Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

Global regulatory targets for reducing CO2 emissions along with the customer demand is driving the automotive sector towards energy efficient transportation. Powertrain electrification offers great potential to improve the fuel economy due to the extra control flexibility compared to vehicles with a single power source. The benefits of the electrification can be significantly reduced when auxiliaries such as the vehicle climate control system directly competes with the powertrain for battery energy, reducing the range and energy efficiency. Connected and Automated Vehicles (CAVs) can increase the energy savings by allowing to switch from instantaneous optimization to predictive optimization by leveraging information from advanced navigation systems, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication. In this work, two energy optimization problems for CAVs are studied. First is to jointly optimize the vehicle and powertrain dynamics and the second is to optimize the vehicle climate control system. The focus of this work is to combine the Dynamic Programming (DP), Approximate Dynamic Programming (ADP) and perturbation theory based approaches to solve the energy optimization problems with variations in external inputs and parameters that affects the plant model, objective function or constraints. To this end, mathematical methods are used to develop two novel algorithms that compensates for mismatches between nominal and estimated parameters. The first approach develops a cost correction scheme to evaluate the sensitivity of the value function to parameters, with the ultimate goal of correcting the original optimization problem online with the observed parameters. Two case-studies are considered with variations in vehicle payload and auxiliary power load. Second, a novel algorithm for solving dynamic optimization problem is developed to apply closed-loop corrections to solution of the original optimization problem without the need to (open full item for complete abstract)

Committee: Marcello Canova (Advisor); Abhishek Gupta (Committee Member); Stephanie Stockar (Committee Member) Subjects: Engineering; Mechanical Engineering

Master of Science in Engineering, University of Akron, 2016, Electrical Engineering

An observer-controller pair was designed to regulate the fuel flow rate and the flue-gas oxygen ratio of a combustion boiler. The structure of the observer was a proportional-integral state estimator. The designed controller was composed of a combination of two common controller structures: state-feedback with reference tracking and proportional-integral-derivative(PID). A discrete-time, linear state-space model of the combustion system was developed such that the linear controller and observer could be designed. This required establishing separate models pertaining to the combustion process, actuators, and sensors. The complete model of the combustion system incorporated all three models. The combustion model, which related the flue-gas oxygen ratio to the fuel and oxygen flow rates, was obtained using the mathematical formulas corresponding to combustion of natural gas. The actuators were modeled using measured fuel and oxygen flow rate data for various actuator signals, and fitting the data to a parametric model. The established nonlinear models for the combustion process and actuators required linearization about a specified operating point. The sensors model was then obtained using the predictive error identification technique based on batch input-output data. For the acquired model of the combustion system, a linear quadratic regulator was used to calculate the optimal state feedback gain. The classical controller gains were determined by tuning the gains and evaluating the simulation of the closed-loop response. Computer-aided simulations provided evidence that the controller and state estimator could regulate the desired set point in the presence of moderate disturbances. The observer-controller pair was implemented and verified on an experimental boiler system by means of an embedded system. Even in the presence of a disturbance resulting from a 50% blockage of the surface area of the air intake duct, the closed-loop system was capable of regulating t (open full item for complete abstract)

Committee: Nathan Ida Dr. (Advisor); Robert Veillette Dr. (Committee Member); Kye-Shin Lee Dr. (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science, The Ohio State University, 2015, Mechanical Engineering

The problem considered is the robustness of a system stabilized by a discrete controller under state feedback with time delay. The ability to guarantee certainty of stability when the system is uncertain falls in the domain of robust control. Historically, robustness bounds have been formulated for net uncertainty ranges of the resulting overall system. These robustness bounds are state dependent and change with selection of states of the system. Recent new work has reformulated bounds in terms of uncertainty in the plant which originates system uncertainty not only by itself but also by affecting the state feedback and sampling as well. Results have been presented which demonstrate state selection variance for the case of plant uncertainty in order to possibly obtain better robustness bounds. Methods to implement such change of states and conditions for favorable state transformation are discussed. The methods are applied to cases studied in the past to document superior bounds. Finally, results are presented for state transform in the case of disturbances that has a known form based on previous work on perturbations with known structure.

Committee: Rama Yedavalli (Advisor); Junmin Wang (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, Case Western Reserve University, 2025, Biomedical Engineering

This dissertation focuses on non-perceptual effects of artificial sensation measured by effects in the motor system. Tactile feedback is used throughout the brain, from the “highest” cortical level to the “lower” spinal or brain stem level. Touch is first used before perception, or pre-perceptually, by the brain stem in simple, automatic modulation of the motor system. For example, carrying an object from place to place or even shifting it in one's hand involves many changing tactile signals. Even a single ridge of a fingertip supplies a unique signal for use in object manipulation. If one had to actively perceive and act upon all this information, merely picking up an object would become overwhelming. Fortunately, the lower levels of our brain automatically make minor adjustments to grip based on tactile information. What is not known is how relevant perceptual qualities are to these automatic corrections to grip. The cortex, not the brainstem, is the location of tactile perception, so it stands to reason that the brainstem does not require “natural” qualities of tactile feedback. Our lab has a group of participants with peripheral nerve cuff electrodes we can stimulation through. We tested how well artificial tactile feedback would integrate with the sensorimotor system in tasks of increasing complexity. We found that peripheral nerve stimulation is processed similarly to naturally generated touch with and without perception and may engage with the motor system as seen by the intent to modulate grip force.

Committee: Dustin Tyler (Advisor); A Bolu Ajiboye (Committee Chair); Hillel Chiel (Committee Member); M. Cenk Çavuşoğlu (Committee Member) Subjects: Biomedical Engineering; Engineering; Neurosciences

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Sociotechnical systems in safety-critical domains are distributed and contain interdependencies between the different elements, including human and automated roles that need to coordinate and synchronize their activities with dynamic events in the environment. The advancement of technology and the introduction of machines capable of acting at a higher level of autonomy has increased the complexity of such Distributed Work Systems (DWSs). An envisioned DWS is described by a set of static paper-based documents and will be deployed in the next few years. The short-range low-altitude air mobility system is one very good example of an envisioned DWS. Interactions between human and automated roles and their environment are dynamic, evolve, and change over time, causing emergent effects like taskload peaks and coordination breakdowns. A well-designed DWS will be able to keep pace with the work environment dynamics (like the dynamics of aircraft governed by laws of flight in a short-range low-altitude air mobility system) and succeed in responding to the disturbance. This creates the need to understand the dynamics of envisioned DWS, such as how a DWS performs in high-paced situations like anomaly response. Assessing the feasibility and robustness of an envisioned DWS comes with challenges: the physical system does not yet exist, its design and operations are often underspecified, and multiple versions may exist within a designer community about what future operations will look like. Therefore, as a part of this dissertation, an exploratory early-stage computational modeling and simulation technique is described and demonstrated to evaluate an envisioned DWS. Using functional modeling and computational simulation capabilities, the dissertation shows a technique that can help evaluate envisioned DWS by discovering things that are not uncovered by traditional normative simulations. The primary advantage of the technique is the ability to evaluate the dynamics of work in (open full item for complete abstract)

Committee: Martijn Ijtsma (Advisor); Michael Rayo (Committee Member); David Woods (Committee Member) Subjects: Industrial Engineering; Systems Design

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

Master of Science, Miami University, 2024, Mechanical and Manufacturing Engineering

The demand for haptics in large touchscreen displays (TSDs) has rapidly grown in recent years. However, due to limitations of existing haptic actuators used within mobile devices the technology to implement haptic feedback in large TSDs is immature. To address challenges for generating haptic feedback in large touch screen displays, this study proposes a haptic module based on a “smart” fluid called Electro-Rheological (ER) fluid for use in large TSD applications. An ER haptic module is presented which utilizes ER fluid to provide a means to control vibrations transmitted through the base of the module to an output display mass felt by a user modeling a large TSD. The module is experimentally evaluated to understand its dynamic behavior and the influence of the ER fluid on haptic feedback. Two variables into the ER fluid are controlled: input voltage and frequency. Findings show that the input voltage into the ER fluid directly corresponds to the magnitude of the acceleration response transmitted through the module, with as high as an 88% increase in amplitude observed. Frequency input is shown to affect the shape of the resulting waveform, with various waves with different frequencies present based on the input frequency.

