Doctor of Philosophy, The Ohio State University, 2017, Civil Engineering

Natural disasters are one of the constant challenges for designing new and strengthening existing infrastructures. Such hazards in the past have incurred significant loss of life and economic damage; therefore, further research is warranted in this area to enhance the health and minimize the cost of maintaining and upgrading infrastructures, improve residents' comfort, and enable achieving higher levels of life safety. To this end, the field of hazard mitigation and control focuses on performance improvement, safety, and cost effectiveness of structures mostly through minimizing large deformations of seismic-excited structures and suppressing the damage and collapse in dynamic systems due to excessive vibrations. Past developments in active and semi-active control designs, such as the commonly used state space controllers (e.g. linear quadratic regulator for fully observed systems and linear quadratic Gaussian for partially observed systems), consider linear feedback strategies. Meanwhile, such control strategies require linearization, and the system is usually linearized based on linear elastic properties. The control force is proportional to the state space vector and the dynamics and constraints of control devices are mainly ignored. The objective functions have restrictive forms, and are solely dependent on a second order convex function of the response variables. To overcome the aforementioned shortcomings, this dissertation develops new stochastic control algorithms for active and semi-active control strategies. This research concentrates on the development of frameworks that incorporate nonlinearity of the system, uncertainty of the excitation, and constraints and dynamics of the control device. Control designs are developed based on different objective functions such as higher order polynomials of response variables, reliability of the structure, and life cycle cost of the system considering hazard risks in seismic prone areas. In particular, a nonlinear (open full item for complete abstract)

Committee: Abdollah Shafieezadeh Dr. (Advisor); Natassian Brenkus Dr. (Committee Member); Halil Sezen Dr. (Committee Member); Wei Zhang Dr. (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, Case Western Reserve University, 2024, EMC - Mechanical Engineering

In robot control, classical stability is formed around a stable point (attractor) or connected stable points (limit cycles). In contrast, connected saddles can be used to describe stable sequences of states. The connection between two saddles in phase space is a heteroclinic channel, and stable heteroclinic channels (SHCs) can be combined to form cycles and networks – stable heteroclinic networks (SHNs). While the stability and subperiod at each saddle have been mathematically predicted, the potential of SHCs as robot controllers has not been fully realized. To move from modelling to control, tools are needed to more precisely design and manipulate these systems. First, this manuscript expands the SHC-framework with a task space transformation inspired by a popular robot control framework – dynamic movement primitives (DMPs). Stable heteroclinic channel-based movement primitives (SMPs) have an intuitive visualization feature that allows users to easily initialize the controller using only the robot's desired trajectory in its task space. After applying SMPs to a simple robotic system, we characterize the SHC system variables in the larger SMP system, and use the SMP variable nu – the saddle value – for local, real-time controller tuning without compromising the overall stability of the system. Finally, we explore more complex, branching connected-saddle topologies as stable heteroclinic networks. SHCs and SHNs are stochastic systems where noisy external input, such as sensory input, can be used as the stochastic component of the system. For robots, we can use SHNs as a decision-making model where the external input directly drives which decision is made. Overall, this manuscript seeks to parametrize the saddle network frameworks SHCs and SHNs for user-friendly, robust, and versatile robot control. Networks of saddles exist as models for neural activity, neuromechanical models, and robot control, and they can provide further utility in the study and application of (open full item for complete abstract)

Committee: Kathryn Daltorio (Advisor); Roger Quinn (Committee Member); Hillel Chiel (Committee Member); Murat Cenk Cavusoglu (Committee Member) Subjects: Mechanical Engineering; Robotics

Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

With the rapid advances in device technology and computational resources, the performance of communication and computing systems achieved a massive breakthrough recently. Proportional to these improvements, new services developed on these systems require substantially higher resource consumption, such as energy and time. Consequently, optimal control of these systems under stringent resource constraints has become prevalent. Furthermore, as a result of uncertainty and data scarcity in these systems, learning methods with online exploration are particularly required for optimal performance. In this dissertation, we investigate optimal stochastic control of communication and computing systems from a learning theory perspective, and develop data-efficient and robust learning algorithms. Toward this goal, we start by studying adaptive rate selection in multi-channel wireless networks for serving randomly arriving traffic with deadline constraints. Here, the controller might increase the communication rate for expedited transmission, but this comes at the expense of increased operational costs (e.g., energy consumption). Taking the packet deadlines, channel statistics and operational costs into account, we characterize the optimal transmission rate, and propose a learning algorithm that uses bandit feedback and converges to the optimal transmission rate with order-optimal regret in the absence of any prior statistical knowledge. Next, we focus on budget-constrained bandit problem in a stochastic setting. In many fundamental applications, such as adaptive routing and task scheduling, each decision depletes a random and potentially heavy-tailed cost (e.g., completion time or energy) from a common budget, and yields a random reward at the end. The cost and reward are unknown at the time of a decision, and received by bandit feedback upon completion. The controller aims to maximize the expected total reward under the budget constraints. For this bandit problem, we prop (open full item for complete abstract)

