>Rate and Capacity of secure computation: The efficiency of converting the offline samples into secure computation during the online phase is essential. However, investigating this ``production rate'' for general secure computations seems analytically intractable. Towards this objective, we introduce a new model of secure computation -- one without any communication -- that has several practical applications. We lay the mathematical foundations of formulating rate and capacity questions in this framework. Our research identifies the first tight rate and capacity results (a la Shannon) in secure computation. </p> <p>Reverse multiplication embedding: We identify a new problem in algebraic complexity theory that unifies several efficiency objectives in cryptography. Reverse multiplication embedding seeks to implement as many (base field) multiplications as possible using one extension field multiplication. We present optimal construction using algebraic function fields. This embedding has subsequently led to efficient improvement of secure computation, homomorphic encryption, proof systems, and leakage-resilient cryptography. </p> <p>Characterizing the robustness to side-channel attacks: Side-channel attacks present a significant threat to the offline phase. We introduce the cryptographic analog of common information to characterize the offline phase's robustness quantitatively. We build a framework for security and attack analysis. In the context of robust threshold cryptography, we present a state-of-the-art attack, threat assessment, and security fix for Shamir's secret-sharing. </p> <p><br></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)

The research on autonomous vehicles, and more specifically cooperation between autonomous vehicles, has become a prominent research field during the last cou- ple of decades. One example is the combination of an unmanned aerial vehicle (UAV) together with an unmanned ground vehicle (UGV). The benefits of this are that the two vehicles complement each other, where the UAV provides an aerial view and can reach areas where a ground vehicle can not. Furthermore, since the UAV has a limited range, the UGV can then serve as transport and recharge sta- tion for the UAV. This master thesis studies how model predictive control (MPC) can be used to land a UAV on a moving UGV.  A linear MPC is chosen, since previous work using this has shown promising results. The UAV is chosen to be controlled using commands in pitch, roll and climbing rate. The MPC is designed as a decoupled controller, with a separate horizontal and vertical controller. This allows for a spatial constraint to be im- plemented, which constrains the UAV from entering ground level before arriving above the UGV. It also constrains the UAV from potentially hitting protruding ob- jects on the UGV. The horizontal controller uses a simple planner, which guides the UAV to land on the UGV from behind.  The MPC is evaluated using a additive white Gaussian noise (AWGN) sen- sor error model with zero mean. The scenario used is that the UAV starts 50 m from the UGV, and the UGV starts driving in a given direction turning randomly. The MPC lands successfully in 100 % of the simulations for a wide range of tun- ings. The MPC maintains the same landing statistics with a delay in the sensor information of up to 500 ms. The AWGN could be increased while maintaining successful landings, however with significantly more retakes and longer landing times. Lower AWGN variance only slightly improves performance, suggesting that the MPC is quite robust towards high variance in the state estimation.  The MPC is also compared to a PID controller. The MPC has significantly shorter landing times. The PID has a more oscillatory control signal, however, the PID has a lower variance in landing positions, but a slightly less centered mean on the UGV. The overall results show that an MPC can be used to achieve a flexible controller that can be tuned and reformulated to fit the situation, and performs as good or better compared to a PID controller.  The hardware tests show promising results for the implementation of the MPC. The controller is not tuned and no system identification is done specifi- cally for the physical UAV, suggesting that the controller is robust for varying settings. Even though the UAV never lands on the UGV, the visual behavior and control signal plots suggest that it would be able to land. However, these tests are performed using global navigation satellite system state estimation on a sta- tionary UGV, therefore further tests need to be performed in more challenging scenarios.

