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, 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, 2024, Engineering and Applied Science: Aerospace Engineering

The integration of AI in autonomous vehicles has been rapidly expanding and has the potential to raise concerns about non-compliant or malicious actors. Predicting movements or strategies of these actors could provide a substantial advantage in the mitigation of such threats. In a simulated asteroids style game, capture of these actors closely resembles pursuit evasion problems in differential games. In this work, multiple evader control methods are mapped by an adaptable fuzzy modified potential field avoidance method trained via genetic algorithm. Evader routes are integrated and optimal interception points are determined by numerical methods or a fuzzy logic approach. Time delayed mines are then placed at the interception point to eliminate the evader. The fuzzy modified potential field has also been separately trained to produce highly effective avoidance within congested asteroid environments.

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 Engineering

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

Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through various pairwise conflict scenarios as well as a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space. It was found that the overall number of collisions decreased by an average of 98% using the proposed optimized algorithm, thereby, supporting its effectiveness.

Committee: Donghoon Kim Ph.D. (Committee Chair); Daegyun Choi Ph.D. (Committee Member); 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, 2021, Engineering and Applied Science: Aerospace Engineering

Multi-drone slung payload systems that do not rely upon a leader for control instruction are a highly resilient option for critical delivery missions. In comparison to traditional control and modern deep-learning approaches, fuzzy systems are uniquely suited to balancing highly complex behaviors and high user readability post-training. By properly categorizing, training, and stacking purpose-driven fuzzy inference system (FIS) modules with separate cost functions or summed cost function components, unique behaviors can be developed and combined to form a powerful overall controller. In general, these have been referred to as genetic fuzzy trees (GFTs). Though technically the GFT proposed in this paper is a parallel set of four primary behaviors, the core program developed as a result of this research can and will easily add more tree-like series FIS modules to improve the responsiveness of the system to physical parameters. As a furthering of research in the field of genetic fuzzy decentralized control tasks, an early solution is explored for full three-dimensional point-mass control of a payload with neighbor avoidance safety and basic linear targeting behaviors. Drone teams of nominal counts (three to four drones depending on training task) are attached to a point mass payload by identical cables and are provided with visually obtainable state information. Isolated trainings build behaviors in tasks including cable pitch, altitude, cable yaw, and planar navigation, with a focus on visual representation of learned behaviors via fuzzy rule base surface plots. In response to difficulties experienced in setup of prior two-dimensional tests, a highly customizable object-oriented architecture dubbed GRAFT (Genetic ReArrangeable Fuzzy Tuner) for both simulation trial and fuzzy inference system creation has been developed and is explored briefly as a vital tool for future hand-built multi-inference system fuzzy controllers. This design (open full item for complete abstract)

Committee: Ou Ma Ph.D. (Committee Chair); Kelly Cohen Ph.D. (Committee Member); Catharine McGhan Ph.D. (Committee Member); Anoop Sathyan PhD (Committee Member) Subjects: Aerospace Materials

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

PhD, University of Cincinnati, 2017, Engineering and Applied Science: Aerospace Engineering

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire dete (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Franck Cazaurang Ph.D. (Committee Member); Nicholas C. Ernest Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2016, Engineering and Applied Science: Aerospace Engineering

In recent years, there have been huge advances in controllers used in autonomous systems. One particular area of research is Fuzzy Logic Controllers (FLCs) that are trained by both expert knowledge-based optimization algorithms such as Genetic Algorithms (GAs). GAs represent a particularly powerful way to optimize a FLC as they often perform better than other search algorithms when the state space is rather complex. A downside to these systems however is that their functionality is difficult to verify due to highly non-linear behavior. Typical verification involves Monte Carlo type simulations that require time to perform and may miss critical test points. Therefore, there is a need for more formal ways of verifying the correctness of FLCs based on specifications set forth for their operation over the entire state space. In this work, a method of converting a 2-input 1-output FLC with specific constraints into a Piecewise Affine Hybrid System (PWAHS) is extended to a 3-input 1-output case for implementation of a cascaded FLC system. Verification of safety properties of the converted PWAHS using Formal Methods tools is then conducted. FLCs were then created for a case study involving both one and two degree-of-freedom inverted pendulum systems and trained with Genetic Algorithms. Specifications about their functionality were derived from expert knowledge of the system and simulation results. The specifications were translated into temporal logic specifications that could then be checked by Symbolic Model Checkers (SMCs) utilizing Satisfiability Modulo Theories (SMT) solvers. It is shown that the FLCs created are shown to meet the requirements set forth, and in cases where they do not, generated counterexamples give a point in the state space where they are violated. FLCs that violated the properties were then re-trained and checked again using specifications derived from simulation results. The final FLC was shown to meet all safety specifications under all condition (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Matthew A. Clark M.S. (Committee Member); Nicholas D. Ernest Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Aerospace Materials

PhD, University of Cincinnati, 2016, Engineering and Applied Science: Aerospace Engineering

The National Air Space (NAS) can be easily described as a complex aviation system-of-systems that seamlessly works in harmony to provide safe transit for all aircraft within its domain. The number of aircraft within the NAS is growing and according the FAA, ``[o]n any given day, more than 85,000 flights are in the skies in the United States...This translates into roughly 5,000 planes in the skies above the United States at any given moment. More than 15,000 federal air traffic controllers in airport traffic control towers, terminal radar approach control facilities and air route traffic control centers guide pilots through the system''. The FAA is currently rolling out the Next Generation Air Transportation System (NextGen) to handle projected growth while leveraging satellite-based navigation for improved tracking. A key component to instantiating NextGen lies in the equipage of Automatic Dependent Surveillance-Broadcast (ADS-B), a performance based surveillance technology that uses GPS navigation for more precise positioning than radars providing increased situational awareness to air traffic controllers. Furthermore, the FAA is integrating UAS into the NAS, further congesting the airways and information load on air traffic controllers. The expected increase in aircraft density due to NextGen implementation and UAS integration will require innovative algorithms to cope with the increase data flow and to support air traffic controllers in their decision-making. This research presents a few innovative algorithms to support increased aircraft density and UAS integration into the NAS. First, it is imperative that individual tracks are correlated prior to fusing to ensure a proper picture of the environment is correct. However, current approaches do not scale well as the number of targets and sensors are increased. This work presents a fuzzy clustering design to hierarchically break the problem down into smaller subspaces prior to correlation. This approach provide (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Sundararaman Anand Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Bruce Walker Sc.D. (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2016, Engineering and Applied Science: Aerospace Engineering

There is growing interest in the effectiveness of emulating human decision making and learning in modern aerospace applications. The following thesis is an examination of several applications in which cascading type 1 and 2 fuzzy logic has been utilized in artificial intelligence and machine learning problems to demonstrate its capabilities. In Fuzzy Logic Inferencing for PONG (FLIP), the effectiveness of cascading type 1 logic is examined as an optimal controller for players in the game of PONG. Robotic collaboration is also developed as the PONG game was expanded into a multi-player option. Precision Route Optimization using Fuzzy Intelligence (PROFIT) examines the use of fuzzy logic as an optimizer in a cascaded algorithmic solution to a modified Traveling Salesman Problem (TSP). The TSP is modified in a way to better mimic a real-life scenario where footprints must be visited instead of simply points, which gives an interesting complexity to the problem. Collaborative Learning using Fuzzy Inferencing (CLIFF) is an extension of the PONG game introduced in FLIP, however a type-2 fuzzy logic toolbox is developed for potential use in development of a robotic coach that could optimize its players to beat an opponent in an application of layered fuzzy learning. Considering the successes associated with these research endeavors, it can be concluded that cascading type 1 and 2 fuzzy logic are both interesting tools that can further the abilities of intelligent systems and machine learning algorithms.

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

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

In recent times, there have been a growing number of aerospace application utilizing “Intelligent Systems” methods. These methods include Genetic Algorithms and Fuzzy Logic. Genetic Algorithms were used as an optimization tool for morphing wing airfoils and to optimize Fuzzy Logic path planning algorithms. Fuzzy Logic was used to guide an agent through a hazard field while minimizing exposure to the hazard. Additionally, advanced statistical techniques such as Proper Orthogonal Decomposition were used in the investigation of creating low-order models of supersonic cavity flows, and attempting to remove noise (such as smoke) from video taken during fire-fighting operations. The main goal of this research was take these intelligent systems methods and utilize them to develop user-friendly software applications which can be used by undergraduate and graduate students. It is for this reason that the source code is being included in this thesis, to allow future students to utilize and build upon these applications. AeroMorph provides the user with a graphical interface which can be used to optimize airfoils shapes for a given flight scenario. It uses XFOIL as a virtual wind tunnel to provide quick results which are then optimized using the genetic algorithm. Then the optimized airfoil can be tested against the original to see the improvement. GAPPER provides graphical tools to build hazard maps and solve them using different Fuzzy Logic path-planning routines. The user can use a genetic algorithm to optimize a path-planning routine and then the user can directly control the agent to benchmark the path-planning routine against a human. ssPOD provides a graphical environment in which Proper Orthogonal Decomposition can be performed on a variety of different problems. It quickly and efficiently provides results for a number of useful variables, and includes plotting tools to make communication of results easy. The MATLAB programs were benchmarked to determine (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Shaaban Abdallah Ph.D. (Committee Member); Awatef Hamed Ph.D. (Committee Member); Manish Kumar 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

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, 2010, Engineering and Applied Science: Aerospace Engineering

In this study, a fuel system controller for a gas turbine engine was examined. Controller design in this application is challenging due to nonlinearities in the closed loop system, as well as uncertainties associated with hardware components from part variation or degradation. Current closed loop design methodologies are discussed, as are the limitations or challenges facing these systems. Details on fuzzy logic control and its benefits in this type of application are explored. Information on genetic algorithms is presented, along with a study on how this optimization approach can be utilized to enhance the fuzzy logic controller process. A fuzzy logic controller structure was developed for providing closed loop fuel control in the gas turbine application, using a genetic algorithm to tune the system to provide an accurate and fast response to changing input demands. With a genetic fuzzy controller in place, closed loop analysis was performed, along with a stochastic robustness analysis to assess controller performance in an uncertain environment. Results show that the genetic fuzzy system performed well in this application, resulting in a system with fast rise and settling times to stepping inputs, while also minimizing overshoot and steady state error. Robustness characteristics of the fuzzy controller were also demonstrated, as the stochastic robustness analysis yielded acceptable performance in each simulation of the closed loop system with uncertainties included.

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

Master of Science in Engineering, University of Toledo, 2012, College of Engineering

In this thesis an Intelligent Energy Management System (EMS) for end consumer has been proposed. This system develops an algorithm for smart meter which is integrated between distribution grid and end consumers. The smart meter determines when to draw the energy from the grid or the storage unit for consumption. The first objective of the intelligent EMS is to save the cost for consumers by shifting the power drawn from the grid from high cost period to low cost period. The second objective of the intelligent EMS is to avoid grid overload by shifting the power drawn from the grid from high demand period to low demand period. The algorithm takes into consideration the hourly price and load demand of the grid. The algorithm was tested with the real data collected by ISO New England for the six states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island and Vermont, during the period of Jan 1, 2011 to Dec 31, 2011. Two approaches based on Fuzzy Logic and Genetic Algorithm (GA) were used. It was demonstrated the GA based approach outperformed the Fuzzy Logic based approach. The intelligent approach based on GA resulted in more cost saving as compared to what was theoretically foreseen and predicted.

Committee: Dr. Devinder Kaur PhD (Committee Chair); Dr. Ezzatollah Salari PhD (Committee Member); Dr. Mansoor Alam PhD (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering; Energy; 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