Committee: Atilla Eryilmaz (Advisor); Ness B. Shroff (Committee Member); Abhishek Gupta (Committee Member); Yu Su (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering

The power system is provisioned to evolve into a smart grid that is greener, safer, and more efficient. DC microgrid, a new form of distribution system and an emerging electrical network on ships/airplanes/electronic devices, has risen to prominence as an important building block of the future grid and many other applications. With proper operation and coordination, DC microgrids can exploit the flexibility in generation as well as consumption units, which have been standing unresponsive for decades, to approach a more robust and efficient grid such that every component in a power grid can reach its full potential to vibrantly participate in grid services. This dissertation presents systematic approaches to solve DC microgrid control and optimization problems that are usually marked by challenges like uncertainty, nonlinearity, tractability, and structural constraint issues. First, it is well known that when a DC microgrid is operated in island mode, the stability critical power balance is shadowed by uncertain and volatile generation and consumption. We propose a robust stability framework containing a set of sufficient conditions to provide provable stability guarantee for such systems. We then further investigate into robust control design to improve the performance of the system. In view of the physical communication structures that commonly exist in a microgrid, decentralized/distributed controllers are recognized to be more applicable in practice for their limited reliance on information transmissions. Nevertheless, with the communication structural constraints, the decentralized/distributed control design problem is NP-hard in general, and an ill-designed controller may as well render an originally operative system unstable. We propose an algorithm to design a structurally constrained controller in such a way that it can guarantee a design direction with provable improving performance. Second, for DC microgrids that are in grid-connected mode, the (open full item for complete abstract)

Committee: Wei Zhang (Advisor); Giorgio Rizzoni (Advisor); Antonio Conejo (Committee Member); Mahesh Illindala (Committee Member); Andrej Rotter (Other) Subjects: Electrical Engineering; Energy; Engineering; Operations Research

PhD, University of Cincinnati, 2005, Engineering : Electrical Engineering

Within this dissertation, the problem of the control of the decentralized path planning decision processes of multiple cooperating autonomous aerial vehicles engaged in search of an uncertain environment is considered. The environment is modeled in a probabilistic fashion, such that both a priori and dynamic information about it can be incorporated. The components of the environment include both target information and threat information. Using the information about the environment, a computationally feasible decision process is formulated that can decide, in a near optimal fashion, which path a searching vehicle should take, using a dynamic programming algorithm with a limited look ahead horizon, with the possibility to extend the horizon using Approximate Dynamic Programming. A planning vehicle must take into account the effects of its (local) actions on meeting global goals. This is accomplished using a passive and predictive cooperation scheme among the vehicles. Lastly, a flexible simulator has been developed, using sound simulation analysis methods, to simulate a UAV search team, which can be used to create statistically valid results demonstrating the effectiveness of the model and solution methods.

Committee: Dr. Emmanuel Fernandez (Advisor) Subjects:

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science in Electrical Engineering (MSEE), Wright State University, 2023, Electrical Engineering

Stochastic resonance (SR) is a phenomenon that can enhance the detection or transmission of weak signals by adding random noise to a non-linear system. SR introduced into the human motor control system as a subthreshold mechanical vibration has shown promise to improve sensitivity to haptic feedback. SR can be valuable in a laparoscopic surgery application, where haptic feedback is critical. This research sought to find if applying SR to the human motor control system improves performance in a laparoscopic probing task, if the performance differs based on the location of stochastic resonance application, and if there are sustained effects from SR after its removal. Subjects were asked to perform a palpation task using a laparoscopic probe to determine whether a series of simulated tissue samples contained a tumor. Subjects in the treatment groups were presented with a series of samples under the following conditions: Pre-SR, SR applied to the forearm or elbow, and Post-SR. Subjects in the control group did not have SR applied at any point. Performance was measured through the accuracy of tissue assessment, subjects' confidence in their assessment, and assessment time. Data from 27 subjects were analyzed to investigate the application of stochastic resonance and its sustained effects to improve haptic feedback sensitivity in a simulated laparoscopic task. The forearm group was shown to have significant improvement in the accuracy of tissue identification and sensitivity to haptic feedback with the application of SR. Additionally, the forearm group showed a greater improvement in accuracy and sensitivity than the elbow group. Finally, after SR was removed, the forearm group showed sustained significant improvement in accuracy and sensitivity. Therefore, the experiment results supported the hypotheses that stochastic resonance improves subjects' performance and haptic perception, that performance improvement differs based on application location, and that subjec (open full item for complete abstract)

Committee: Luther Palmer III, Ph.D. (Advisor); Caroline Cao Ph.D. (Committee Member); Katherine Lin M.D. (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Engineering; Health; Health Care; Mechanical Engineering; Surgery

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

Optimization of dynamic engineering systems requires an integrated approach that accounts for the coupling between embodiment design and control system design, simultaneously. Generally known as combined design and control optimization (co-design), these methods offer superior system performance and reduced costs. Despite the widespread use of co-design techniques in the literature, extremely limited research has been done to address the issue of uncertainty in co-design problem formulations. This is problematic as all engineering models contain some level of uncertainty that might negatively affect the system's performance, if overlooked. Accounting for these uncertainties transforms the deterministic problem into a stochastic one, requiring the use of appropriate stochastic optimization approaches. Therefore, this dissertation serves as the starting point for research on stochastic co-design problems when the uncertainty is propagated into the system from random design decision variables and/or problem parameters. Specifically, a simultaneous co-design formulation within multidisciplinary dynamic system design optimization (MDSDO), along with a special class of direct methods, known as direct transcription (DT), are consistently used throughout this research as the basis for uncertainty considerations. Using techniques from robust design optimization (RDO), we develop a novel stochastic co-design formulation within MDSDO, known as robust MDSDO (R-MDSDO). This formulation enables a protective measure against uncertainties by minimizing the sensitivity of the objective function to variations in design decision variables and fixed problem parameters. The R-MDSDO formulation is applied to two case studies to assess its effectiveness and implementation challenges. A more rigorous evaluation of probabilistic constraints is required to ensure reliability. In this dissertation, we develop a novel stochastic co-design approach based on the principles of RBDO. We im (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Kelly Cohen Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2016, Industrial and Systems Engineering

Electric vehicles hold great promise in improving transportation energy consumption efficiency. However, battery-related "range-anxiety" hampers large-scale EV adoption. To relieve range anxiety and accelerate wider EV adoption, two issues should be addressed. The first is how to develop and optimize the layout of a network of public EV charging stations to keep pace with increasing EV charging demand. Second, the strains of added EV charging loads on the electricity distribution infrastructure should be managed. This dissertation will address these two issues through new optimization models and simulations. Specifically, we formulate a stochastic flow-capturing charging station location model to solve the first issue and obtain an optimal charging station network layout. For the second issue, stochastic control models are created to schedule EV charging loads. This model assumes some flexibility to defer EV charging demands, to minimize distribution infrastructure investment and degradation costs. We expand this model to allow the charging station to participate ancillary service markets, either by modulating EV charging or by using distributed resources. The outcome of this dissertation will help EV infrastructure planners develop an adequate charging station network and facilitate wider EV adoption. It will also help system operators and energy planners better understand the challenges of vehicle-grid integration and prepare appropriate strategies to address these issues.

Committee: Ramteen Sioshansi (Advisor); Guzin Bayraksan (Committee Member); Lixin Ye (Committee Member) Subjects: Energy; Operations Research

Master of Sciences, Case Western Reserve University, 2016, EECS - Electrical Engineering

The networked microgrids, or microgrid community (MGC) have been recognized as a promising future electrical infrastructure, for its operational and economic benefits along with unique control and challenges. Built as a practical example of IEEE Standard 1547.4, the MGC in this study contains three microgrids interconnected through the medium voltage (MV) feeder system. To study the static and dynamic performance of the system, a detailed electromagnetic model was constructed in PSCAD software. Typical scenarios are simulated in both grid connected and islanded modes to demonstrate the operational feasibility of MGC in responding to secondary control signals, and the effectiveness of primary controls in facilitating energy transaction and mitigating fluctuations in voltage and frequency during network disturbances. Also to study the energy management of the MGC, a two-stage stochastic mixed integer programming model was built in the AIMMS modeling environment. Optimal operational decisions are solved with uncertainties in load, renewable generation and bulk grid electricity price.