Committee: Jeong-Hoi Koo (Advisor); Jinjuan She (Committee Member); Sk Hasan (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

Autonomous vehicles are a hot topic in control theory and are utilised in different fields such as industries, aerospace and robotics. Trajectory-tracking is one of the crucial features of autonomous vehicles which involves two major steps: generating a reference trajectory and tracking it. In many cases, the reference trajectory is generated by using an objective function where we want vehicles to move towards the extremum (maximum/minimum) of the objective function which is a terminal position of the trajectory. We performed some simulations and experiments using traditional controllers including the Proportional-Derivative Controller (PDC), Model-Predictive Controller (MPC), and Pure Pursuit Controller (PPC). These controllers while showing some degree of desirable vs. undesirable behaviour, they are model-based controllers that require a mathematical expression of an objective function. Additionally, they show difficulties and they have other limitations. In this thesis, we make a case for the utilization of extremum-seeking control (ESC) systems which are model-free, real-time, adaptive systems. We revisit some works that have been done regarding the classic ESC (C-ESC) structure. The primary part of this thesis is where we provide the simulations and experimental works using control-affine ESC (CA-ESC) systems which have been used rarely in experimental environments in literature. Particularly, we utilized single-integrator and unicycle dynamics CA-ESC structures and conducted simulations and experiments. Additionally, we propose a novel amended CA-ESC design (for both single-integrator and unicycle dynamics) by adopting some developments that took place in this area in recent years. Our proposed design consists of a Geometric-Based Extended Kalman Filter (GEKF) for gradient estimation and adaptation law for attenuation oscillations and better convergence rate. We performed simulations to show the effectiveness of our proposed design.

Committee: Sameh Eisa Ph.D. (Committee Chair); Shaaban Abdallah Ph.D. (Committee Member); Abhinav Sinha Ph.D. (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2024, Engineering and Applied Science: Civil Engineering

Buildings are the largest energy consumers, contributing to more than 40% of global carbon dioxide (CO2) emissions [1]. A significant portion of this energy consumption is attributed to building mechanical systems, particularly air handling units. Air handling units are a crucial component responsible for distributing conditioned air throughout the building. The supply and return fans within these units play a key role in air circulation and are responsible for substantial energy usage. This paper investigates strategies to decrease the energy burden on these fans. An evaluation of existing economizer damper control measures highlighted a dire need for a novel approach to modulate outdoor, exhaust, and return air dampers. The “split-signal damper control” suggested by Nassif and Moujaes [2] showed promising results, even though it required improvements for effective implementation in building mechanical systems. Further investigations introduced a method known as “duct static pressure set point reset”, which involves dynamically adjusting duct static pressure according to space airflow requirements rather than maintaining a constant pressure set point [3]. This research aims to improve the economizer damper control sequence for implementation in variable air volume (VAV) systems, develop a statistical model to simulate energy savings and refine the split-signal damper control sequence by integrating demand control ventilation (DCV). Additionally, cumulate energy savings and cost reductions due to duct static pressure set point adjustments, and improved economizer damper control sequence to attract building owners and operation managers. Experimental tests conducted on chilled water VAV system yielded an energy savings of 0.2% to 5% on improved split-signal damper control compared to the traditional three-coupled damper control method. Additionally, the control sequence could prevent reverse airflow through the exhaust damper. The statistic (open full item for complete abstract)

Committee: Nabil Nassif Ph.D. (Committee Chair); Tianren Wu Ph.D. (Committee Member); Arpan Guha Ph.D. (Committee Member) Subjects: Civil Engineering

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Aerospace Engineering

Conventional control approaches have been developed based on mathematical models of systems that contain multiple user-defined parameters, and it is time-consuming to determine such parameters. With advancements in computing power, artificial intelligence (AI) has been recently used to control autonomous systems. However, it is difficult for engineers to understand how the resulting output is obtained because most AI techniques are a black box without defining a mathematical model. On the other hand, a fuzzy inference system (FIS) is a preferable option because of its explainability. By adding learning capability to the FIS using a genetic algorithm (GA), the FIS can provide a near-optimal solution, which is known as a genetic fuzzy system (GFS). To exploit the advantages of the GFS, this work develops the FIS-based control approaches for diverse autonomous platforms, which include aerial, ground, and space platforms. For aerial platforms, this work develops a FIS-applied collision avoidance (CA) algorithm that can provide a near-optimal solution in terms of the travel distance of unmanned aerial vehicles (UAVs). After introducing a compact form of equations, which reduces the number of unknown parameters from 6 to 2, based on the enhanced potential field (EPF) approach, the proposed FIS models determine two unknowns, which are the magnitude of the avoidance maneuvers. The proposed models are trained to overcome the drawbacks of the artificial potential field (APF) while minimizing the travel distance of the UAVs, the trained FIS models are tested in a complex environment in the presence of multiple static and dynamic obstacles by increasing the number of UAVs in a given area. Numerical simulation results are presented for the training and testing results, including the comparison with the EPF. For ground platforms, this work proposes a decentralized multi-robot system (MRS) control approach to perform a collaborative object transportation with a near- (open full item for complete abstract)

Committee: Donghoon Kim Ph.D. (Committee Chair); Anoop Sathyan Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2023, Engineering and Applied Science: Mechanical Engineering

Multi-robot systems are a promising solution for object transportation as they have huge advantages over single-purposed robots - they are more versatile, less specialized, and more resilient to system failure while they can be scaled in numbers to meet operating requirements. As humans continue to explore further into our universe and domestic needs continue to grow with an increasing population, more robots will be required to complete more jobs. Most importantly, this current philosophy does not consider environments without human intervention or teleoperation. In projects such as NASA Gateway where “galactic pitstops” may not have a human aboard for many months, faults or incomplete tasks would endanger any mission relying on consistent uptime. Tasks such as moving a simple object from an initial position to a target region, such as staging materials, must be completed by a reliable robotic system to save mission critical resources and time. However, when scaling numbers, multi-robot control and communication becomes complex as either monitoring technology or environmental cues must be deployed to enable coordination. In order to make the multi-robot system not only resilient, but also independent from environmental cues and hence universally deployable out-of-the-box, we propose a purely emergent interaction model based on touch between the individual mobile robots and contact with a manipulated object. This is realized with an attractor dynamics trajectory planner coined as “convergence” and a contact controller referred as “adherence” is benchmarked in a simulated non-prehensile object transportation task. The outcomes from this project include the “convergence” and “adherence” algorithms and how they behave as a coupled dynamic system, a Gazebo and Robotic Operating System (ROS) simulation, documentation and analysis of emergent behaviors from the coupled dynamic syste (open full item for complete abstract)

Committee: Tamara Lorenz Ph.D. (Committee Chair); Ali Minai Ph.D. (Committee Member); Nikita Kuznetsov Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Robotics

Master of Science, University of Toledo, 2023, Electrical Engineering

Modern industrial automation utilizes local controllers based on microcontrollers, programmable logic controllers, remote terminal units, and edge servers to form an Edge-assisted Networked Control System (EaNCS). The edge server offers high computational capacity at the cost of network delays and potential data loss over wired/wireless networks, whereas local controllers provide reliable performance. This thesis focuses on the delay analysis of the EaNCS. The network delay is analyzed with the help of Network Calculus. Computational delays in local controllers and edge servers are modeled by measurements. The network and computational delays are parameters to design guidelines that we propose, which answer the question of how many local controllers an edge server can support considering the traffic profile and network capacity in an EaNCS. The design guidelines have been experimentally verified in a physical testbed that consists of an edge server, a network switch, Raspberry Pis, and a programmable logic controller.

Committee: Richard Molyet (Committee Member); Liang Cheng (Committee Chair); William Evans (Committee Member) Subjects: Electrical Engineering

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

Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

Foster parents in the State of Ohio were explored in this dissertation study. The purpose of this research was to address a gap in existing literature concerning foster parent experiences in Ohio within the foster care system. The job of a foster parent is to provide a safe place and to make a difference in the lives of children that come into their homes. This role is oftentimes accompanied by high demands, low autonomy, and low support. It is imperative to correct this pattern as it can result in adverse psychological effects and negative impacts on mental health, leading to foster parents exiting the system in high volumes. This study used a qualitative approach to address the research question. The Job Demand-Control-Support (JDCS) model was applied to this research and used as a guide in providing a deeper analysis of the inner workings of the foster care system as it pertains to foster parents. A purposeful and snowball sample of 20 Ohio foster parents participated in open-ended Zoom and telephone interviews. They provided in-depth responses on their experiences within the foster care system. The interviews were transcribed, coded, and analyzed for major themes. ATLAS.ti Cloud software was used for coding analysis of the collected data. Four major themes and nine sub-themes resulted from the interviews. The findings contributed to research by providing future foster parents and leadership within the foster care system with meaningful strategies to improve foster parent retention rates in Ohio.

