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)

This thesis considers the problem of implementation of Model Predictive Control (MPC) strategies in the general area of Heating, Ventilation, Air Conditioning (HVAC). Specifically, the contributions utilize the constraint handling and optimality properties of MPC to achieve energy efficient control of many different HVAC systems. First, the thesis focuses on a linear offset-free MPC design for a vapor compression cycle. The key contributions include a a sequential tuning method and application to a detailed simulation test-bed, demonstrating superior closed-loop results to that of traditional control strategies in the presence of both disturbances and measurement noise. Next, a modified linear offset-free MPC formulation is implemented on a heat pump. The key contribution is the formulation of an optimization problem that recognizes the tradeoff between energy conservation and tracking performance. Simulation results illustrate superior performances as measured through three separate metrics: safety, energy efficiency and tracking. The implementation of MPC formulations to these realistic problems also pointed to a lack of MPC formulations with explicit performance considerations in the control design. Thus, in the final part of the thesis, these observed shortcomings in the standard offset-free linear MPC design are addressed via a new performance specification-based MPC. Desired closed-loop output response is specified and achieved through a tiered optimization formulation that can handle plant model mismatch. Superior closed-loop response, in terms of desired transient behavior and disturbance rejection, relative to standard linear-based and offset-free MPC designs is achieved. Finally, directions for future work are discussed. / Thesis / Doctor of Philosophy (PhD)

Energy management strategies for microgrids, containing energy storage, renewable energy sources (RES), and electric vehicles (EVs); which interact with the grid on an individual basis; are presented in Chapter 3. An optimization problem to reduce cost, formulated over a rolling time horizon, using predicted values of load demand, EV connection/disconnection times, and charge levels at time of connection, is described. The solution provides the on-site storage and EV charge/discharge powers. For the first time, both bidirectional and unidirectional charging are considered for EVs and a controller which accommodates uncertainties in EV energy levels and connection/disconnection times is presented. In Chapter 4, a stochastic chance constraints based optimization is described. It affords significant improvement in robustness, over the conventional controller, to uncertainties in system parameters. Simulation results demonstrate that the stochastic controller is at least twice as effective at meeting the desired EV charge level at specific times compared to the non-stochastic version, in the presence of uncertainties. In Chapter 5, a network of microgrids, containing RES and batteries, which trade energy among themselves and with the utility grid is considered. A novel distributed energy management system (EMS), based on a central EMS using a Multi-Objective (MO) Rolling Horizon (RH) scheme, is presented. It uses Alternating Direction Method of Multipliers (ADMM) and Quadratic Programming (QP). It is inherently more data-secure and resilient to communication issues than the central EMS. It is shown that using an EMS in the network provides significant economic benefits over MGs connected directly to the grid. Simulations demonstrate that the distributed scheme produced solutions which are very close to those of the central EMS. Simulation results also reveal that the faster, less memory intensive distributed scheme is scalable to larger networks -- more than 1000 microgrids as opposed to a few hundreds for the central EMS. / Thesis / Doctor of Philosophy (PhD)

A formulation of a subspace identification method for multi-phase processes with applications to rotational moulding and suggestions for improvements and experimental applications. / This thesis focuses on the implementation of subspace identification (SSID) for nonlinear, chemical batch processes by introducing a model identification method for multi-phase processes. In this thesis, a multi-phase process refers to chemical or biological batch-like processes with properties that cause a change in the dynamics during the evolution of the process. This can occur, for example, when a process undergoes a change of state upon reaching a melting point. Existing SSID techniques are not designed to utilize any known, multiphase nature of a process in the model identification stage. The proposed approach, Multiphase Subspace Identification (MPSSID), is conducted by first splitting historical data into phases during the identification step and then building a subspace model for each phase. The phases are then connected via a partial least squares (PLS) model that transforms the states from one phase to the next. This approach makes use of existing SSID techniques that allow for model construction using batches of nonunifrom length. Here, MPSSID is applied to a uniaxial rotational moulding process. In rotational moulding, the dynamics switch as the process undergoes heating, melting, and sintering stages that are visibly distinct and recognizable upon a certain temperature (not time) being reached. Results demonstrate the ability of multiphase models to better predict the temperature trajectories and final product quality of validation batches. As an extension to this rotational moulding analysis, additional MPSSID methods of implementation are proposed and the results are compared. A MPSSID mixed integer linear program is then introduced for implementation within model predictive control. The applications to rotational moulding are presented within the context of plastics manufacturing and the impact of plastic on the global climate crisis, with suggestions for future work. / Thesis / Master of Applied Science (MASc) / The control of chemical processes is an important factor in achieving high quality products. To control a process well, the mathematical model of the system must be accurate. In the past, mathematical models for process control were designed based on engineering approximations. Now, with major advances in computing and sensor technology, it is possible to design a simulation of the entire process. These simulations can be designed using first-principles or black box approaches. First-principles approaches utilize rigorous models that are based on the complex chemical and physical formulas that govern a system. Black box approaches do not look at the first-principles dynamics. They only utilize the measured process inputs and outputs to form a model of the system. They are widely used because of their ease of implementation in comparison to first-principles approaches. In this thesis, a new black box process control model is proposed and is found to yield better theoretical results than existing techniques. This model is tested on data from a plastics manufacturing process called rotational moulding, which involves loading polymer powders into a mould that is simultaneously rotated and heated to yield seamless plastic parts. Lastly, a control framework that is compatible with the new black box model is proposed to be used for future experimental tests.

With the growing capacity of renewable energy production sources, nuclear energy, once a mainstay of power generation, faces challenges due to its limited adaptability to fluctuating energy demands. This inherent rigidity makes it less desirable than the more flexible renewable sources. However, integrating thermal energy storage (TES) systems offers a promising avenue, enabling nuclear power plants (NPPs) to enhance their operational flexibility and remain competitive in an evolving renewable market. A comprehensive ranking methodology has been introduced, delineating the criteria and processes to determine the most synergistic TES/NPP design couplings. This methodology considers the unique characteristics of both current and prospective reactor fleets, ensuring broad applicability across various nuclear technologies. Economic analysis further supports the case for TES integration. Findings indicate that when equipped with TES systems, NPPs can remain price competitive, even with carbon-neutral alternatives like solar power generation. A lab-scale TES system was meticulously designed and constructed to validate these theoretical propositions. For its control, the Python GEKKO model predictive control (MPC) was employed, a decision influenced by the proven efficacy of GEKKO in managing complex systems. Tests conclusively demonstrated the feasibility and efficiency of using GEKKO for MPC of TES systems. A novel methodology for the MPC of a RELAP5-3D input deck has been proposed and elaborated upon. This methodology was rigorously tested at two distinct scales. The initial focus was on a thermal-hydraulic model of the lab-scale TES system. Subsequent efforts scaled up to control a more intricate thermal-hydraulic model, representing a small modular reactor (SMR) paired with an oil-based TES system. In both scenarios, GEKKO exhibited exemplary performance, controlling the RELAP5-3D models with precision and ensuring they met the stipulated demand parameters. The research underscores the potential of RELAP5-3D MPC in streamlining the licensing process for TES systems intended for NPP coupling. This approach could eliminate the need for expensive and time-consuming experiments, paving the way for more efficient and cost-effective nuclear energy solutions.

<p>This thesis aims to design a controller to apply medication to patients with hypothyroidism, a disease that occurs due to the underacting thyroid gland. The body cannot produce sufficient thyroid hormones, which leads to an increase in the production of hormones in the pituitary gland. The thyroid malfunctioning could lead to other associated conditions like nausea, fatigue, heart conditions, higher cholesterol, and elevated blood pressure. Thus, it is essential to ensure that the levels of thyroid hormones, Triiodothyronine (T3) and Thyroxine (T4), are healthy. The production of these hormones is governed by the hypothalamus-pituitary-thyroid (HPT) axis, a part of the endocrine system. This illness cannot be cured but can be regulated entirely through medication. The standard practice to control hypothyroidism in patients is to prescribe a constant daily dosage of synthetic T4 (i.e., levothyroxine) and, in some cases, an additional dose of synthetic T3 (i.e., Liothyronine). In this thesis, simulation studies are performed where two patients with varying levels of hypothyroidism are prescribed constant doses of synthetic hormones. The medications initially help the patients but are unsuccessful in maintaining healthy ranges. Using model predictive control, an observer-controller-based compensator is proposed to prescribe varying medication doses as inputs based on the patient's requirement. The inputs are quantized to be practically implemented in a real patient scenario. This compensator successfully improves the patient's hormone levels toward healthy values and ensures that the hormone trajectories follow the body's circadian rhythm.  </p>

Data-driven model predictive control framework of semicontinuous distillation process / Distillation technology is one of the most sought-after operations in the chemical process industries. Countless research has been done in the past to reduce the cost associated with distillation technology. As a result of process intensification, a semicontinuous distillation system is proposed as an alternative for purifying the n-component mixture (n>=3) which has the advantage over both batch and continuous process for low to medium production rates. A traditional distillation setup requires n-1 columns to separate the components to the desired purity. However, a semicontinuous system performs the same task by integrating a distillation column with n-2 middle vessel (storage tank). Consequently, with lower capital cost, the total annualized cost (TAC) per tonne of feed processed is less for a semicontinuous system compared to a traditional setup for low to medium throughput. Yet, the operating cost of a semicontinuous system exceed those of the conventional continuous setup. Semicontinuous system exhibits a non-linear dynamic behavior with a cyclic steady state and has three modes of operation. The main goal of this thesis is to reduce the operating cost per tonne of feed processed which leads to lower TAC per tonne of feed processed using a model predictive control (MPC) scheme compared to the existing PI configuration This work proposes a novel multi-model technique using subspace identification to identify a linear model for each mode of operation without attaining discontinuity. Subsequently, the developed multi-model framework was implemented in a shrinking horizon MPC architecture to reduce the TAC/tonne of feed processed while maintaining the desired product purities at the end of each cycle. The work uses Aspen Plus Dynamics simulation as a test bed to simulate the semicontinuous system and the shrinking horizon MPC scheme is formulated in MATLAB. VBA is used to communicate the inputs from MPC in MATLAB to the process in Aspen Plus Dynamics. / Thesis / Master of Science in Chemical Engineering (MSChE)

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24613号 / 工博第5119号 / 新制||工||1979(附属図書館) / 京都大学大学院工学研究科原子核工学専攻 / (主査)教授 村上 定義, 教授 横峯 健彦, 教授 宮寺 隆之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Gene therapy is advancing rapidly, with Recombinant Adeno-associated virus (rAAV) being investigated for potential use in treating cancer and neurological disorders. Plasmid DNA transfection and viral infection are standard methods for producing large-scale rAAV vectors. However, improving yield production requires careful monitoring and control of process state variables, which can be expensive and time-consuming. This thesis proposes a model predictive control (MPC) model that can efficiently monitor, predict, and optimize the final product by controlling state variables like DOT and temperature. The model relies on an unstructured mechanistic kinetic model designed explicitly based on rAAV upstream production. Monitoring viral vector production based on substrate or biomass concentration enhances bioprocess production efficiency. However, other state variables like dissolved oxygen (DO), pH, and temperature should also be considered. The objective of this thesis is to enhance cell growth in bioreactors by regulating dissolved oxygen and temperature levels using a Model Predictive Control (MPC) system. This model can be employed in different processes to enhance cell growth and examine the impact of control measures. The goal is to achieve a high cell density, increase productivity, and lower costs in a shorter duration. Simulink, a software tool developed by MATLAB, seamlessly integrates Ordinary Differential Equations (ODEs) to optimize bioprocesses in bioreactors. The Model Predictive Control (MPC) controller expertly regulates Dissolved Oxygen Tension (DOT) and temperature, thereby increasing cell growth concentrations. This sophisticated controller efficiently manages multiple variables simultaneously and exceeds the Proportional Integral Derivative (PID) controller. The model is straightforward to comprehend and promptly responds to anomaly data. To evaluate the suggested resolution, we conducted tests on both PID and MPC controllers by introducing measurement noise to the DOT. Our analysis indicated that MPC demonstrated superior performance based on the ISE (Integral of Squared Error), IAE (Integral of Absolute Error), and ITAE (Integral of Time-weighted Absolute Error), all of which were substantially higher for the PID controller. Regardless of changing conditions, MPC adeptly tracks the setpoint and optimizes the variable to enhance production efficiency.

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