Doctor of Philosophy, Case Western Reserve University, 2016, EECS - System and Control Engineering

The closed-loop optimal control of multiple model linear systems with unknown parameters is investigated. The Bellman equation is modified to include the discrete random variable of the system mode conditioned on the measurements, and is then used to determine the optimal state feedback or dynamic output feedback controllers. Dynamic programming with the modified Bellman equation is used to calculate the optimal cost with the dual covariance. The dual covariance quantifies the probing aspects of the controller and is demonstrated that the closed-loop state or dynamic output feedback controllers have the dual property for the discrete-time multiple model linear systems with unknown parameters studied in this work. Monte Carlo simulations are used to show that the closed-loop control with state or dynamic output feedback always performs better than controllers such as the Certainty Equivalence or DUL controllers. Finally, the direct discrete-time implementation of the dual dynamic output feedback controller developed in this work is applied to the control of the nonlinear F-16 aircraft. The dual regulator is designed for stability augmentation in the context of reconfigurable control using the multiple model formulation integrated with flight and propulsion to accommodate sensor, actuator, and engine faults. The design process is explained in the context of trim, linearization, calculation of the mode probabilities, and tuning of the Kalman filters and includes the implementation of a six-stage dual regulator with a bank of parallel Kalman filters. The flight simulation results are presented for cases such as speed and pitch rate sensor faults, 1.5% and 3% losses of elevator actuator power, and 4% loss of engine power during steady-state level flight of the nonlinear F-16 aircraft model.

Committee: Kenneth Loparo PhD (Advisor); Marc Buchner PhD (Committee Member); Vira Chankong PhD (Committee Member); Richard Kolacinski PhD (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

The ubiquitous influence of fossil fuels in driving the world economy and the imperative need to reduce dependence of transportation on these fuels, has brought about a decade of research on alternative propulsion systems. Of the several alternative propulsion systems, hybrid electric vehicles (HEVs) are seen as an important short-term solution. In the most generic sense, a HEV consists of a battery and one or more electric machines in addition to the engine powered by petroleum/diesel. Depending on the vehicle architecture, the additional degree of freedom in selecting the amount of energy supplied by the primary and the secondary source of energy is a challenging control and optimization problem. The energy management strategy in a HEV aims at finding the optimal distribution of energy between the battery and the fuel to satisfy the requested power from the driver.Different energy management strategies have been developed both by the industry and the academia and they can be classified into non-realizable and realizable energy management strategies based on the amount of information required for real-time implementation. Traditionally, the non-realizable strategies formulate the energy management problem as a constrained optimal control problem of minimizing a performance index over a finite time interval under operational constraints. These strategies provide the global optimal solution and are used as benchmark solutions for comparative analysis of strategies. The realizable strategies in the literature have been primarily developed for implementation in real vehicles and have been shown to produce results similar to the global optimal solution. In spite of the extensive amount of research on both non-realizable and realizable energy management strategies, there are many shortcomings in the literature which have been addressed in this dissertation. The energy management problem of finding the optimal split between the different sources of energy in a charge-sust (open full item for complete abstract)

Committee: Giorgio Rizzoni Professor (Advisor); Stephen Yurkovich Professor (Committee Member); Vadim Utkin Professor (Committee Member); Yann Guezennec Professor (Committee Member); Simona Onori PhD (Committee Member) Subjects: Alternative Energy; Automotive Engineering; Electrical Engineering; Mechanical 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, Case Western Reserve University, 2022, EECS - System and Control Engineering

In this thesis, a control methodology that merges the adaptive extremum seeking control (ESC) with the robust quantitative feedback theory (QFT) into one compact control scheme is proposed. It utilizes the properties inherent in both methods to develop a novel control law that guarantees the convergence of a systems performance function driven by a plant model to meet multiple robust control performance objectives simultaneously. The system structure is set up to have the output of a linear time-invariant (LTI) model with structured uncertainties as the operating variable for a nonlinear objective function with possibly time-varying parameters. A new bound called the extremum seeking bound (ESB) is introduced to the QFT design process. By utilizing the information derived from tuning the ESC scheme for the systems static function, an ESB can be constructed that accounts for the speed of convergence of the ESC scheme and then combined with the other classical QFT bounds to design a controller that is both adaptive and robust for the system structure presented. The control architecture for this design is presented and used to develop the conceptual basis and detailed mathematical justification for the control law. The robust extremum seeking control (RESC) design is validated on two engineering problems using MATLAB and Simulink. One is a nonlinear system with time-varying parameters driven by a dynamical model with uncertain parameters that is described at a high level of abstraction to demonstrate the general applicability of the design method. The second, a specific engineering problem, is the design of a generator torque controller of a 10 kilowatts (KW) wind turbine for maximum power point tracking (MPPT) in region 2 operation.

Committee: Mario Garcia-Sanz (Advisor); Christian Zorman (Committee Member); Evren Gurkan-Cavusoglu (Committee Member); Vira Changkong (Committee Member) Subjects: Applied Mathematics; Electrical Engineering; Engineering; Mechanical Engineering; Systems Design

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

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

Supremacy in armed conflict comes not merely from superiority in capability or numbers but from how assets are used, down to the maneuvers of individual vehicles and munitions. This document outlines a research plan focused on skirmish-level tactics to militarily relevant scenarios. Skirmish-level refers to both the size of the adversarial engagement -- generally one vs. one, two vs. one, and/or one vs. two -- as well as the fact that the goal or objective of each team is well-established. The problem areas include pursuit-evasion and target guarding, either of which may be considered as sub-problems within military missions such as air-to-air combat, suppression/defense of ground-based assets, etc. In most cases, the tactics considered are comprised of the control policy of the agents (i.e., their spatial maneuvers), but may also include role assignment (e.g, whether to act as a decoy or striker) as well as discrete decisions (e.g., whether to engage or retreat). Skirmish-level tactics are important because they can provide insight into how to approach larger scale conflicts (many vs. many, many objectives, many decisions). Machine learning approaches such as reinforcement learning and neural networks have been demonstrated to be capable of developing controllers for large teams of agents. However, the performance of these controllers compared to the optimal (or equilibrium) policies is generally unknown. Differential Game Theory provides the means to obtain a rigorous solution to relevant scenarios in the form of saddle-point equilibrium control policies and the min/max (or max/min) cost / reward in the case of zero-sum games. When the equilibrium control policies can be obtained analytically, they are suitable for onboard / real-time implementation. Some challenges associated with the classical Differential Game Theory approach are explored herein. These challenges arise mainly due to the presence of singularities, which may appear in even the simplest differenti (open full item for complete abstract)

Committee: Zachariah Fuchs Ph.D. (Committee Member); David Casbeer Ph.D. (Committee Member); Dieter Vanderelst Ph.D. (Committee Member); Meir Pachter Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science in Mechanical Engineering (MSME), Wright State University, 2021, Mechanical Engineering

A set of guidance control laws is developed for application to a reduced order dynamic aircraft model. A feedback control formulation utilizing a linear quadratic regulator (LQR) is developed, together with methods for easing the design burden associated with gain tuning. Metrics are developed to assess the stability margin of the controller over the full flight envelope of a notional unmanned aerial vehicle (UAV) model. A feedforward control path is then added to the architecture. The performance of the guidance control laws is assessed through time domain step response metrics as well as through execution of a design mission. The thesis closes with a discussion of possible improvements regarding gain optimality and run-time performance of the model.

Committee: Mitch Wolff Ph.D. (Advisor); Scott Thomas Ph.D. (Committee Member); James Menart Ph.D. (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Electrical Engineering