The upward trends in renewable energy deployment in recent years brings new challengesto the development of electrical networks. Interconnected microgrids appear as a novelbottom-up approach to the production and integration of renewable energy.Using model predictive control (MPC), the energy management of several interconnectedmicrogrids is investigated. An optimisation problem is formulated and distributed ontothe individual units using the alternating direction method of multipliers (ADMM). Themicrogrids cooperate to reach a global optimum using neighbour-to-neighbour communications.The benefits of using distributed operation control for microgrids are analysed and a controlarchitecture is proposed. Two algorithms are implemented to solve the optimisationproblem and their advantages or differences are confronted. / Förnybara energikällor har ökat under senaste åren. Det innebär nya utmaningar förevolutionen av elektriska nät. Microgrids är en bottom-up ansats för produktion ochintegrering av förnybar energi.Energiförsörjning av flera sammankoppladeMicrogrids studeras in detta arbete genommodellbaserad prediktiv kontroll (MPC). Ett optimeringsproblem formuleras på de enskildaenheterna med Alternating DirectionMethod ofMultipliers (ADMM) och parallellberäkningar härledas.Microgrids samarbetar för att nå en global lösning av neighbourto-neighbour kommunikation.Distribuerad energiförsörjning av microgrids analyseras och två kontroll algorithmerutformas.

The aim of this project was to compare two control algorithms designed to steer an autonomous vehicle. The comparison was made using a simulated environment to evaluate the performance of both controllers. The simulation used in this project was designed in Python and used an algorithm which randomly constructed roads from predefined road segments to create paths for the vehicle to follow. In this environment the Linear Time Varying (LTV)-Model Predictive Controller (MPC) and Pure Pursuit Controller (PPC) algorithms were evaluated. The thesis compared how well they follow paths, the average control cost of completing tasks, how well they handle input constraints, and the computational time for each algorithm. The data was collected by driving along three sets of randomly generated roads with both control algorithms. One set mostly straight, one with some turns, and one with mostly turns. An Analysis of Variance (ANOVA) test was used to make the comparison between the performance of the two algorithms. The results showed that both algorithms performed well. The PPC had low computation time and used less control, but it also had larger position errors. The LTV-MPC had higher computation time, but smaller position errors at the cost of larger control values. The conclusion is that the MPC is preferable if computational capabilities are available. Room for future work exists in the form of comparing additional controller types for autonomous vehicles and exploring different tuning parameters for the MPC controller. The simulation could also be expanded to more accurately reflect real world conditions. / Målet med detta projekt var att jämföra två kontrollalgoritmer avsedda för att styra en självkörande bil. Jämförelsen gjordes med hjälp av en simulering som utformades i Python. Den använde sig av en algoritm som slumpmässigt satte ihop vägar från förkonstruerade delar för att skapa banor för den självkörande bilen att följa. I denna miljö har vi testat två algoritmer, en LTV-MPC och en PPC. Vi jämförde hur pass väl de följer banor som skall likna riktiga vägar, hur mycket styrning de använder sig av för att bedöma energianvändning, hur väl de förhåller sig till begränsningar på acceleration och styrning, och den beräkningstiden som krävdes för att köra vår algoritm. Datan samlades genom att köra längs med tre grupper av slumpmässigt genererade vägar med båda kontrollalgoritmerna. En grupp innehöll huvudsakligen raka sträckor, en innehöll en del svängar, och en innehöll mycket svängar. ANOVA-testet användes för att göra jämförelsen mellan resultatet av dessa två algoritmer. Resultatet visade att båda algoritmer presterar väl. PPCn hade låg beräkningstid och mindre styrvärden, men större positionsfel. MPCn hade högre beräkningstid och större styrvärden, men mindre positionsfel. Slutsatsen är att MPCn är att föredra om beräkningsmöjligheterna finns tillgängliga. Det finns utrymme för framtida arbete i form av att jämföra fler kontrollalgoritmer och att utforska fler parameter justeringar för MPCn. Utöver det finns det även utrymme för en simulation som reflekterar verkligheten noggrannare.

This thesis presents the initial components of an integrated guidance, navigation, and control system for vertical take-off and landing (VTOL) autonomous unmanned aerial vehicles (UAVs) such that they may map complex environments that may be hostile. The first part of this thesis presents an autonomous guidance system. For goal selection, the map is partitioned around the presence of obstacles and whether that area has been explored. To perform this partitioning, the Octree algorithm is implemented. In this thesis, we test this algorithm to find a parameter set that optimizes this algorithm. Having selected goal points, we perform a comparison of the LPA* and A* path planning algorithms with a custom heuristic that enables reckless or tactical maneuvers as the UAV maps the environment. For trajectory planning, the fMPC algorithm is applied to the feedback-linearized equations of motion of a quadcopter. For collision avoidance, standalone versions of 4 different constraint generation algorithms are evaluated to compare their resulting computation times, accuracy, and computed volume on a voxel map that simulates a 2-story house along with fixed paths that vary in length at fixed intervals as basis of tests. The second part of this thesis presents the theory of Model Reference Adaptive Control(MRAC) along with augmentation for output signal tracking and switched-dynamic systems. We then detail the development of longitudinal and lateral controllers a Quad-Rotor Tailsitter(QRBP) style UAV. In order to successfully implement the proposed controller on the QRBP, significant effort was placed upon physical design and testing apparatus. / Master of Science / For an autonomously operated, Unmanned Aerial Vehicle (UAV), to operate, it requires a guidance system to determine where and how to go, and a control system to effectively actuate the guidance system's commands. In this thesis, we detail the characterization and optimization of the algorithms comprising the guidance system. We then delve into the theory of MRAC and apply it toward a control system for a QRBP. We then detail additional tools developed to support the testing of the QRBP.

Cette thèse propose des stratégies de gestion de l'énergie conçues pour un bus urbain électrique hybride. Le système de commande hybride devrait créer une stratégie efficace de coordination du flux d’énergie entre le moteur thermique, la batterie, les moteurs électriques et hydrauliques. Tout d'abord, une approche basée sur la programmation dynamique déterministe (DDP) a été proposée : algorithme d'optimisation simultanée de la vitesse et de la puissance pour un trajet donné (limité par la distance parcourue et le temps de parcours). Cet algorithme s’avère être gourmand en temps de calcul, il n’a pas été donc possible de l’utiliser en temps réel. Pour remédier à cet inconvénient, une base de données de profils optimaux basée sur DP (OPD-DP) a été construite pour une application en temps réel. Ensuite, une technique de programmation dynamique stochastique (SDP) a été utilisée pour générer simultanément et d’une manière optimale un profil approprié de la vitesse du Bus ainsi que sa stratégie de partage de puissance correspondante. Cette approche prend en compte à la fois la nature stochastique du comportement de conduite et les conditions de circulations urbaines (soumises à de multiples aléas). Le problème d’optimisation énergétique formulé, en tant que problème intrinsèquement multi-objectif, a été transformé en plusieurs problèmes à objectif unique avec contraintes utilisant une méthode ε-constraint afin de déterminer un ensemble de solutions optimales (le front de Pareto).En milieu urbain, en raison des conditions de circulation, des feux de circulation, un bus rencontre fréquemment des situations Stop&Go. Cela se traduit par une consommation d'énergie accrue lors notamment des démarrages. En ce sens, une stratégie de régulation de vitesse adaptative adaptée avec Stop&Go (eACCwSG) apporte un avantage indéniable. L'algorithme lisse le profil de vitesse pendant les phases d'accélération et de freinage du Bus. Une autre caractéristique importante de cet algorithme est l’aspect sécurité, étant donné que l’ACCwSG permet de maintenir une distance de sécurité afin d’éviter les collisions et d’appliquer un freinage en douceur. Comme il a été mentionné précédemment, un freinage en douceur assure le confort des passagers. / This PhD thesis proposes Energy Management Strategies conceived for a hybrid electrical urban bus. The hybrid control system should create an efficient strategy of coordinating the flow of energy between the heat engine, battery, electrical and hydraulic motors. Firstly, a Deterministic Dynamic Programming (DDP) based approach has been proposed: simultaneous speed and powersplit optimization algorithm for a given trip (constrained by the traveled distance and time limit). This algorithm turned out to be highly time consuming so it cannot be used in real-time. To overcome this drawback, an Optimal Profiles Database based on DP (OPD-DP) has been constructed for real-time application. Afterwards, a Stochastic Dynamic Programming (SDP) technique is used to simultaneously generate an optimal speed profile and related powersplit strategy. This approach takes into account a stochastic nature of the driving behavior and urban conditions. The formulated energy optimization problem, being intrinsically multi-objective problem, has been transformed into several single-objective ones with constraints using an ε-constraint method to determine a set of optimal solutions (the Pareto Front).In urban environment, due to traffic conditions, traffic lights, a bus encounters frequent Stop&Go situations. This results in increased energy consumption during the starts. In this sense, a relevant Eco Adaptive Cruise Control with Stop&Go (eACCwSG) strategy brings the undeniable benefit. The algorithm smooths speed profile during acceleration and braking phases. One more important feature of this algorithm is the safety aspect, as eACCwSG permits to maintain a safety distance in order to avoid collision and apply a smooth braking. As it was mentioned before, smooth braking ensures passengers comfort.

This master thesis project is focused on the development of an LQR controller and its comparison with other controllers (PID and MPC), in order to successfully control an Autonomous Underwater Vehicle manipulation system. The modelling of the manipulator was performed first in Matlab and later on in Simulink-Simscape. Once the manipulator was integrated with the AUV model, the LQR controller was also developed initially in Matlab and then in Simulink. The controller was then extracted from Simulink as a C-code and verified in Stonefish. After confirming that the LQR code was working in Stonefish, its results from Simulink were compared with PID and MPC results for two different trajectories. The data for comparison and statistical analysis were divided into the two trajectory scenarios (horizontal and vertical) since the weight matrices of both controllers were different. Looking at the system’s overall behavior the Model Predictive Control (MPC) and LQR had similar results, regarding the rise time, overshoot, steady-state error and robustness to disturbances. An anticipated fact for the MPC was that it takes the longest run time for both scenarios. Lastly, as expected the PID had the worst response of all three controllers, in both scenarios. Implementing a PID on a nonlinear system, produced many oscillations without being able to stabilize at the reference value, thus giving a large steady-state error. In addition, it could not counteract the noise disturbances in the signal. / Detta examensarbete är inriktat på utvecklingen av en LQR-styrenhet och dess jämförelse med andra kontroller (PID och MPC), för att framgångsrikt styra ett autonomt undervattensfordon-manipulationssystem. Modelleringen av manipulatorn utfördes först i Matlab och senare i Simulink-Simscape. När manipulatorn väl hade integrerats med AUV modellen, utvecklades LQR styrenheten också inledningsvis i Matlab och sedan i Simulink. Kontrollenheten extraherades sedan från Simulink som en C-kod och verifierades i Stonefish. Efter att ha bekräftat att LQR koden fungerade i Stonefish, jämfördes resultaten från Simulink med PID och MPC resultat för två olika banor. Data för jämförelse och statistisk analys delades in i de två bana-scenarierna (horisontella och vertikala), eftersom viktmatriserna för båda kontrollerna var olika. När man tittar på systemets övergripande beteende hade Model Predictive Controller (MPC) och LQR liknande resultat när det gäller stigningstid, överskott, steady-state fel och robusthet mot störningar. Ett förväntat faktum för MPC var att det tar den längsta körtiden för båda scenarierna. Slutligen, som väntat, hade PID det sämsta svaret av alla tre kontrollerna, i båda scenarierna. Implementering av ett PID på ett olinjärt system gav många oscillationer utan att kunna stabilisera sig vid referensvärdet, vilket gav ett stort steady-state fel. Dessutom kunde den inte motverka bullerstörningarna i signalen.