Committee: Susan Campbell (Committee Chair); John Nadalin (Committee Member); Dail Fields (Committee Member) Subjects: Business Administration; Public Administration; Public Policy; Social Research; Social Structure

MS, University of Cincinnati, 2022, Engineering and Applied Science: Electrical Engineering

Applied Machine Learning on real-time Cyber Physical Systems (CPS) brings several new challenges to Machine Learning (ML) based control. CPS are subjected to environmental changes, noise, hardware limitations and tightly coupled time constraints, which make real-time control a non-trivial task. This thesis work focus on studying applicability of ML based control in real-time CPS using physical pinball machines as sandboxes. A simulator framework to evaluate ML algorithms in a virtual setting and a real-world framework to evaluate ML algorithms in physical pinball machines are developed. Both frameworks provide visual information and extracted features for the ML agent, and actuates the system according to ML agent control signals. The real-world framework utilizes a real-time state tracker, hardware based synchronizer, and a non-invasive system actuation method to realize the abstracted framework. We discuss the development of the simulation framework and the real-world framework. Subsequently, we move into the application of model-free ML, where we experiment with reinforcement learning under different perception models and modular learning. Finally, we discuss the application of model-based ML where we experiment with Model Predictive Control (MPC) with Deep Neural Networks (DNN) and Support Vector Regression (SVR), on selected primitive goals in the system. Each technique is statistically evaluated and results are presented. The evaluation results showed that ML based MPC was able to reach up to 96% accuracy in the selected shot aiming scenario.

Committee: Zachariah Fuchs Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2022, Mechanical Engineering

The drawbar pull force of a vehicle can be characterized as its towing capacity, typically of an external implement or load. Quantitatively, drawbar pull is the effective force that automobiles, robots, and other transportation machines can apply to a load by means of an internal combustion engine, electric motor, and mechanical transmission power generation. Resources from the U.S. Army Engineers, the International Organization of Standardization, and the National Aeronautics and Space Administration have established guidelines for drawbar pull testing with heavy machinery and exploratory vehicles on non-solid drive surfaces. In this research, the listed resources as well as others were referenced for the development of additional drawbar pull test procedures for ground vehicle robots operating on solid drive surfaces. In drawbar pull testing, wheel slip is induced on the vehicle. The prevalence of safety mechanisms such as a traction control system in modern vehicles made this a practical application to incorporate into the study as well. In addition to single-robot capabilities, there may be benefits in capacity or efficiency when multiple robots are used to execute a task. When it comes to the pulling effort generated by ground vehicle robots, the scalability of the drawbar pull capacity when units are added to the system is another point of interest. In this thesis research, a novel drawbar pull force rig developed for ground robots was used to evaluate the effect of a traction control algorithm on drawbar test performance, the role that drive surface plays in drawbar test performance, and the scalability of the traction control algorithm as a multi-robot system. This thesis also discusses the measures taken to validate the functionality of the test rig, traction control algorithm, and multi-robot system. Two robot models, the Clearpath Jackal and ROBOTIS Turtlebot3 Waffle, were used to demonstrate the methodology discu (open full item for complete abstract)

Committee: Adam Schroeder (Committee Chair); Sorin Cioc (Committee Member); Brian Trease (Committee Member) Subjects: Mechanical Engineering; Robotics

PhD, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

Combined plant and control design (control co-design, or CCD) methods are generally used to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, reliability-based design optimization (RBDO) principles have been used within CCD to address the design of stochastic dynamic systems. However, since the new reliability-based CCD (RBCCD) algorithms use all-at-once (AAO) formulations of CCD, only most-probable-point (MPP) methods can be used as a reliability analysis technique. This is a limitation as the use of such methods for highly-nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue as the dynamic equality constraints would be satisfied through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any accurate reliability analysis method could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) has been proposed for RBCCD. To quantify the uncertainty propagation, an accurate reliability analysis method using generalized polynomial chaos (gPC) expansions has been proposed. The effectiveness of the RB-MDF-MDSDO formulation and the proposed reliability analysis method are established via two test problems. The performance of the gPC method relative to the current state of the art, MPP methods, is relatively unknown for RBCCD applications. Specifically, the only known information pertains to RBDO applications, where the gPC expansion method is generally known to be more accurate, but also computationally more expensive than the MPP methods. Therefore, to benchmark the performance of the gPC expansion method against MPP methods, the first-ever double-loop and single-loop MPP-based formulations of RB-MDF-MDSDO are developed, and their solution accuracy and e (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Sam Anand Ph.D. (Committee Member) Subjects: Engineering

