UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Applied Aerial Autonomy for Reliable Indoor Flight and 3D Mapping(2024) Shastry, Animesh Kumar; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Uncrewed Aerial Systems (UAS) are essential for safely exploring indoor environments damaged by shelling, fire, floods, and structural collapse. These systems can gather critical visual and locational data, aiding in hazard assessment and rescue planning without risking human lives. Reliable UAS deployments requires advanced sensors and robust algorithms for real-time data processing and safe navigation, even in GPS-denied and windy conditions. This dissertation details three research projects to improve UAS performance: (1) in-flight calibration to improve estimation and control, (2) system identification for wind rejection, and (3) indoor aerial 3D mapping. The dissertation begins with introducing a comprehensive nonlinear filtering framework for UAV parameter estimation, which considers factors such as external wind, drag coefficients, IMU bias, and center of pressure. Additionally, it establishes optimized flight trajectories for parameter estimation through empirical observability. Moreover, an estimation and control framework is implemented, utilizing the mean of state and parameter estimates to generate suitable control inputs for vehicle actuators. By employing a square-root unscented Kalman filter (sq-UKF), this framework can derive a 23-dimensional state vector from 9-dimensional sensor data and 4-dimensional control inputs. Numerical results demonstrate enhanced tracking performance through the integration of the estimation framework with a conventional model-based controller. The estimation of unsteady winds results in improved gust rejection capabilities of the onboard controller as well. Closely related to parameter estimation is system identification. Combining with the previous work a comprehensive system identification framework with both linear offline and nonlinear online methods is introduced. Inertial parameters are estimated using frequency-domain linear system identification, incorporating control data from motor-speed sensing and state estimates from automated frequency sweep maneuvers. Additionally, drag-force coefficients and external wind are recursively estimated during flight using a sq-UKF. A custom flight controller is developed to manage the computational demands of online estimation and control. Flight experiments demonstrate the tracking performance of the nonlinear controller and its improved capability in rejecting gust disturbances. Aside from wind rejection, aerial indoor 3D mapping is also required for indoor navigation, and therefore, the dissertation introduces a comprehensive pipeline for real-time mapping and target detection in indoor environments with limited network access. Seeking a best-in-class UAS design, it provides detailed analysis and evaluation of both hardware and software components. Experimental testing across various indoor settings demonstrates the system's efficacy in producing high-quality maps and detecting targets.Item Mean-field Approaches in Multi-agent Systems: Learning and Control(2023) Tirumalai, Amoolya; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In many settings in physics, chemistry, biology, and sociology, when individuals (particles) interact in large collectives, they begin to behave in emergent ways. This is to say that their collective behavior is altogether different from their individual behavior. In physics and chemistry, particles interact through the various forces, and this results in the rich behavior of the phases of matter. A particularly interesting case arises in the dynamics of gaseous star formation. In models of star formation, the gases are subject to the attractive gravitational force, and perhaps viscosity, electromagnetism, or thermal fluctuations. Depending on initial conditions, and inclusion of additional forces in the models, a variety of interesting configurations can arise, from dense nodules of gas to swirling vortices. In biology and sociology, these interactions (forces) can be explicitly tied to chemical or physical phenomena, as in the case of microbial chemotaxis, or they can be more abstract or virtual, as in the case of bird flocking or human pedestrian traffic. We focus on the latter cases in this work. In collective animal or human traffic, we do not say that animals or humans are explicity subject to physical forces that causes them to move in alignment with each other, or whatever else. Rather, they behave as if there were such forces. In short, we use the language and notation of physics and forces as a convenient tool to build our understanding. We do so since natural phenomena are rich with sophisticated and adaptive behavior. Bird flocks rapidly adapt to avoid collisions, to fly around obstacles, and to confuse predators. Engineers today can only dream of building drone swarms with such plasticity. An important question to answer is how one takes a model of interacting individuals and builds a model of a collective. Once one answers this question, another immediately follows: how do we take these models of collectives and use them to discover representations of natural phenomena? Then, can we use these models to build methods to control such phenomena, assuming suitable actuation? Once these questions are answered, our understanding of collective dynamics will improve, broadening the applications we can tackle. In this thesis, we study collective dynamics via mean-field theory. In mean-field theory, an individual is totally anonymous, and so can be removed or permuted from a large collective without changing the collective dynamics significantly. More specifically, when any individual is excluded from the definition of the empirical measure of all the individuals, those empirical measures converge to the same measure, termed the mean-field measure. The mean-field measure is governed by the forward Kolmogorov equation. In certain scenarios where an analogy can be drawn to particle dynamics, these forward Kolmogorov equations can be converted to compressible Euler equations. When optimal control problems are posed on the particle dynamics, in the mean-field limit we obtain a forward Kolmogorov equation coupled to a backward Hamilton-Jacobi-Bellman (-Isaacs) equation (or a stationary analogue of these). This system of equations describes the solution to the mean-field game. The first two problems we explore in this thesis are focused on the system identification (inverse) problem: discover a model of collective dynamics from data. In these problems, we study a generalized hydrodynamic Cucker-Smale-type model of flocking in a bounded region of 3D space. We first prove existence of weak bounded energy solutions and a weak-strong uniqueness principle for our model. Then, we use the model to learn a representation of the dynamics of data associated to a synthetic bird flock. The second two problems we study focus on the control (forward) problem: learn an approximately optimal control for collective dynamics online. We study this first in a relatively simple state-and-control-constrained mean-field game on traffic. In this case, the mean-field term is contained only in the mean-field game's cost. We first numerically study a finite horizon version of this problem. The approach for the first problem is not online. Then, we take an infinite horizon version, and we form a system of approximate dynamic programming ODE-PDEs from the exact dynamic programming PDEs. This approach results in online learning and adapting of the control to the dynamics. We prove this ODE-PDE system has a unique weak solution via semigroup and successive approximation methods. We present a numerical example, and discuss the tradeoffs in this approach. We conclude the thesis by summarizing our results, and discussing future directions and applications in theoretical and practical settings.Item Control and Stabilization of Soft Inverted Pendulum on a Cart(2023) Ajithkumar, Ananth; Chopra, Nikhil Dr.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Underactuated systems are systems that cannot be controlled to track any arbitrary trajectories in their configuration space. In this work, we introduce a novel soft-robotic pendulum on a cart system. This is an underactuated soft-robotic system with two degrees of under-actuation. We model the system, derive the kinematics, and motivated by the control strategies for classical underactuated systems, we study the swing-up control and stabilization of this system around the vertical equilibrium point. The switching-based control law uses an energy-based control for swing-up and LQR for stabilization once the system is within the region of attraction of LQR. The simulation results depict the efficaciousness of the developed control scheme. Further, in this thesis, we discuss the viability and feasibility of feedback linearization, partially feedback-linearize the system, and analyze the zero dynamics of the system.Item Distributed Control for Formula SAE-Type Electric Vehicle(2022) Falco, Samantha Rose; Khaligh, Alireza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The recent trend in transportation electrification creates an enormous increase in demand for electric vehicles (EVs). Increasingly, electric cars have novel features like autonomous driving and fault tolerance, all of which require additional hardware and computation power. Changes to the electronic control unit (ECU) structure will be needed to make these advances scalable. This thesis examines the driving economic, technical, and societal factors behind needed changes to the existing control structures. It proposes a control platform design to address issues of complexity and scalability. A generic, modular control board structure using the TMS320F2837xS digital signal processor (DSP) is described with several input/output functionalities including a wide range of analog inputs, multiple logic levels for digital pins, CAN communication, and wireless communication capabilities. A distributed control network is built by interconnecting multiple implementations of the control board, each of which has distinct responsibilities dictated by software instead of hardware. A prototype electric vehicle control structure for a Formula SAE electric vehicle was built utilizing a network of three control boards and tested to prove the viability of the proposed concept. Results of these tests and future steps for the project are discussed.Item ESTIMATION AND CONTROL OF NONLINEAR SYSTEMS: MODEL-BASED AND MODEL-FREE APPROACHES(2020) Goswami, Debdipta; Paley, Derek A.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)State estimation and subsequent controller design for a general nonlinear system is an important problem that have been studied over the past decades. Many applications, e.g., atmospheric and oceanic sampling or lift control of an airfoil, display strongly nonlinear dynamics with very high dimensionality. Some of these applications use smaller underwater or aerial sensing platforms with insufficient on-board computation power to use a Monte-Carlo approach of particle filters. Hence, they need a computationally efficient filtering method for state-estimation without a severe penalty on the performance. On the other hand, the difficulty of obtaining a reliable model of the underlying system, e.g., a high-dimensional fluid dynamical environment or vehicle flow in a complex traffic network, calls for the design of a data-driven estimation and controller when abundant measurements are present from a variety of sensors. This dissertation places these problems in two broad categories: model-based and model-free estimation and output feedback. In the first part of the dissertation, a semi-parametric method with Gaussian mixture model (GMM) is used to approximate the unknown density of states. Then a Kalman filter and its nonlinear variants are employed to propagate and update each Gaussian mode with a Bayesian update rule. The linear observation model permits a Kalman filter covariance update for each Gaussian mode. The estimation error is shown to be stochastically bounded and this is illustrated numerically. The estimate is used in an observer-based feedback control to stabilize a general closed-loop system. A transferoperator- based approach is then proposed for the motion update for Bayesian filtering of a nonlinear system. A finite-dimensional approximation of the Perron-Frobenius (PF) operator yields a method called constrained Ulam dynamic mode decomposition (CUDMD). This algorithm is applied for output feedback of a pitching airfoil in unsteady flow. For the second part, an echo-state network (ESN) based approach equipped with an ensemble Kalman filter is proposed for data-driven estimation of a nonlinear system from a time series. A random reservoir of recurrent neural connections with the echo-state property (ESP) is trained from a time-series data. It is then used as a model-predictor for an ensemble Kalman filter for sparse estimation. The proposed data-driven estimation method is applied to predict the traffic flow from a set of mobility data of the UMD campus. A data-driven model-identification and controller design is also developed for control-affine nonlinear systems that are ubiquitous in several aerospace applications. We seek to find an approximate linear/bilinear representation of these nonlinear systems from data using the extended dynamic mode decomposition algorithm (EDMD) and apply Liealgebraic methods to analyze the controllability and design a controller. The proposed method utilizes the Koopman canonical transform (KCT) to approximate the dynamics into a bilinear system (Koopman bilinear form) under certain assumptions. The accuracy of this approximation is then analytically justified with the universal approximation property of the Koopman eigenfunctions. The resulting bilinear system is then subjected to controllability analysis using the Myhill semigroup and Lie algebraic structures, and a fixed endpoint optimal controller is designed using the Pontryagin’s principle.Item DYNAMICS AND CONTROL OF AN ELASTIC ROD IN AIR AND WATER(2019) Burch, Travis Taylor; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis investigates the modeling and control of bio-inspired flexible structures for robotics applications. Many animals move through complicated natural environments and perform complex tasks by exploiting soft structures. Soft structures are highly versatile and are a growing area of interest in robotics because they can have decreased weight, size, and mechanical complexity relative to more traditional rigid robotics. This work uses planar discrete elastic rod (PDER) theory for modeling two types of flexible structures. First, a flexible airfoil is modeled using PDER theory, including the Improved Lighthill model (ILM) of hydrodynamic forces to study the propulsion thrust. The propulsion thrust generated by rigid and flexible foils are also measured experimentally and compared to the model. Second, a state-space description of a flexible pendulum with torque input is presented. Linear state-and output-feedback hybrid controllers stabilize the inverted flexible pendulum starting from the down equilibrium.Item Dynamics and Control of a Hovering Quadrotor in Wind(2019) Craig, William Stephen; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quadrotor helicopters show great promise for a variety of missions in commercial, military, and recreational domains. Many of these missions require flight outdoors where quadrotors struggle, partially due to their high susceptibility to wind gusts. This dissertation addresses the problem of quadrotor flight in wind with (1) a physics-based analysis of the interaction between the wind and the quadrotor, (2) the addition of flow sensing onboard the quadrotor to measure external wind, and (3) both linear and nonlinear control development incorporating flow sensing and taking aerodynamic interactions into account. Using flow measurements in addition to traditional IMU sensing enables the quadrotor to react to the wind directly, rather than delaying until the wind affects the rigid-body dynamics as with IMU sensing alone. The aerodynamic response of a quadrotor to wind is modeled using blade flapping, which characterizes the tilt of the rotor plane a result of uneven lift on the blades. The model is validated by mounting a motor and propeller to a spherical pendulum and subjecting it to a wind gust. The blade-flapping model is utilized in a nonlinear geometric feedback-linearization controller that is built in a cascaded framework, first developing the inner-loop attitude controller, then the outer-loop position controller. The controller directly cancels the forces and moments resulting from aerodynamic disturbances using measurements from onboard flow probes, and also includes a variable-gain algorithm to address the inherent thrust limitations on the motors. A linear model and controller is also developed, using frequency-domain system-identification techniques to characterize the model, and handling-qualities-criteria based optimization to select gains. A linear model of the aerodynamic interactions, based on the blade-flapping work, provides flow-feedback capability similar to the nonlinear controller. Experimental testing is performed for each of the developed controllers, all of which show improvement through the use of flow feedback. Attitude is tested independently by mounting the quadrotor on a ball-joint, allowing for both gust and saturation testing. Gust rejection is also tested for both linear and nonlinear controllers in free flight, showing further benefits than considering attitude alone.Item Identification of State-Space Rotor Wake Models with Application to Coaxial Rotorcraft Flight Dynamics and Control(2019) Hersey, Sean Patrick; Celi, Roberto; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Modern aerodynamic analysis tools, such as free-vortex wake models and CFD-based techniques, include fewer theoretical limitations and approximations than classical simplified schemes, and represent the state-of-the-art in rotorcraft aerodynamic modeling, including for coaxial and other advanced configurations. However, they are impractical or impossible to apply to many flight dynamics problems because they are not formulated in ordinary differential equation (ODE) form, and they are often computationally intensive. Inflow models, for any configuration type, that couple the accuracy of high-fidelity aerodynamic models with the simplicity and ODE form of dynamic inflow-type theories would be an important contribution to the field of flight dynamics and control. This dissertation presents the methodology for the extraction of linearized ODE models from computed inflow data acquired from detailed aerodynamic free-vortex wake models, using frequency domain system identification. These methods are very general and applicable to any aerodynamic model, and are first demonstrated with a free wake model in hover and forward flight, for a single main rotor, and subsequently for the prediction of induced flow off the rotor as well, at locations such as the tail or fuselage. The methods are then applied to the extraction of first order linearized ODE inflow models for a coaxial rotor in hover. Subsequent analysis concluded that free-vortex wake models show that the behavior of the inflow of a coaxial configuration may be higher-order. Also, tip-path plane motion of a coaxial rotor causes wake distortion which has an impact on the inflow behavior. Therefore, the methodology is expanded to the identification of a second order inflow representation which is shown to better capture from all of the relevant dynamics from free-vortex wake models, including wake distortion. With ODE models of inflow defined for an advanced coaxial configuration, this dissertation then presents a comparison of the fully-coupled aircraft flight dynamics, and the design of an explicit modeling-following feedback controller, with both a free-vortex wake identified model and a momentum theory based approach, concluding that accurate inflow modeling of coaxial rotor inflow is essential for investigation into the flight dynamics and control design of advanced rotor configurations.Item Optimality of Event-Based Policies for Decentralized Estimation over Shared Networks(2016) Vasconcelos, Marcos Muller; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cyber-physical systems often consist of multiple non-collocated components that sense, exchange information and act as a team through a network. Although this new paradigm provides convenience, flexibility and robustness to modern systems, design methods to achieve optimal performance are elusive as they must account for certain detrimental characteristics of the underlying network. These include constrained connectivity among agents, rate-limited communication links, physical noise at the antennas, packet drops and interference. We propose a new class of problems in optimal networked estimation where multiple sensors operating as a team communicate their measurements to a fusion center over an interference prone network modeled by a collision channel. Using a team decision theoretic approach, we characterize jointly optimal communication policies for one-shot problems under different performance criteria. First we study the problem of estimating two independent continuous random variables observed by two different sensors communicating with a fusion center over a collision channel. For a minimum mean squared estimation error criterion, we show that there exist team-optimal strategies where each sensor uses a threshold policy. This result is independent of the distribution of the observations and, can be extended to vector observations and to any number of sensors. Consequently, the existence of team-optimal threshold policies is a result of practical significance, because it can be applied to a wide class of systems without requiring collision avoidance protocols. Next we study the problem of estimating independent discrete random variables over a collision channel. Using two different criteria involving the probability of estimation error, we show the existence of team-optimal strategies where the sensors either transmit all but the most likely observation; transmit only the second most likely observation; or remain always silent. These results are also independent of the distributions and are valid for any number of sensors. In our analysis, the proof of the structural result involves the minimization of a concave functional, which is an evidence of the inherent complexity of team decision problems with nonclassical information structure. In the last part of the dissertation, the assumption on the cooperation among sensors is relaxed, and we show that similar structural results can also be obtained for systems with one or more selfish sensors. Finally the assumption of the independence is lifted by introducing the observation of a common random variable in addition to the private observations of each sensor. The structural result obtained provides valuable insights on the characterization of team-optimal policies for a general correlation structure between the observed random variables.Item Combustion Instability and Active Control: Alternative Fuels, Augmentors, and Modeling Heat Release(2016) Park, Sammy; Yu, Kenneth H; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Experimental and analytical studies were conducted to explore thermo-acoustic coupling during the onset of combustion instability in various air-breathing combustor configurations. These include a laboratory-scale 200-kW dump combustor and a 100-kW augmentor featuring a v-gutter flame holder. They were used to simulate main combustion chambers and afterburners in aero engines, respectively. The three primary themes of this work includes: 1) modeling heat release fluctuations for stability analysis, 2) conducting active combustion control with alternative fuels, and 3) demonstrating practical active control for augmentor instability suppression. The phenomenon of combustion instabilities remains an unsolved problem in propulsion engines, mainly because of the difficulty in predicting the fluctuating component of heat release without extensive testing. A hybrid model was developed to describe both the temporal and spatial variations in dynamic heat release, using a separation of variables approach that requires only a limited amount of experimental data. The use of sinusoidal basis functions further reduced the amount of data required. When the mean heat release behavior is known, the only experimental data needed for detailed stability analysis is one instantaneous picture of heat release at the peak pressure phase. This model was successfully tested in the dump combustor experiments, reproducing the correct sign of the overall Rayleigh index as well as the remarkably accurate spatial distribution pattern of fluctuating heat release. Active combustion control was explored for fuel-flexible combustor operation using twelve different jet fuels including bio-synthetic and Fischer-Tropsch types. Analysis done using an actuated spray combustion model revealed that the combustion response times of these fuels were similar. Combined with experimental spray characterizations, this suggested that controller performance should remain effective with various alternative fuels. Active control experiments validated this analysis while demonstrating 50-70\% reduction in the peak spectral amplitude. A new model augmentor was built and tested for combustion dynamics using schlieren and chemiluminescence techniques. Novel active control techniques including pulsed air injection were implemented and the results were compared with the pulsed fuel injection approach. The pulsed injection of secondary air worked just as effectively for suppressing the augmentor instability, setting up the possibility of more efficient actuation strategy.