Committee: Mingguo Hong (Advisor); Kenneth Loparo (Committee Member); Marija Prica (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy (PhD), Wright State University, 2013, Environmental Sciences PhD

Time series that exhibit multiple scaling properties in the frequency domain are common in natural systems (e.g., temperature through geologic time). NOAA verified hourly water level data ranging from 20 to 30 years in duration for nine stations in the North American Great Lakes is converted to the frequency domain using a complex discrete fast Fourier transform (FFT) and then expressed as a power spectrum in terms of frequency versus power. To quantify power law scaling behavior, a scaling exponent (β) is determined by fitting a power function to a log-log plot of frequency (f ) or period (T) versus power in the frequency domain. For water level fluctuations in the Great Lakes, the frequency domain exhibits four distinct regions of power law scaling. The mathematical relationship of the scaling exponent (β) to 1/f time series behavior is examined employing Bode analysis. Variations in scaling behavior of water level data, indicated by the patterns of change in amplitude and phase across frequencies, can be expressed through transfer functions. The transfer functions are created using Laplace transforms. Each Laplace term (s) has a fractional exponent based on the scaling exponent (β) derived from the Bode magnitude plot. Convolution of the transfer function in the time domain is equivalent to multiplication in the frequency domain (Laplace space). Combining the transfer functions for all frequencies yields a Frequency Response Model and provides a basis to determine how the system that created the time series will respond to any given input over all frequencies. For water level fluctuations in the Great Lakes, the scaling behavior pattern is well approximated by a combination of four linear differential equations or transfer functions, one primary equation for each distinct scaling region. The collective interactions of all equations over all frequencies create the Great Lakes Frequency Response Model and represent the underlying physical dynamics of the Great La (open full item for complete abstract)

Committee: Sarah Tebbens Ph.D. (Advisor); Christopher Barton Ph.D. (Committee Member); John Flach Ph.D. (Committee Member); Paul Seybold Ph.D. (Committee Member); Brian Tsou Ph.D. (Committee Member) Subjects: Applied Mathematics; Environmental Science; Geophysical; Geophysics; Hydrologic Sciences; Mathematics; Systems Design; Systems Science; Water Resource Management

PhD, University of Cincinnati, 2007, Engineering : Electrical Engineering

Biological pathways provide a comprehensive view of a biological phenomenon, in the form of a network of inter-related reactions or processes. Modeling the biochemical reactions helps in studying and analyzing a biological pathway. This is done through parameter extraction and development of mathematical models of the biological systems. The importance of such modeling lies in the ability to easily perform mathematical mutations and optimizations to achieve a specific result, which can then be duplicated in the laboratory. The ability to control the outputs of biological reactions increases the possibilities for new applications, such as developing crops resistant to infection and bio-engineering drugs for diseases like Hepatitis and HIV-AIDS. Studying random mutations through practical experimentation is time consuming and expensive. Mathematical modeling definitely provides an affordable and convenient virtual experimental platform. However current methods are limited, as they produce results that may be difficult to be reproduced by biologists. The typical results do not address the practical constraints and feasibilities of the proposed mathematical mutation. Hence, there is a definite need for efficient algorithms and software, which not only help study the effect of mutations in a mathematical setting, but also provide practical methods to control biological pathways in a laboratory setting. In this dissertation, we develop an algorithm named Box which addresses this issue. The Box algorithm encompasses all the steps needed, from modeling a pathway to producing the biological controls needed to achieve desired mutations. The Box algorithm can be explained in terms of six logical steps: bio-modeling development language, bio-control database integration, sensitivity analysis, bio-rules formation, output optimization and comparison. The first step, the bio-modeling development language BMDL, is a new type of representation for a biological model. It is a weighte (open full item for complete abstract)

Committee: Dr. Carla Purdy (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2008, Statistics

Computer simulations have become increasingly popular as a method for studying physical processes that are difficult to study directly. These simulations are based on complex mathematical models that are believed to accurately describe the physical process. We consider the situation where these simulations take a long time to run (several hours or days) and hence can only be conducted a limited number of times. As a result, the inputs (design) at which to run the simulations must be chosen carefully. For the purpose of fitting a response surface to the output from these simulations, a variety of designs based on a fixed number of runs have been proposed.In this thesis, we consider sequential adaptive designs as an “efficient” alternative to fixed-point designs. We propose new adaptive design criteria based on a cross validation approach and on an expected improvement criterion, the latter inspired by a criterion originally proposed for global optimization. We compare these new designs with others in the literature in an empirical study and they shown to perform well. The issue of robustness for the proposed sequential adaptive designs is also addressed in this thesis. While we find that sequential adaptive designs are potentially more effective and efficient than fixed-point designs, issues such as numerical instability do arise. We address these concerns and also propose a diagnostic tool based on cross validation prediction error to improve the performance of sequential designs. We are also interested in the design of computer experiments where there are control variables and environmental (noise) variables. We extend the implementation of the proposed sequential designs to achieve a good fit of the unknown integrated response surface (i.e., the averaged response surface taken over the distributions of the environmental variables) using output from the simulations. The goal is to find an optimal choice of the control variables while taking into account the distr (open full item for complete abstract)