Master of Science, The Ohio State University, 2022, Mechanical Engineering

An efficient simulation framework for co-optimization of design and control is fundamental in the development phase of hybrid electric vehicles to achieve the best system- level improvements of energy efficiency and emissions. Coordination schemes for co- optimization have been widely investigated in the literature, but only for a limited number and nature of design and control variables. In this study a decomposition-based coordination scheme capable to handle multi-time scale, time variant and time invariant (discrete and continuous) variables with ability to handle each sub-problem with different solver is not only demonstrated but also compared with simultaneous-based scheme in terms of optimality of the solution and computational performance. The two coordination schemes are used to co-optimize energy management strategy and components sizing for a series hybrid truck. In addition, multiple objectives are weighted in the cost function: fuel consumption, battery size, and tailpipe pollutant emissions. Results show that the simultaneous scheme is computationally less expensive for simple problems, but it becomes computationally inefficient with increasing problem complexity, with the additional drawback of not being able to handle integer-valued dynamic variables. On the other hand, the decomposition-based scheme can solve such problems, but with a more complex problem formulation. Results show that the decomposition-based scheme has not only 14% improvement in computational performance, but the optimality of the solution is also comparable with simultaneous-based scheme. Hence, as compared to the dynamic optimization, co-optimization yields up to 3.7% improvement in the average genset efficiency operation. Moreover, the fuel consumption for dynamic optimization was 2.5 kg which is reduced to 1.6 kg with co-optimization and was further reduced to 1.5 by adding engine on off control.

Committee: Qadeer Ahmed (Advisor); Shawn Midlam-Mohler (Committee Member); Manfredi Villani (Other) Subjects: Aerospace Engineering; Automotive Engineering; Electrical Engineering; Mechanical Engineering; Robotics

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

This dissertation studies cybersecurity, with a focus on the control theory for cyber physical system (CPS). We consider CPS as a networked control system (NCS) where the sensing and control signals transmitted through the communication network can be compromised. To secure the NCS, we develop detection and response algorithms against adversarial attacks. In particular, we propose dynamic watermarking as an active detection scheme against sensor spoofing attacks, stochastic transmission of the control signals as a control response against bus-off (DoS) attack, and optimization on communication link elimination as a control response against general attack scenarios whenever network switching is needed. In all the cases, we show the efficacy of applying the proposed algorithms against attacks and the guarantee of control performance after applying security measures. For communication link elimination, we consider a group of agent switching from a fully connected communication network to a backup network with limited bandwidth due to attack. This problem is formulated as a linear quadratic Gaussian (LQG) team problem. We show that optimizing communication links in the backup network is equivalent to adding a cardianlity constraint to the original quadratic optimization problem. Due to the combinatorial nature of the problem, we apply convex relaxation on the constraint set and random projection on the objective function as dimenaionality reduction tools. We provide the theoretical performance guarantee on the near-optimal solution obtained from the relaxed problem. For stochastic transmission against bus-off attacks, we present a mathematical model for the bus-off attack and formulate it as a non-zero sum game between the controller-transmitter pair and the attacker. A stochastic transmission policy is proposed as a proactive countermeasure when the attacker persists in the network. We determine the Nash equilibria of the non-zero sum game in the cases off open- (open full item for complete abstract)

Committee: Abhishek Gupta (Advisor); Emre Koksal (Committee Member); Aylin Yener (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2021, Engineering (Computer Science)

To achieve a reduction in carbon emission, researchers are looking for new methods to connect energy systems to enhance efficiency. Due to a large influx of variable and distributed energy resources in the electricity market of the U.S., there are significant alterations in the net electricity demand. And since the traditional nuclear power generation is inflexible, its supply cannot be aligned with the demand which in turn impacts its economic viability. The Nuclear Renewable Integrated Energy System (NR-IES) is a leading solution that integrates nuclear power plants, renewable energy, hydrogen generation plants, and energy storage systems so that thermal and electrical power can be dispatched to meet full grid flexible requirements while also producing hydrogen and maximizing revenue. This thesis introduces a Deep Reinforcement Learning (DRL) based framework to address the challenging decision-making for NR-IES. The goal is to maximize revenue by concurrently producing and selling hydrogen and electricity at different prices while maintaining energy flow in subsystems balanced. To enable an efficient and flexible computational framework for DRL's research and development, an FMI/FMU based co-simulation environment for NR-IES simulation has been developed to integrate the OpenAI Gym and Ray/RLLib. Two state-of-the-art DRL algorithms, namely Soft Actor-Critic (SAC), and Proximal Policy Optimization (PPO) have been investigated to demonstrate DRL's superiority in controlling NR-IES.

Committee: Dr. Raghav Khanna (Committee Chair); Dr. Devinder Kaur (Committee Member); Dr. Ahmad Javaid (Committee Co-Chair) Subjects: Computer Engineering; Computer Science; Energy; Sustainability