The goal of this research is to demonstrate how optimal control methods can be used as the basis for designing feedback controller parameterizations that can be evolved in an evolutionary algorithm (EA) to obtain understandable near-optimal solutions to different types of adversarial optimal control problems. This is accomplished by mathematically framing the dynamics and utility of the problem in the context of an optimal control scenario and analytically solving for the optimality conditions. The resulting set of optimal bang-bang control outputs are then used as the basis for a state space based feedback controller parameterization. This parameterization allows one to encode a feedback controller into a genome by breaking the state space of the problem up into regions of discrete constant control. There are two main benefits to this methodology. First, it drastically reduces the solution space and allows the resulting evolved solutions to be visually analyzed much in the same way as the results of analytically solving the problem to extract useful information about the underlying optimal control boundaries. This allows one to better understand what the evolved solution will do in any given situation as opposed to black box parameterizations whose performance cannot be easily understood without extensive verification. Second, it allows one to take advantage of geometric state information to create more meaningful evaluation, crossing, and mutation methods. As is shown in the results, this allows one to increase the performance across various aspects of the EA. This dissertation demonstrates this methodology in three different optimal control problems: a Real-Time Combat problem, a Turn-Constrained Path Planning problem, and a Predator Prey problem. In cases where the optimal solution exists, the evolved control boundaries are compared back to the optimal ones in order to verify optimality. The EA is also applied to variations of the problems in which the optimal s (open full item for complete abstract)

Committee: Zachariah Fuchs Ph.D. (Committee Chair); David Casbeer Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); Dieter Vanderelst Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy in Engineering, Cleveland State University, 2021, Washkewicz College of Engineering

This dissertation focuses on the study and optimization of human training and its physiological effects through the use of advanced exercise machines (AEMs). These machines provide an invaluable contribution to advanced training by combining exercise physiology with technology. Unlike conventional exercise machines (CEMs), AEMs provide controllable trajectories and impedances by using electric motors and control systems. Therefore, they can produce various patterns even in the absence of gravity. Moreover, the ability of the AEMs to target multiple physiological systems makes them the best available option to improve human performance and rehabilitation. During the early stage of the research, the physiological effects produced under training by the manual regulation of the trajectory and impedance parameters of the AEMs were studied. Human dynamics appear as not only complex but also unique and time-varying due to the particular features of each person such as its musculoskeletal distribution, level of fatigue, fitness condition, hydration, etc. However, the possibility of the optimization of the AEM training parameters by using physiological effects was likely, thus the optimization objective started to be formulated. Some previous research suggests that a model-based optimization of advanced training is complicated for real-time environments as a consequence of the high level of complexity, computational cost, and especially the many unidentifiable parameters. Moreover, a model-based method differs from person to person and it would require periodic updates based on physical and psychological variations in the user. Consequently, we aimed to develop a model-free optimization framework based on the use of Extremum Seeking Control (ESC). ESC is a non-model based controller for real-time optimization which its main advantage over similar controllers is its ability to deal with unknown plants. This framework uses a physiological effect of training as bio-fe (open full item for complete abstract)

Committee: Hanz Richter Ph.D. (Advisor); Antonie van den Bogert Ph.D. (Committee Member); Eric Schearer Ph.D. (Committee Member); Kenneth Sparks Ph.D. (Committee Member); Douglas Wajda Ph.D. (Committee Member) Subjects: Engineering; Mechanical Engineering; Robotics; Robots

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

A major emphasis within the automotive industry today is autonomous driving. Many recent studies in this area deal with the development of real-time optimal control strategies to improve overall vehicle energy efficiency. Although such research is critically important, it overlooks the potential need to reevaluate the design of an autonomous vehicle itself, especially as it relates to the powertrain. Failing to thoroughly examine the impact of autonomous driving on vehicle powertrain design could limit the potential opportunities to augment the energy-efficiency gains from optimal powertrain control (power demand) strategies. Therefore, this thesis addresses this situation by investigating the impact of autonomous driving on the design (sizing) and control strategies (energy management + power demand) of a plug-in hybrid-electric vehicle (PHEV) powertrain. In particular, a dynamic optimization method known as multidisciplinary dynamic system design optimization (MDSDO) is used to formulate and solve a combined optimal design and control optimization (or control co-design) problem for an autonomously-driven PHEV powertrain under two simulation conditions: in the first, only an autonomous driving cycle represented by a hypothetical lead (HL) duty cycle is considered, whereas the second also includes acceleration and all-electric range (AER) performance along with the HL duty cycle in order to generate an overall powertrain design solution. The optimal solutions for both simulation conditions are then compared to those corresponding to a control co-design problem for a human-driven PHEV powertrain, with the results indicating that autonomous driving does indeed have a significant impact on both powertrain design and control. Therefore, this implies a compelling need to reevaluate current powertrain design conventions when developing autonomous vehicles.

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

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

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

A novel approach to parameterize and solve for optimal satellite attitude state trajectories is presented. The optimal trajectories are parameterized using fuzzy inference systems (FISs), and the FISs are optimized using a genetic algorithm. Eight different constrained optimization problems are solved. The objective of each optimization problem is either battery charge maximization, link margin (equivalent to antenna gain) maximization, or experiment temperature minimization. All optimization problems consider reaction wheel angular velocity and reaction wheel angular acceleration constraints, and five of the optimization problems consider either battery charge constraints, antenna gain constraints, or both battery charge and antenna gain constraints. Reaction wheel constraints are satisfied using an attitude state filter at the output of the FISs and an optimal magnetic torque / reaction wheel desaturation algorithm, the design of both of which is presented herein. Optimal attitude state trajectory, or attitude profile, FISs are compared with a nominal attitude profile. It is shown that, while the nominal attitude profile offers good performance with respect to both battery charge and link margin, the optimal attitude profile FISs are able to outperform the nominal profile with respect to all objectives, and a minimum temperature attitude profile FIS is able to achieve average experiment temperatures 30–40 K lower than the nominal attitude profile. The attitude state trajectory optimization solutions presented in this work are motivated by the needs and constraints of the CryoCube-1 mission. Because this work is integral to the functionality of the CryoCube-1 satellite system, the effort taken to successfully build, test, deliver, launch, and deploy this CubeSat is detailed. The intent of providing this systems view is to provide the context necessary to understand exactly how the attitude state trajectory optimization results were used within the satellite system.

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member); Phil Putman Ph.D. (Committee Member); Anoop Sathyan Ph.D. (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Today's world faces numerous environmental challenges as we attempt to meet the growing demands for mobility while tackling its negative externalities. In recognition of these negative externalities, world governments have enacted increasingly stricter standards that the means of mobility must meet. To meet these demands, private and public industries have invested tremendous resources into alternative means of mobility. The most promising step to immediately reduce mobility's negative externalities is the use of hybrid technologies. Hybrid technologies utilizes traditional petrochemical energy sources combined with electrical sources to power mobility. If properly controlled, this allows for a reduction in energy consumption and pollutant production in vehicles. The challenge for mobility engineers is how to properly control these multi-domain systems to reduce negative externalities. Extensive research in the field of optimal control applied to hybrid vehicles has already shown that fuel consumption can be minimized within a charge sustaining hybrid through optimized torque splitting. Furthermore, research into pollutant production and control has greatly reduced air pollution from vehicles. Both fields have already penetrated the consumer market and helped form control strategies that are already on the road. However, the increasing demands placed by regulations require constantly pushing the bounds for the extra reduction in fuel consumption or pollutant production. As such, this research develops a methodology that can be applied to HEVs to establish a controls strategy for the simultaneous reduction of fuel consumption and pollutant production. This work relies on model-based techniques to simulate vehicle operation, and optimal control techniques to use the developed vehicle models to establish control policies to reduce fuel consumption and pollution production. This work goes through the development of a catalyst and emissions model that predicts tailpipe (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Advisor); Ahmet Selamet (Committee Member); Vadim Utkin (Committee Member); Punit Tulpule (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The increasing complexity of the Powertrain model with the emerging trends in the hybrid and connected vehicles industry demands new approaches. As an Optimal Control Problem for the Energy Management of these class of vehicles becomes more complicated and larger in size due to addition of several mixed integer (continuous and discrete) states and controls variables in a dynamical system, the currently used offline global optimization techniques such as Dynamic Programming may not find a practical application due to a significantly high computational effort or in some cases, even failing to provide any solution at all. Thus, it becomes important to investigate a substitute optimization-based algorithm that can offer a good scalability in terms of numerical efficiency and computational effort as the Optimization Control Problem (OCP) becomes larger in size. In this thesis, we attempt to explore and solve different sizes of Optimal Energy Management Problems concerned with a Parallel P2 Hybrid Electric Vehicle using DP as well as a new algorithm called Pseudospectral Collocation method or PSC (using CasADi). Due to PSC's promising performance and a possible interface with MATLAB/Simulink as shown in the last chapter, this thesis essentially aims to stimulate researchers' interest even further to explore and solve much complicated and larger Hybrid/Electric Vehicle EMS problems using the proposed methodology.

Committee: Qadeer Ahmed Dr. (Advisor); Giorgio Rizzoni Dr. (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

Connected vehicles promise to increase transportation options and reduce travel times while improving the safety of road users. Convoying/platooning are the common use case of connected vehicles technology and the driveability performance impact of such convoy has never been researched before. The vehicles when following each other in a convoy, using adaptive cruise control (ACC), is augmented by the lead vehicle information (vehicle acceleration) through the vehicle to vehicle communication as a feedforward control is called Cooperative Adaptive Cruise Control (CACC). This dissertation analyses the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles. In order to assess the driveability performance, a framework consisting of various metrics has been developed. The parameter space robust control methodology has been used to design the controller that improves the convoy's driveability and the performance is compared to the convoy that is being tuned for maintaining the time gap. These simulation results were verified in a real-time setting using a Hardware-in-the-Loop (HIL) setup using a CARSIM high-fidelity car model. With the use of the V2X technology, the fuel economy of the connected vehicle can be improved and it is called Eco-Driving. This dissertation proposes a framework for Eco-driving that is comprised of Eco-Cruise, Greenwave algorithm, and Eco-CACC. The Eco-Cruise is the algorithm which calculates the optimal velocity profile based on the route information such as speed limit, stop sign and traffic sign location and the vehicle powertrain model. A Dynamic programming based algorithm which minimizes the fuel economy is developed. The Eco-Cruise algorithm stops at all the stop signs and traffic light (assuming red light) optimally. Driving scenario has a very big impact on the Eco-cruise algorithm, and a new methodology has been proposed in this dissertation, that formulates a metric based route selection t (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Vadim Utkin (Committee Member); Bilin Aksun-Guvenc (Committee Member); Abhishek Gupta (Committee Member) Subjects: Automotive Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

In response of the world's increasing concern on carbon emissions, vehicle real-world fuel economy potential is being further developed by autonomy and connectivity, so as to achieve superior judgment and to eliminate fuel waste by imperfect human operations. A starting point is by redesigning the existing Adaptive Cruise Control (ACC) system with route preview and optimal control calculation, which is regarded as predictive optimal control in this work. The work targets to provide algorithm solutions for designing and implementing predictive optimal control in a real-world vehicle system, covering the aspects of control, estimation, and prediction. For control development, two algorithms are designed for the scenarios of optimal car-following speed control and optimal cruise control on a hilly route. The two designs share a common concept that by previewing the upcoming condition change, the vehicle control can be scheduled with a constrained modulation range in trade of improved operation cost. For the problem of optimal car-following speed control, which contains a mixed-integer programming problem caused by gearshifts, optimization complexity is broken down by a hybrid solver of Quadratic Programming (QP) and Pontryain's Minimum Principle (PMP). The solver partitions the problem into simplified sub-problems with quick quasi-optimal solutions, so that the search space is efficiently reduced to achieve constrained optimal control solutions in real time. Control results show major fuel saving benefits with clean gear shifts. For the problem of cruising on a hilly route, where the vehicle drives on a high gear and the engine operates near the torque capacity curve, control solving encounters the challenges by non-convex & non-affine constraints, along with state-dependent system switching. To achieve flexibility in optimal control solving, a PMP analytical solution set is developed to self-expand forward in time, detecting the system's constraints and switches wh (open full item for complete abstract)

Committee: Umit Ozguner (Advisor); Giorgio Rizzoni (Committee Member); Vadim Utkin (Committee Member) Subjects: Electrical Engineering

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, The Ohio State University, 2016, Electrical and Computer Engineering

