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

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

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 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

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

MS, University of Cincinnati, 2013, Engineering and Applied Science: Aerospace Engineering

The problem of magnetic attitude control of a CubeSat is analyzed. Three controller types are examined: a Constant-Gain Simple PD controller, a Linear Constant-Gain Optimal PD controller (i.e. an LQR), and a Fuzzy Gain-Scheduled PD controller. Each subsequent controller type utilizes a more-complex design algorithm. The Simple PD controller is tuned by hand iteration, the LQR is tuned using rule-of-thumb algorithms, and the Fuzzy Gain-Scheduled PD controller is designed using a Genetic Algorithm operating on two Fuzzy Inference Systems. Though the basic structures of these three controllers are identical, the differing design processes lead to different controller performance. The use of a Genetic-Fuzzy System is of particular interest, because this demonstrates the use of an intelligent algorithm to automate the controller design process. The techniques presented herein are directly applicable to any magnetically actuated satellite that can be modeled as a rigid body, although the mass distribution, geometry, and orbit of the satellite will determine controller-specific constants.

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

Master of Science in Engineering (MSEgr), Wright State University, 2010, Electrical Engineering

High power silicon controlled rectifiers (SCR) are used in the application of infrared radiation testing. A case study has been performed on a department of defense facility utilizing SCRs to transfer electrical energy to thermal energy. The facility is capable of generating up to 5000 °F across large cross-sectional areas, requiring tens of megawatts of power. The combination of high power, unbalanced loads, and SCR switching generate high harmonic disturbances that offer significant challenges for conventional linear control systems. In addition, unbalanced three-phase distribution systems are difficult to model, specifically during switching transients. Fuzzy logic is used to characterize the non-linear plant dynamics, control the system output, and reduce harmonics. Although the use of fuzzy logic for harmonic reduction has been used extensively in the power industry, most applications focus on compensating for harmonic disturbance rather than avoiding it. Harmonic compensation adds hardware in the system, which adds maintenance costs and inefficiency. This thesis introduces a technique to eliminate harmonic content in the control loop without adding additional hardware. A simulation of the system was created and fuzzy logic was used to characterize the behavior of the simulation. The simulation results demonstrated the non-linear control problem and identified key harmonic areas to avoid. A fuzzy proportional-integral controller along with a fuzzy harmonic reduction controller is implemented in this thesis to improve the control response while avoiding harmful harmonic interference. The fuzzy harmonic reduction controller yielded a hybrid pulse width modulation output that eliminated the most harmful harmonics while maintaining closed loop control.

Committee: Kuldip Rattan PhD (Advisor); Xiaodong Zhang PhD (Committee Member); Marian Kazimierczuk PhD (Committee Member) Subjects: Electrical Engineering

Master of Science in Engineering (MSEgr), Wright State University, 2009, Electrical Engineering

Modern electronics are operating at lower voltages with higher currents, requiring power converters to deliver these requirements efficiently. In this thesis, a switch-inductor buck converter is designed for the required specifications; and for the selected design, component losses and efficiency are calculated. A MATLAB/Simulink model is constructed, and tested in the presence of load and source disturbances to show the converter cannot maintain the desired output voltage when the disturbances are applied. A fuzzy logic PID controller is designed to regulate the duty cycle of the converter to control the output voltage. Control surfaces are designed for proportional, integral, and derivative gains of the fuzzy PID controller. The compensated power converter is tested using the Simulink model in the presence of the disturbances, and it is shown that the fuzzy controller is capable of keeping the power converter output voltage within the operating requirements, while improving system speed and stability.

Committee: Kuldip Rattan PhD (Advisor); Marian Kazimierczuk PhD (Committee Member); Xiaodong Zhang PhD (Committee Member); Kefu Xue PhD (Other); Joseph F. Thomas, Jr. PhD (Other) Subjects: Electrical Engineering

MS, University of Cincinnati, 2012, Engineering and Applied Science: Aerospace Engineering

The ever-increasing applications of UAV's have shown the great capabilities of these technologies. However, for many cases where one UAV is a powerful tool, an autonomous swarm all working cooperatively to the same goal presents amazing potential. Environment that are dangerous for humans, are either too small or too large for safe or reasonable exploration, and even those tasks that are simply boring or unpleasant are excellent areas for UAV swarm applications. In order to work cooperatively, the swarm must allocate tasks and have adequate path planning capability. This paper presents a methodology for two-dimensional target allocation and path planning of a UAV swarm using a hybridization of control techniques. Genetic algorithms, fuzzy logic, and to an extent, dynamic programming are utilized in this research to develop a code known as “UNCLE SCROOGE” (UNburdening through CLustering Effectively and Self-CROssover GEnetic algorithm). While initially examining the Traveling Salesman Problem, where an agent must visit each waypoint in a set once and then return home in the most efficient path, the work's end goal was a variant on this problem that more closely resembled the issues a UAV swarm would encounter. As an extension to Dr. Obenmeyer's “Polygon-Visiting Dubins Traveling Salesman Problem”, the Multi-Depot Polygon-Visiting Dubins Multiple Traveling Salesman Problem consists of a set number of visibility areas, or polygons that a number of UAV's, based in different or similar depot must visit. While this case is constant altitude and constant velocity, minimum turning radii are considered through the use of Dubins curves. UNCLE SCROOGE was found to be adaptable to the PVDTSP, where it competed well against the methods proposed by Obenmeyer. Due to limited benchmarking ability, as these are newly formed problems, Obenmeyer's work served as the only basis for comparison for the PVDTSP. UNCLE SCROOGE brought a 9.8% increase in accuracy, and a run-time reduction (open full item for complete abstract)

Committee: Kelly Cohen PhD (Committee Chair); Manish Kumar PhD (Committee Member); Bruce Walker ScD (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2011, Engineering and Applied Science: Aerospace Engineering

Fire is a natural agent of change for our planet's survival and has the capability to cause devastating effects (economical, societal, environmental, etc) when it encroaches into our daily lives. In the midst of a wildland fire, incident commanders are bombarded with massive amounts of data, accurate or not, and must make real-time decisions on how to allocate available resources to extinguish the fire with minimal damage. The scenario is modeled as an attacker-defender style game, such that the defender (resources with fire retardants) is protecting its assets (homes, businesses, power plants, etc) while the attacker (wildland fires) is attempting to deliver maximum destruction to those assets. The problem can be formulated in terms of optimal control theory, utilizing the gold standard of optimization, Dynamic Programming (DP), to exhaustively search the solution space for the minimized cost. However, its drawback is directly related to its method of finding the optimal solution: the exhaustive search. The amount of processing time to compute the minimum cost exponentially increases with the complexity of the system. For this reason, the DP approach is generally executed offline for real-world applications. Due to the large solution space of a wildland fire scenario, execution of DP offline is problematic as resource allocation decisions must be made in real-time. The current research effort seeks to show a new and unique control algorithm, based on Neuro-Fuzzy Dynamic Programming (NFDP), that can nearly replicate the DP algorithm results but can execute in real-time and remain robust to uncertainties. An artificial neural network provides the approximate cost-to-go function for the DP, fulfilling the need for real-time execution. The neural network is trained by approximate policy iteration using Monte Carlo simulations. Since our sensors may provide inaccurate or incomplete data of the environment, a fuzzy logic component is integrated to provide robustness in t (open full item for complete abstract)

Committee: Kelly Cohen PhD (Committee Chair); Manish Kumar PhD (Committee Member); Grant Schaffner PhD (Committee Member); Bruce Walker ScD (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy in Engineering, University of Toledo, 2011, College of Engineering

The technologies of hybridization of vehicles have been proven to significantly improve the fuel economy and reduce the environmental pollution. These technologies combine additional power sources with a traditional internal combustion engine. In some other modern vehicles, advanced cylinder management is the means to reduce fuel consumption and emissions. While these advanced technologies aim at energy savings and preserving the environment they create additional noise, vibration and harshness (NVH) problem. The noise, vibration and harshness problem has been a major area of research in the automotive industry. Vibration is the main cause for noise. With the advent of alternative energy and hybrid vehicles come new vibration problems and challenges that require nontraditional solutions. Semi-active vibration isolation devices are preferred to address the problem due to their effectiveness and affordability. A magnetorheological (MR) fluid mount can provide effective vibration isolation for applications such as hybrid vehicles. The MR fluid can produce different levels of damping when exposed to different levels of magnetic field. The fluid can be working in three modes which are the flow mode, shear mode and squeeze mode. A mixed mode MR fluid mount was designed to operate in a combination of the flow mode and the squeeze mode. Each of the working modes and the combined working mode has been studied. The mount's performance has been verified in simulation and experiments. The focus of the current study is on the design of a control system for the mixed mode MR fluid mount. Based on a model for the uni-axial MR mount a controller has been designed to achieve the lowest possible vibration transmissibility. Furthermore, the MR mount in two degree of freedom structure has been modeled. Displacement transmissibility and force transmissibility are considered in this scenario. It is desirable to minimize both transmissibilities. The controllers to achieve the lowest val (open full item for complete abstract)

Committee: Mohammad Elahinia PhD (Committee Chair); Mansoor Alam PhD (Committee Member); Mohamed Hefzy PhD (Committee Member); Ezzatollah Salari PhD (Committee Member); Mohsin Jamali PhD (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2005, Civil Engineering

A multi-paradigm nonparametric model, dynamic fuzzy wavelet neural network (WNN) model, is developed for structural system identification of three dimensional highrise buildings. The model integrates chaos theory (nonlinear dynamics theory), a signal processing method (wavelets), and two complementary soft computing methods (fuzzy logic and neural network). An adaptive Levenberg-Marquardt-least-squares learning algorithm is developed for adjusting parameters of the dynamic fuzzy WNN model. The methodology is applied to one five-story test frame and two highrise moment-resisting building structures. Results demonstrate that the methodology incorporates the imprecision existing in the sensor data effectively and balances the global and local influences of the training data. It therefore provides more accurate system identifications and nonlinear approximation with a fast training convergence. A nonparametric system identification-based model is developed for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy WNN model. The model does not require complete measurements of the dynamic responses of the whole structure. A damage evaluation method is proposed based on a power density spectrum method. The multiple signal classification method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using experimental data obtained for a 38-story concrete test model. It is demonstrated that the WNN model together with the pseudospectrum method is effective for damage detection of highrise buildings based on a small amount of sensed data. A nonlinear control model is developed for active control of highrise three dimensional building structures including geometrical and material nonlinearities, coupling action between lateral and torsional motions, and actuator dynamics. A dynamic fuzzy wavelet neuroemulator is developed for predicting the structural response in futur (open full item for complete abstract)

Committee: Hojjat Adeli (Advisor) Subjects: Engineering, Civil

Doctor of Philosophy (PhD), Ohio University, 1992, Electrical Engineering & Computer Science (Engineering and Technology)

The objective of this thesis is to develop new strategies for the control of uncertain systems. Two issues are considered here. The first issue is concerned with controller design based on a mathematical model with parameter variation. The second one is concerned with fuzzy controller design without using mathematical models. Both issues deal with different aspects of uncertainty. Robust control algorithms are developed to maintain the system performance comparable to that of a nominal model's, even when the real system parameters differ from the nominal model's. As an application, robust controller design for robot motion is studied. The system model (parameters) of the manipulator varies as the payload or the configuration changes. The parameters of the controller have to be updated at every sampling (operation) point to accommodate the changes in the system parameters. To reduce the amount of computation in updating the parameters of the controller, a robust eigenstructure assignment approach is introduced. Also, a new algorithm of robust pole assignment for multimode systems is presented here. Unlike conventional pole assignment, the desired pole position is not specified by n self-conjugated numbers but by a given disk in order to obtain more flexibility in choosing a feedback matrix to increase the robustness. An algorithm is proposed to find a feedback matrix to maintain the closed loop poles within the given disk even when the system works under different operating conditions. Another major issue considered here is fuzzy rule based controller design. The fuzzy control scheme, as originally advocated by Zadeh and Mamdani, is used as the means of both capturing human expertise and dealing with uncertainty about the system. This approach has been applied to many ill-defined and complex systems. In many cases, it achieved superior results over conventional controllers. However, in a rule-based fuzzy controller, the number of fuzzy rules (in a complete rule set) (open full item for complete abstract)

Committee: G. Raju (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 1994, Systems and Control Engineering

This work develops a new method for the implementation, synthesis and analysis of Fuzzy Logic Controllers (FLC). For a large class of practical problems this new method greatly reduces memory storage requirements and is significantly more computational efficient than existing methods for Fuzzy Logic Controllers implementations. Fuzzy PID controllers are included in this class. The key idea of this method is that only a small set of "active" rules which are needed at each time step and they can be generated, rather than stored. We call this new method the Active Cell Method, since each rule can be viewed as a "cell" in a table. The contributions of this research are both practical and analytical. The Active Cell Method provides a systematic methodology for the design, validation and analysis of Fuzzy Logic Controllers. The memory requirements and computational load are dramatically reduced in comparison with existing methods. In addition, controllers that are derived using the Active Cell Method can be analytically compared to Classical Controllers, which are used to crisp systems. In particular, the stability properties of Fuzzy Logic Controllers can be determined.

Committee: H. Chizeck (Advisor) Subjects: