This thesis considers the problem of control of nonlinear systems subject to limited availability of measurements and uncertainty in model parameters. To address this problem, first a linear offset free MPC is designed. Subsequently, a Lyapunov-based offset free MPC design is presented to handle structured uncertainty subject to constant disturbances. The controller's ability to handle unstructured uncertainty and measurement noise is demonstrated through simulation examples. Next, the problem of handling lack of state measurements as well as uncertainty is considered. To achieve simultaneous state and disturbance parameter estimation, a Lyapunov-based model predictive controller (MPC) is integrated with a moving horizon based mechanism, to achieve (where possible) offset elimination in the unmeasured states as well. A chemical reaction process example is presented to illustrate the key points. Finally its efficacy is demonstrated through a polymerization process example. / Thesis / Doctor of Philosophy (PhD)

<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>

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>