Electric power grid is experiencing a major paradigm shift toward a more reliable, efficient, and environmentally friendly grid. The concept of microgrid is introduced to integrate distributed renewable generation in proximity to demands for both environmental and power-efficient promises. A microgrid can be disconnected, or "islanded", from the main grid and operates on its own, providing energy to remote areas or during faults of the main grid for better reliability. Islanded microgrids inherit several different properties from traditional power grids, including uncertain and limited generation, mixed R/X ratio lines, and lack of power inertia from synchronous generators. Those properties pose new challenges for the stable operation of islanded microgrids. The dissertation is dedicated to addressing the control challenges of islanded microgrids. The contribution is twofolds. First, we propose a polynomial time optimal power flow (OPF) solver which finds an optimal operating point for the inverters of the distributed energy resources. The proposed algorithm can account for the cost functions on the reactive generation that are common in microgrids. It also brings new understanding on the conjectures of exact semidefinite programming (SDP) convex relaxation on the OPF problem. Furthermore, we show that without the load over-satisfaction assumption usually seen in the literature, a near global optimum can be found for the OPF problem with arbitrary convex quadratic cost functions. The results are important to both microgrids and the classical OPF problem. Our second major contribution is developing a novel distributed controller that addresses the control challenges originated from limited generation, mixed R/X ratio lines, and lack of power inertia properties of islanded microgrids. The proposed controller can ensure proportional active and reactive power sharing and frequency synchronization while respecting the voltage constraints. Variances of the distributed (open full item for complete abstract)

Committee: Wei Zhang (Advisor); Kevin Passino (Committee Member); Andrea Serrani (Committee Member); Krishnaswamy Srinivasan (Other) Subjects: Electrical Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2014, Electrical and Computer Engineering

This work presents the development of an integrated flight controller for a generic model of a hypersonic air-breathing vehicle. The flight control architecture comprises a guidance and trajectory planning module and a nonlinear inner-loop adaptive controller. The emphasis of the controller design is on achieving stable tracking of suitable reference trajectories in the presence of a specific engine fault (inlet unstart), in which sudden and drastic changes in the vehicle aerodynamics and engine performance occur. First, the equations of motion of the vehicle for a rigid body model, taking the rotation of the Earth into account, is provided. Aerodynamic forces and moments and engine data are provided in lookup-table format. This comprehensive model is used for simulations and verification of the control strategies. Then, a simplified control-oriented model is developed for the purpose of control design and stability analysis. The design of the guidance and nonlinear adaptive control algorithms is first carried out on a longitudinal version of the vehicle dynamics. The design is verified in a simulation study aiming at testing the robustness of the inner-loop controller under significant model uncertainty and engine failures. At the same time, the guidance system provides reference trajectories to maximize the vehicle's endurance, which is cast as an optimal control problem. The design is then extended to tackle the significantly more challenging case of the 6-degree-of-freedom (6-DOF) vehicle dynamics. For the full 6-DOF case, the adaptive nonlinear flight controller is tested on more challenging maneuvers, where values of the flight path and bank angles exceed the nominal range defined for the vehicle. Simulation studies show stable operation of the closed-loop system in nominal operating conditions, unstart conditions, and during transition from sustained scramjet propulsion to engine failure mode.

Committee: Andrea Serrani Prof. (Advisor); Umit Ozguner Prof. (Committee Member); Zhang Wei Prof. (Committee Member) Subjects: Aerospace Engineering; Computer Engineering; Electrical Engineering; Engineering

Master of Science, The Ohio State University, 2014, Mechanical Engineering

This thesis details the development, implementation, and experimental testing of a supervisory energy management control strategy for the vehicle electrical system of a passenger car. The control strategy commands the alternator duty cycle such that vehicle fuel economy is optimized whilst the instantaneous load current demand is met and constraints on the system voltage and battery state of charge are satisfied. To this extent, Pontryagin's Minimum Principle (PMP) is utilized alongside a vehicle plant model in order to evaluate the behavioral characteristics of the vehicle electrical system subjected to optimal control. These observations are employed in the development of an adaptive, PMP-based supervisory strategy capable of real-time control. Experimental testing of the in-house developed control strategy, termed "A-PMP", is benchmarked against a baseline production control strategy, demonstrating consistent improvements in vehicle fuel economy.

Committee: Marcello Canova (Advisor); Shawn Midlam-Mohler (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering