Doctor of Philosophy in Engineering, University of Toledo, 2004, Electrical Engineering

A new class of intelligent adaptive control for systems with complex and multimodal dynamics including scheduled and unscheduled ‘Jumps', is developed. Those systems are often under the challenge of unforeseen changes due to wide range of operations and/or external influences. The underlying structural feature is an introduction of an Intelligent Supervisory Loop (ISL) to augment the Model Reference Adaptive Control (MRAC) framework. Four novel design formulations are developed which evolve from different methods of conceiving ISL, structured into intelligent control algorithms, and then investigated with comprehensive simulation models of a single link flexible robotic manipulator as well as a six degree of freedom F16 fighter aircraft. The first scheme is a Fuzzy Multiple Reference Model Adaptive Controller (FMRMAC). It consists of a fuzzy logic switching strategy introduced to the MRAC framework. The second is a novel Neural Network Parallel Adaptive Controller (NNPAC) for systems with unmodeled dynamics and mode swings. It consists of an online growing dynamic radial basis neural network, which controls the plant in parallel with a direct MRAC. The third scheme is a novel Neural Network Parallel Fuzzy Adaptive Controller (NNPFAC) for dynamic ‘Jump' systems showing scheduled mode switching and unmodeled dynamics. The scheme consists of a growing online dynamic Neural Network (NN) controller in parallel with a direct MRAC, and a fuzzy multiple reference model generator. The fourth scheme is a Composite Parallel Multiple Reference Model Adaptive Controller (CPMRMAC) for systems showing unscheduled mode switching and unmodeled dynamics. The scheme consists of an online growing dynamic NN controller in parallel with a direct MRAC, and an NN multiple reference model generator. Extensive feasibility simulation studies and investigations have been conducted on the four proposed schemes, and with results consistently showing that the four design formulations developed in (open full item for complete abstract)

Committee: Adel Ghandakly (Advisor) Subjects:

Master of Science, The Ohio State University, 2013, Electrical and Computer Engineering

This research is motivated by the world's fast-growing demand for energy savings. Due to the increasing cost of fossil fuels (e.g., oil, coal, natural gas), research has been conducted to effectively reduce the electricity consumption in office buildings by means of employing smart lighting. This thesis investigates the implementation of an adaptive and nonadaptive fuzzy control for a smart light experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under cross-illumination effects, and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive fuzzy controller is implemented and applied to the experimental testbed. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive fuzzy controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting for complex failures, a fuzzy model reference learning controller (i.e., adaptive fuzzy) is developed for the experimental testbed, and this algorithm proves to be able to adapt to uncertainties such as disturbances and failures via a learning mechanism.

Committee: Kevin M. Passino Prof. (Advisor); Wei Zhang Prof. (Committee Member) Subjects: Electrical Engineering

Master of Science in Engineering (MSEgr), Wright State University, 2016, Electrical Engineering

Fuzzy logic control of a Hyperloop is carried out in this thesis. Hyperloop is being described hypothetically as a fifth mode of mass transportation and is a registered trademark of Space Exploration Technologies Corporation (SpaceX). To inspire others to help in its development and make it a reality, the Hyperloop is being explored as open-source technology by SpaceX. In this thesis a near friction-less track is constructed and is fixed inside a tunnel. External fans are used to produce air pressure inside the tunnel to propel the vehicle down the track. Fuzzy Logic Control is used to stop the vehicle at a desired location. The objective is to stop the vehicle at various end point positions. It is assumed that the vehicle is traveling at or near the desired velocities before the Fuzzy Logic Controller become active. The results show that the Fuzzy Logic Controller is able to effectively stop the vehicle at or near the desired end point positions given a very dynamic and highly non-linear environment.

Committee: Kuldip Rattan Ph.D. (Advisor); Marian Kazimierczuk Ph.D. (Committee Member); David Gross Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering; Engineering; Transportation

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

MS, University of Cincinnati, 2018, Engineering and Applied Science: Aerospace Engineering

Aerospace applications are composed of many dynamic systems which are coupled, nonlinear, and difficult to control. Fuzzy logic (FL) systems provides a means by which to encode expert knowledge into a set of rules which can produce highly nonlinear control signals; this is possible because FL, like many other soft computational methods is a universal approximator. While FL systems alone excel at encapsulating expert knowledge bases, when coupled with genetic algorithms (GA), they can learn the knowledge base from evolutionary repetition. It is the goal of this work to present the efficacy of hybrid genetic fuzzy systems (GFS) in a variety of applications. This will be achieved through exploring three specific use cases. First, a variation of a benchmark problem presented at the 1990 American Control Conference is used to demonstrate the robustness of FL control as well as the utility of GAs in the learning process. The results are a controller that is far more resistant to even large changes in the plant dynamics compared to a linear controller and a process by which a class of controllers may be quickly tuned for changes to the plant system. The next problem applies the same approach to an elevator actuator for pitch control of an F-4 Phantom. This controller is tuned for a nominal case and ten subjected to the same plant with degraded aerodynamic coefficients. It is compared to a well-tuned PID controller. The effort culminates in a practical application of a FL system to guide a small unmanned aerial system (sUAS) to a precision landing on a target platform moving with uncertain velocity. This was accomplished using custom developed Python software for GFS control in conjunction with Robot Operating System (ROS) and a simulation environment called Gazebo. Heavy emphasis was placed on using only software components which can be easily implemented on popular hardware platforms. ROS was critical to meeting this goal, as well as the open source flight cont (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); George T. Black M.S. (Committee Member) Subjects: Engineering

Doctor of Philosophy, University of Toledo, 2016, Industrial Engineering

The main goal of this research is to present new efficient methods and optimization models to enhance the Green Supply Chain Planning (GSCP). As a first objective, we focus on developing a novel optimization planning model in a green supply chain network consisting of suppliers, assemblers, distribution centers, and retailers. This model is subjected to various constraints which are related to the inventory and forward logistics management. We applied the proposed model for a vacuum and floor machines manufacturer case study located in the Midwestern, U.S. The main objective functions include: minimizing the costs of assembling, transporting, holding inventory at assembling sites and distribution centers, and shortage at retailers under carbon dioxide (CO2) emissions constraints throughout the logistic network; maximizing service levels and determining the acceptable service levels to meet final customers' demands. We applied three different solution methods including a gradient-based algorithm in MATLAB “Find Minimum of Constrained nonlinear multivariable function (FminCon)”, a novel metaheuristic algorithm “Grey Wolf”, and the “Branch and Bound (B&B)” algorithm in Lingo to find optimal solutions for the proposed optimization model, which has a specific complexity. We compared the achieved optimal solutions by these methods. The case study and expanded numerical example verify whenever the parameter of the minimum service level at retailers' sites increases or decreases, the amount of produced CO2 emissions and the total costs of the supply chain will directly correlate. It also demonstrates the trade-offs among the total costs of the supply chain network, CO2 emissions, and service levels. The achieved results reflect the efficiency of the proposed model for GSCP. As a second objective, we concentrate on revealing more information about optimal points in which performance measures of various adaptive (X ) ¯quality control charts hold their optimal minimum values. (open full item for complete abstract)

Committee: Matthew Franchetti Dr. (Committee Chair); Efstratios Nikolaidis Dr. (Committee Member); Kumar Ashok Dr. (Committee Member); Zhang Yue Dr. (Committee Member); Spivak Alex Dr. (Committee Member) Subjects: Applied Mathematics; Artificial Intelligence; Automotive Engineering; Business Costs; Computer Science; Engineering; Environmental Engineering; Environmental Management; Health Care; Health Care Management; Operations Research; Sustainability; Systems Design; Transportation Planning

MS, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in “dull, dirty or dangerous” mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Elad Kivelevitch Ph.D. (Committee Member); Bruce Walker Sc.D. (Committee Member) Subjects: Aerospace Materials

Master of Science, The Ohio State University, 2010, Electrical and Computer Engineering

Current bipedal robots with articulated legs, even the most impressive prototypes to date, still lack the ability to execute dynamic motions such as jumping and running with comparable performance to biological systems. Recently a new biped prototype, KURMET, has been built at OSU to serve as an experimental platform for further investigation into the performance of dynamic movements in bipedal machines with biologically-realistic features. KURMET has series-elastic actuators (SEA) at all leg joints. The presence of SEAs provides the compliance that is needed in dynamic motions, yet also complicates the controller's tasks, especially when combined with articulated legs in a system that is not naturally stable. This thesis develops a fuzzy control system for hopping with KURMET. With this controller, KURMET can stably hop at varying heights and forward/backward velocities. The control system is arranged into two levels. The low-level control executes the hop motion. It employs a hopping state machine that is specifically designed to accommodate the natural dynamics of the SEAs. The high-level control is a fuzzy controller that is called at discrete instances (every top of flight (TOF)) to regulate the key parameters in the state machine. Through proper selection of these parameters, the desired hop height and velocity can be achieved. The fuzzy rulebase is generated via an iterative training process, which is done off-line through dynamic simulation using detailed models of the articulated mechanism and the series-elastic actuation. The fuzzy rulebase is later modified by on-line adaptation. The fuzzy rulebase has fewer than 200 rules; however, the overall fuzzy control system is able to produce robust and accurate hopping performance in KURMET. Experimental data shows that the maximum error of the torso height at TOF is controlled within 1 cm and the maximum error of the torso velocity at TOF is controlled within 5 cm/s. This thesis also experimentally investigates (open full item for complete abstract)

Committee: David Orin (Advisor); Yuan F. Zheng (Other) Subjects: Electrical Engineering; Engineering; Mechanical Engineering; Robots

Master of Science, The Ohio State University, 2009, Mechanical Engineering

The ability to perform high-speed dynamic maneuvers is an important aspect of locomotion for bipedal animals such as humans. Running, jumping, and rapidly changing direction are fundamental dynamic maneuvers that contribute to the adaptability and performance required for bipeds to move through unstructured environments. A number of bipedal robots have been produced to investigate dynamic maneuvers. However, the level of performance demonstrated by biological systems has yet to be fully realized in a biped robot. One limiting factor in achieving comparable performance to animals is the lack of available control strategies that can successfully coordinate dynamic maneuvers. This thesis develops a control strategy for producing vertical jumping in a planar biped robot as a preliminary investigation into dynamic maneuvers. The control strategy was developed using a modular approach to allow adaptation to further dynamic maneuvers and robotic systems.The control strategy was broken into two functional levels to separately solve the problems of planning and performing the jump maneuver. The jump is performed using a low-level controller, consisting of a state machine for determining the current phase of the jump and motor primitives for executing the joint motions required by the current phase. The motor primitives, described by open- and closed-loop control laws, were defined with numeric control parameters for modifying their performance. The high-level controller performs the task of planning the motion required to achieve the desired jump height. Fuzzy control, an intelligent control approach, was selected for the high-level controller. The fuzzy controller uses heuristic information about the biped system to select appropriate control parameters. This heuristic knowledge was implemented in a training algorithm. The training algorithm uses iterative jumps with error-based feedback to determine the control parameters to be implemented by the fuzzy controller. The cont (open full item for complete abstract)

Committee: James Schmiedeler (Advisor); David Orin (Committee Member); Chia-Hsiang Menq (Committee Member) Subjects: Electrical Engineering; Engineering; Mechanical Engineering

PhD, The Ohio State University, 2003, Industrial and Systems Engineering

Pneumatic actuators offer a low-cost alternative to conventional servo technologies. Like electromagnetic actuators, pneumatics offer clean and reliable operation. Like hydraulic actuators, pneumatics can be coupled directly to a payload, without the need for power or motion conversion. Unlike electromagnetics and hydraulics, a pneumatic actuator exhibits significant nonlinear behavior. These nonlinear characteristics prevent linear control systems, such as PID, from providing acceptable servo control of the pneumatic actuator. Relatively recent developments in control strategies, though, allow for improved control of servopneumatics, making them competitive with traditional servo technologies. The objective of this research is to explore advanced control strategies for proportionally-controlled pneumatic actuators. A significant constraint applied to this study is that the strategies developed must work within the architecture of an industrial programmable logic controller (PLC). Two control systems were developed, and their performance compared to that of a PI controller. A simulation allows for investigation of phenomena not directly measurable with the experimental apparatus. This research demonstrates the capabilities and limitations of advanced control strategies with a PLC.

Committee: Gary Maul (Advisor) Subjects:

Master of Science in Engineering, University of Akron, 2006, Biomedical Engineering

Premature birth is a world wide problem. Neonates, who are born premature, often don't have enough maturity to regulate their temperature. These infants have low metabolic heat production rate and may have high heat loss from the skin. Premature infants are kept in infant incubators which provide convective heating. There are two kinds of techniques available to control the incubator temperature. Currently either the incubator air temperature is sensed and used to control the heat flow, or infant's skin temperature is sensed and used in the close loop control. Skin control often leads to large fluctuations in the incubator air temperature. Air control also leads to skin temperature fluctuations. The question remains if both the skin temperature and the air temperature can be simultaneously used in the control. The purpose of the present study was to address this question by developing a fuzzy logic control which incorporates both incubator air temperature and infant's skin temperature. The temperature space was divided into a number of sub-domains. The crisp values of skin and air temperature were first fuzzified to obtain membership values which were then input to a rule base to obtain the output. This output was defuzzified to obtain a crisp value for the heat flow parameter. This fuzzy logic control system was evaluated using a mathematical model of the infant incubator system (Simon, Reddy, and Kantak, 1994). Simulation results revealed that fuzzy logic system, incorporating both skin and air temperatures, provide a smooth control when compared to either the air or skin control.

Committee: Narender Reddy (Advisor) Subjects: Engineering, Biomedical

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

With the advancement of communication technology, automobiles are gaining more functionalities at an increasing rate. Connected vehicle (CV) technology enables wireless communication between autos, also known as Vehicle to Vehicle (V2V) technology. Creating interpretable vehicle platooning controllers is crucial for improving both human-vehicle communication and ensuring compliance with responsible artificial intelligence (AI) principles. Unforeseen malfunctions of these devices might result in annoyance and a reduction in driver confidence, underscoring the importance of responsible AI. By ensuring meticulous and accountable development, implementation, and use of artificial intelligence systems, we not only reduce the potential disadvantages in platooning controllers but also guarantee transparency, equity, and security in the broader application of AI. The objective of this thesis is to develop fuzzy logic based controllers for connected vehicle platooning, specifically focusing on longitudinal and latitudinal car-following control. The objective of this study is to enhance a car's ability to sustain a consistent time interval between vehicles and optimize the comfort of highway travel. This research examines various scenarios that closely resemble highway circumstances, all of which are conducted at high speeds on the highway. The driving models that have been created aim to reduce the distance or time gap between the preceding vehicles in each scenario to a following distance or time gap of 1 second. The driving models are evaluated against the Krauss driving model, which emulates a human driver, and the Cooperative Adaptive Cruise Control (CACC) driving model in identical settings. This study will specifically examine the traffic flow and safety precautions, including the distance between the car being studied and the vehicle in front of it, the abrupt changes in acceleration of the vehicle being studied, and the time it takes for a collision to occur (TTC).

Committee: Kelly Cohen Ph.D. (Committee Chair); Anoop Sathyan Ph.D. (Committee Member); Donghoon Kim Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Materials

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, 2022, Engineering and Applied Science: Aerospace Engineering

Safety is a critical component for aerospace systems, where a mistake or fault on board an aircraft or spacecraft could result in hardware damage, mission failure, or even the loss of human lives. In the absence of scalable advanced control and system verification methods, one approach is to monitor the behavior of control techniques at run time and intervene to assure safety of the system. Run Time Assurance (RTA) systems are online safety assurance techniques that filter the output of a primary controller to actively assure safety of the system. RTA can be used in safety-critical control applications where a performance driven primary controller may cause the system to violate safety constraints. RTA is designed to be completely independent of the primary controller, and therefore can be applied to any aerospace control system. This research evaluates four categories of RTA approaches for application to different aerospace control systems. Each RTA approach is classified based on its membership to explicit or implicit monitoring and switching or optimization based intervention. First, to show the feasibility and compare performance, an unconstrained linear quadratic regulator is used as the performance driven primary controller for spacecraft docking, while all four RTA approaches are demonstrated to adhere to velocity limit safety constraints. Second, all four RTA approaches are evaluated in a fixed-wing aircraft formation flight scenario with safety constraints on position and velocity. Third, this scenario is expanded to use quadrotors instead of fixed-wing aircraft to evaluate the performance of RTA on a smaller scale, where it can easily be implemented and tested in the real world. Fourth, all four RTA approaches are applied to assure safety during reinforcement learning training in a simplified spacecraft docking scenario. The impact of each RTA on the training time and ultimate performance of the trained controller are compared to reward-shaping appr (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Member); Timothy J. Arnett Ph.D. (Committee Member); Anoop Sathyan PhD (Committee Member); Ou Ma Ph.D. (Committee Member); Kerianne Hobbs Ph.D. (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy (Ph.D.), University of Dayton, 2020, Electrical and Computer Engineering

A multi-input multi-output (MIMO) direct adaptive torque controller with ε-modification;-modification is presented that uses a conventional fuzzy systems to provide end-effector motion task in work space for a class of hyper-redundant manipulators with uncertain dynamics. It is illustrated via both simulated and implemented examples that the MIMO adaptive controller, which drives the torque of each joint to control end-effector dynamic variables, can highly improve the robotic performance considering both its kinematics and dynamics while executing motion control or tracking a reference in work space. In addition, it increases the robustness with respect to disturbances, sensor noise and insufficiently understood dynamics. We prove that the efficacy of our control algorithm affects the accuracy, stability and robustness of both motion control and path tracking. Also, an on-line task modification method (OTMM) is applied to achieve singularity avoidance for the hyper-redundant arm at the velocity level. It avoids the singularity on-line and waives off-line singularity avoidance path planning and the effort to check whether the singularity is escapable for the hyper-redundant manipulator.

Committee: Raul Ordonez (Committee Chair); Vijayan Asari (Committee Member); Malcolm Daniels (Committee Member); Muhammad Usman (Committee Member) Subjects: Electrical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2018, Electrical Engineering

Modern aircraft with nonlinear flight envelopes predominately utilize gain scheduled controllers to provide stability of flight. Using gain scheduled control techniques, nonlinear envelopes can be linearized into collections of linear systems that operate under various system dynamics. Linear controllers approximate the nonlinear response over setpoints of operating conditions which allow traditional linear theory to be applied to maintain stability. Techniques to prove linear stability are well understood and realized in control systems, but when controllers are switched, interpolation methods must be used. Interpolation is necessary as gain scheduled systems do not have foundational switching paradigms as part of their realization and therefore can not naturally guarantee smooth (or stable) transitions. To ensure stability between linear controllers, empirical data must be obtained through test and simulation which adds significant time and fiscal cost to development. This work examines if fuzzy controllers can provide similar response to that of gain scheduled controllers. By representing controllers as fuzzy representations, transitions between the designed linear setpoints can be smoothed by adding membership functions between defined linear controllers. However, fuzzy control lacks analytical tools to find the stability margins to test the stability of fuzzy systems. In order to provide assurance of stability and performance concerns, fuzzy controllers are translated into hybrid automata representations. Hybrid Automata (HA) theory, which is gaining popularity to represent cyber-physical systems (CPS), is an extension of finite state machines (finite automata) which blends continuous dynamics with discrete switching conditions. The hybrid representation of the fuzzy system allows reachability tools and formal methods to examine stability and desired performance characteristics. This provides evidence that a fuzzy controller can produce, at a (open full item for complete abstract)

Committee: Kuldip Rattan Ph.D. (Committee Chair); Matthew Clark M.S.Egr. (Committee Member); Marian Kazimierczuk Ph.D. (Committee Member) Subjects: Computer Science; Electrical Engineering

MS, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of Unmanned Aerial Systems (UAS) continue to multiply. Therefore, solutions to managing vehicles in highly congested airspace must be explored. In this study, an intelligent systems approach was used to help mitigate the risk of collision between aircraft in uncontrolled airspace using a UAS Traffic Management (UTM) System. To test the effectiveness of this system, a three-dimensional environment was created using MATLAB to simulate a fully autonomous heterogeneous fleet of UAS attempting to accomplish a variety of realistic missions, including precision agriculture, package delivery services, natural resource monitoring, and disaster management. Main research challenges include situational awareness, decision making, and multi-agent control in an uncertain, time-critical, spatio-temporal environment. To gain the knowledge, experience, and expertise necessary to solve this large-scale real-world problem, two preliminary research efforts were conducted. First, a simulated gaming platform known as Pong, originally created by ATARI, was used to demonstrate the effectiveness of a fully autonomous team to accomplish a desired task using a cascading Fuzzy system. With this knowledge, a simplified UTM system was developed to test a preliminary design of a fuzzy collision avoidance system. Once complete, this knowledge was used to develop the final UTM system platform capable of using intelligent separation assurance and collision avoidance techniques to mitigate the risk for Near Mid-Air Collisions between aircraft. This fuzzy solution utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. The collision avoidance system was tested in extreme conditions, including close proximity, high closure rates, and conservative maximum turn rates. In the preliminary homogenous case, the collision avoidance techniq (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Grant Schaffner Ph.D. (Committee Member); Gary Slater Ph.D. (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

The problem of landing a small spacecraft on the surface of an asteroid is analyzed in this thesis. The main effort of the thesis is focused around developing a fuzzy logic system to act as the controller. The fuzzy logic system is paired with a genetic algorithm to optimize the controller's membership functions. This optimized controller is then compared with two established controllers: an Optimal Control approach, and a Multiple Sliding-Surfaces Guidance algorithm. The genetic-fuzzy approach presented is applicable to designing controllers for various spacecraft and asteroid profiles.

Committee: Grant Schaffner Ph.D. (Committee Chair); Kelly Cohen Ph.D. (Committee Member); Elad Kivelevitch Ph.D. (Committee Member) Subjects: Aerospace Materials

PhD, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

In recent years, with the promise of extensive commercial applications, the popularity of Unmanned Aerial Vehicles (UAVs) has dramatically increased as witnessed by publications and mushrooming research and educational programs. Over the years, multi-copter aircraft have been chosen as a viable configuration for small-scale VTOL UAVs in the form of quad-copters, hexa-copters and octo-copters. Compared to the single main rotor configuration such as the conventional helicopter, multi-copter airframes require a simpler feedback control system and fewer mechanical parts. These characteristics make these UAV platforms, such as quad-copter which is the main emphasis in this dissertation, a rugged and competitive candidate for many applications in both military and civil areas. Because of its configuration and relative size, the small-scale quad-copter UAV system is inherently very unstable. In order to develop an effective control system through simulation techniques, obtaining an accurate dynamic model of a given quad-copter is imperative. Moreover, given the anticipated stringent safety requirements, fault tolerance will be a crucial component of UAV certification. Accurate dynamic modeling and control of this class of UAV is an enabling technology and is imperative for future commercial applications. In this work, the dynamic model of a quad-copter system in hover flight was identified using frequency-domain system identification techniques. A new and unique experimental system, data acquisition and processing procedure was developed catering specifically to the class of electric powered multi-copter UAV systems. The Comprehensive Identification from FrEquency Responses (CIFER®) software package, developed by US Army Aviation Development Directorate – AFDD, was utilized along with flight tests to develop dynamic models of the quad-copter system. A new set of flight tests were conducted and the predictive capability of the dynamic models were successfully val (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); George Black M.S. (Committee Member); Manish Kumar Ph.D. (Committee Member); Grant Schaffner Ph.D. (Committee Member); Bruce Walker Sc.D. (Committee Member) Subjects: Aerospace Materials

PhD, University of Cincinnati, 2015, Engineering and Applied Science: Aerospace Engineering

Fuzzy Logic Control is a powerful tool that has found great success in a variety of applications. This technique relies less on complex mathematics and more on "expert knowledge" of a system to bring about high-performance, resilient, and efficient control through linguistic classification of inputs and outputs and if-then rules. Genetic Fuzzy Systems (GFSs) remove the need of this expert knowledge and instead rely on a Genetic Algorithm (GA) and have similarly found great success. However, the combination of these methods suffer severely from scalability; the number of rules required to control the system increases exponentially with the number of states the inputs and outputs can take. Therefor GFSs have thus far not been applicable to complex, artificial intelligence type problems. The novel Genetic Fuzzy Tree (GFT) method breaks down complex problems hierarchically, makes sub-decisions when possible, and thus greatly reduces the burden on the GA. This development significantly changes the field of possible applications for GFSs. Within this study, this is demonstrated through applying this technique to a difficult air combat problem. Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) in the 2030 time-frame, it becomes apparent that the mission, flight, and ground controls will utilize the emerging paradigm of Intelligent Systems (IS); namely, the ability to learn, adapt, exhibit robustness in uncertain situations, “make sense” of the data collected in real-time and extrapolate when faced with scenarios significantly different from those used in training. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent controllers for these advanced unmanned craft as the first GFT. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs. Equipped with advanced sensors, a limited supply of Self-Defense Missiles (SDMs), (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Corey Schumacher Ph.D. (Committee Member); Elad Kivelevitch Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); Grant Schaffner Ph.D. (Committee Member) Subjects: Aerospace Materials