Master of Science / Department of Architectural Engineering and Construction Science / Julia A. Keen / Health care facilities are responsible for treating highly infected and contagious patients at the same time as patients who are most susceptible to disease. Therefore, it is important that every available technology and application to be strategically applied to protect each and every occupant. In particular, ultraviolet germicidal irradiation (UVGI) technologies are being used in today's industry as infection control devices, primarily in health care facilities. This paper addresses the effectiveness and economic impact of applying UVGI to remove harmful airborne pathogens and outlines background information on infectious airborne pathogens such as viruses, bacteria, and fungi. Besides UVGI, other engineering control methods covered in this paper include mechanical ventilation and air distribution, filtration, and differential pressure control. Consequently, an economic evaluation of a diagnostic and treatment area was created to compare UVGI technologies and other control methods. The evaluation consists of a baseline system designed to meet code requirements; an upper-room UVGI system; a heating, ventilating, and air-conditioning (HVAC) system with an increased air changes per hour (ACH); and a UVGI system in an AHU. First costs, energy costs, and maintenance costs were the basis of economic comparison. The predicted effectiveness of all the alternatives was held constant and the time required to achieve the desired effectiveness was determined. As a result, the upper-room UVGI system and HVAC system with an increased ACH yielded much higher comparative annual costs as well as significantly better room disinfection effectiveness. The UVGI system in the AHU resulted in a lower comparative annual cost than the baseline system with the same room disinfection effectiveness. By designing infection control systems with UVGI, HVAC engineers will be more capable and successful in providing the optimal control system to these critical facilities.

Calcium (Ca) homeostasis is the maintenance of a stable plasma Ca concentration in the human body in the presence of Ca variability in the physiological environment (e.g. by ingestion and/or excretion). For normal physiological function, the total plasma Ca concentration must be maintained within a very narrow range (2.2-2.4mM). Meeting such stringent requirements is the task of a regulatory system that employs parathyroid hormone (PTH) and calcitriol (CTL) to regulate Ca flux between the plasma and the kidneys, intestines and bones. On the other hand, plasma phosphate control is less tightly, but simultaneously, regulated via the same hormonal actions. Chronic imbalances in plasma Ca levels are associated with disorders of the regulatory organs, which cause abnormal hormonal secretion and activity. These changes in hormonal activity may lead to long-term problems, such as, osteoporosis (increased loss of bone mineral density), which arises from primary hyperparathyroidism (PHPT) – hyper secretion of PTH. Existing in silico models of Ca homeostasis in humans are often cast in the form of a single monolithic system of differential equations and are not easily amenable to the sort of tractable quantitative analysis from which one can acquire useful fundamental insight. In this research, the regulatory systems of plasma Ca and plasma phosphate are represented as an engineering control system where the physiological sub-processes are mapped onto corresponding block components (sensor, controller, actuator and process) and underlying mechanisms are represented by differential equations. Following validation of the overall model, Ca-related pathologies are successfully simulated through induced defects in the control system components. A systematic approach is used to differentiate PHPT from other diseases with similar pathophysiologies based on the unique hormone/ion responses to short-term Ca disturbance in each pathology model. Additionally, based on the changes in intrinsic parameters associated with PTG behavior, the extent of PHPT progression can be predicted and the enlarged gland size estimated a priori. Finally, process systems engineering methods are used to explore therapeutic intervention in two Ca-related pathologies: Primary (PHPT) and Secondary (SHPT) Hyperparathyroidism. Through parametric sensitivity analysis and parameter space exploration, the calcium-sensing receptor (sensor) is identified as a target site in both diseases and the extent of potential improvement is determined across the spectrum of severity of PHPT. The findings are validated against existing drug therapy, leading to a method of predicting drug dosage for a given stage of PHPT. Model Predictive Control is used in drug therapy in SHPT to customize the drug dosage for individual patients given the desired PTH outcome, and drug administration constraints. / Ph. D.

This thesis is concerned with large-scale systems of Ordinary Differential Equations that model Biomolecular Reaction Networks (BRNs) in Systems and Synthetic Biology. It addresses the strategies of model reduction and decomposition used to overcome the challenges posed by the high dimension and stiffness typical of these models. A number of developments of these strategies are identified, and their implementation on various BRN models is demonstrated. The goal of model reduction is to construct a simplified ODE system to closely approximate a large-scale system. The error estimation problem seeks to quantify the approximation error; this is an example of the trajectory comparison problem. The first part of this thesis applies semi-definite programming (SDP) and dissipativity theory to this problem, producing a single a priori upper bound on the difference between two models in the presence of parameter uncertainty and for a range of initial conditions, for which exhaustive simulation is impractical. The second part of this thesis is concerned with the BRN decomposition problem of expressing a network as an interconnection of subnetworks. A novel framework, called layered decomposition, is introduced and compared with established modular techniques. Fundamental properties of layered decompositions are investigated, providing basic criteria for choosing an appropriate layered decomposition. Further aspects of the layering framework are considered: we illustrate the relationship between decomposition and scale separation by constructing singularly perturbed BRN models using layered decomposition; and we reveal the inter-layer signal propagation structure by decomposing the steady state response to parametric perturbations. Finally, we consider the large-scale SDP problem, where large scale SDP techniques fail to certify a system’s dissipativity. We describe the framework of Structured Storage Functions (SSF), defined where systems admit a cascaded decomposition, and demonstrate a significant resulting speed-up of large-scale dissipativity problems, with applications to the trajectory comparison technique discussed above.

To meet the continuously increasing goals in vehicle fuel efficiency, a number of measures are taken in automotive powertrain engineering, such as the combination of electric drives and conventional combustion engines in hybrid vehicles or the increase in gear ratios. This development leads to more complex powertrain systems, such as automatic transmissions. At the same time, the need for complex control systems is increased to achieve this desired functionality. Automatic transmissions are controlled by an electro-hydraulic control unit that governs all operations such as gear shifting and starting. Since most of the control software is designed in the form of open-loop control, most of the operations have to be calibrated manually. Thus, there exists a large number of calibration parameters in the control software that have to be tuned individually for each combination of engine, transmission and vehicle model. This process is therefore time-consuming and costly. Hence, it would be advantageous to reduce the need for calibration and in the end shorten the development process for automatic transmissions by reducing software complexity while maintaining functionality and performance. The goal of this thesis is to replace parts of the control software responsible for conducting the gearshifts that require extensive tuning by implementing control systems that have no need for calibration: adaptive high-gain λ-tracking controllers. In order to obtain the control parameters, i.e., the feedback gains, without calibration, an adaption law is implemented that continuously computes these parameters during operation of the controller. Thus, calibration is no longer needed. Since the system has to be high-gain-stabilizable, an extensive system analysis is conducted to determine whether an adaptive λ-tracking controller can be implemented. A nonlinear model of the clutch system dynamics is formulated and investigated. As a result, high-gain stability is proven for the system class and validated in simulation. Following the stability analysis, the devised adaptive controller is implemented into the control software running on the series production transmission control unit. Extensive simulations with a comprehensive vehicle model running the extended transmission software are conducted to design and to test the adaptive controllers and their underlying parameters during transmission operation in order to evaluate the control performance. The control software containing the adaptive controller is then implemented in two distinct vehicles with different automatic transmissions equipped with series production control hardware for the purpose of hardware experiments and validation. The resulting reduction of calibration efforts is discussed.

The demand for automated decision making, learning and inference in uncertain, risk sensitive and dynamically changing situations presents a challenge: to design computational approaches that promise to be widely deployable and flexible to adapt on the one hand, while offering reliable guarantees on safety on the other. The tension between these desiderata has created a gap that, in spite of intensive research and contributions made from a wide range of communities, remains to be filled. This represents an intriguing challenge that provided motivation for much of the work presented in this thesis. With these desiderata in mind, this thesis makes a number of contributions towards the development of algorithms for automated decision-making and inference under uncertainty. To facilitate inference over unobserved effects of actions, we develop machine learning approaches that are suitable for the construction of models over dynamical laws that provide uncertainty bounds around their predictions. As an example application for conservative decision-making, we apply our learning and inference methods to control in uncertain dynamical systems. Owing to the uncertainty bounds, we can derive performance guarantees of the resulting learning-based controllers. Furthermore, our simulations demonstrate that the resulting decision-making algorithms are effective in learning and controlling under uncertain dynamics and can outperform alternative methods. Another set of contributions is made in multi-agent decision-making which we cast in the general framework of optimisation with interaction constraints. The constraints necessitate coordination, for which we develop several methods. As a particularly challenging application domain, our exposition focusses on collision avoidance. Here we consider coordination both in discrete-time and continuous-time dynamical systems. In the continuous-time case, inference is required to ensure that decisions are made that avoid collisions with adjustably high certainty even when computation is inevitably finite. In both discrete-time and finite-time settings, we introduce conservative decision-making. That is, even with finite computation, a coordination outcome is guaranteed to satisfy collision-avoidance constraints with adjustably high confidence relative to the current uncertain model. Our methods are illustrated in simulations in the context of collision avoidance in graphs, multi-commodity flow problems, distributed stochastic model-predictive control, as well as in collision-prediction and avoidance in stochastic differential systems. Finally, we provide an example of how to combine some of our different methods into a multi-agent predictive controller that coordinates learning agents with uncertain beliefs over their dynamics. Utilising the guarantees established for our learning algorithms, the resulting mechanism can provide collision avoidance guarantees relative to the a posteriori epistemic beliefs over the agents' dynamics.