Committee: WILLIAM NOTZ PhD (Advisor); THOMAS SANTNER PhD (Committee Member); ANGELA DEAN PhD (Committee Member) Subjects: Statistics

Doctor of Philosophy, Case Western Reserve University, 2005, Electrical Engineering

MultiResolution Analysis (MRA) is a common phenomenon of human intelligence. The basic procedure of MRA is that a series of analyses are carried out on an object's representations at different, progressively increasing resolution levels and analysis results at lower resolution levels act as guidance to analyses at higher resolution levels. Many methods such as coarse-fine template matching, reduced model in optimization, coarse-fine path-finding and so on can be seen as implementations of principles of MRA. This dissertation reports on investigations of MRA in different areas from a unified perspective and proposes algorithms from the viewpoint of MRA for attacking machine intelligence tasks in areas such as classification, function approximation, rule learning, optimization by stochastic search, control, and so on. The focus of this dissertation is about three questions: firstly,what is multiresolution? secondly, how to obtain multiresolution respresentations of an object? and thirdly, how to utilize results attained at low resolution levels as guidance for analyses at high resolution levels? Two new concepts, resolution and scale of a cluster, are introduced in this dissertation, and based on these concepts clustering algorithms are developed for obtaining multiresolution representations of an object. Using this uniform approach to attaining multiresolution representations, implementations of MRA are discussed and illustrated with examples for various machine intelligence tasks.

Committee: Yoh-Han Pao (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 1990, Engineering (Undesignated)

Closed loop control of linear stochastic control system with unknown parameters is studied by using a dual control approach. For control problems with either imperfect state observations or parameter uncertainties, the control signal to the system can actively influence the knowledge about the state of the system or about the parameters of the system. The dual controller is designed to regulate the state or output of the system to reach a certain goal and actively probe the system to obtain more information about the unknown system parameters. At each stage, the cost functional associated with the system objective is decomposed into a certainty equivalence cost and a dual cost. The active learning feature of the (dual) control explicitly accounts for the reduction of the dual cost, which measures the uncertainty of the system. An approximation technique is used to obtain a recursive closed form solution of the equations of functional dynamic programming. The dual control algorithm developed reduces the overall cost. Moreover, the algorithm is shown to be the best affine dual controller and it is very simple to implement. In certain situations, using an appropriate dual cost approximation, the computational overhead for the algorithm is minimum, and real-time control applications are practical.

Committee: Kenneth Loparo (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2012, Mechanical and Systems Engineering (Engineering and Technology)

Cellular manufacturing has been an important phenomenon in manufacturing in recent decades. Tremendous amount of work has been done regarding issues such as cell formation, cell loading and job scheduling. However, majority of literature lacks consideration of uncertainty in the problem definition phase, thus methodology. In this dissertation, the impact of uncertainty of demand, processing times and capacity requirements on a cellular manufacturing system (CMS) performance are addressed and stochastic optimization approaches are developed and applied to ten case problems from industrial companies and cellular manufacturing literature. This dissertation consists of mainly three phases, namely: stochastic CMS design, stochastic CMS control and the integrated modeling and analysis of CMS design and CMS control. Capacitated cell formation under the impact of uncertain demand and processing times is defined as the stochastic CMS design problem. On the other hand, cell loading, job sequencing and manpower allocation considering probabilistic demand and processing times are the main issues addressed in the stochastic CMS control phase. Finally, the relationship between stochastic CMS design and stochastic CMS control comprises the "integration" phase. Nonlinear stochastic programming models are developed to optimize each phase and simulation models are also built to validate the results of mathematical optimization and assess manufacturing system performance. To deal with larger problems, as one of the widely used metaheuristic optimization techniques, Genetic Algorithms (GA) is utilized; a GA model is developed and compared with stochastic programming model by using simulation modeling and statistical analysis. Results indicated that stochastic programming can assist with a better decision making on CMS design and control due to its capability of capturing probabilistic nature of problems. In all cases, the proposed stochastic optimization approaches outperformed the con (open full item for complete abstract)

Committee: Gursel Suer (Advisor); Dale Masel (Committee Member); Diana Schwerha (Committee Member); Ana L. Rosado Feger (Committee Member); M. Khurrum S. Bhutta (Committee Member) Subjects: Industrial Engineering; Management; Operations Research; Statistics; Systems Design