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 Dynamics and Control of Bioinspired Swimming, Schooling, and Pursuit(2023) Thompson, Anthony Allan; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Understanding the benefits of the behaviors of aquatic animals can improve the capabilities of robotic systems. Aquatic species such as the zebrafish swim with discrete motions that alternate between perception and action while avoiding predators and swimming in schools, and other species such as the lionfish use their pectoral fins to herd and trap prey. This work seeks to model these bioinspired behaviors (i.e., schooling, swimming with intermittent sensing and actuation, and pursuit and evasion in a structured environment) and enhance our understanding of their benefits. A hybrid dynamic model is derived with two phases; namely a burst phase during which each particle applies a control input and a coast phase during which each particle performs state estimation. This model provides a way to investigate how having non-overlapping sensing and control affects a multi-agent system's ability to achieve collective behavior such as steering to some desired direction. By evaluating the stability properties of the equilibrium points for the collective behavior, investigators can determine parameter values that exhibit exponentially stable behavior. Aside from swimming intermittently, fish also need to avoid predators. Inspired by observations of predation attempts by lionfish (Pterois sp.), a pursuit-evasion game is derived in a bounded environment to study the interaction of an advanced predator and an intermittently steering prey. The predator tracks the prey with a pure-pursuit strategy while using a bioinspired tactic to minimize the evader's escape routes, i.e, to trap the prey. Specifically, the predator employs symmetric appendages inspired by the large pectoral fins of lionfish, but this expansion increases its drag. The prey employs a bioinspired randomly-directed escape strategy to avoid capture and collisions with the boundary known as the protean strategy. This game investigates the predator's trade-off of minimizing the work to capture the prey and minimizing the prey's escape routes. Using the predator's expected work to capture as a cost function determines when the predator should expand its appendages as a function of the relative distance to the evader and the evader's proximity to the boundary. Prey fish also swim in schools to protect themselves from predators. To drive a school of fish robots into a parallel formation, a nonlinear steering controller is derived and implemented on a robotic fish platform. These robotic fish are actuated with an internal reaction wheel driven by a DC motor. Implementation of the proposed parallel formation control law on an actual school of soft robotic fish is described, including system identification experiments to identify motor dynamics and the design of a motor torque-tracking controller to follow the formation torque control. Experimental results demonstrate a school of four robotic fish achieving parallel formations starting from random initial conditions.Item Extensions of the Kuramoto model: from spiking neurons to swarming drones(2020) Chandra, Sarthak; Girvan, Michelle; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Kuramoto model (KM) was initially proposed by Yoshiki Kuramoto in 1975 to model the dynamics of large populations of weakly coupled phase oscillators. Since then, the KM has proved to be a paradigmatic model, demonstrating dynamics that are complex enough to model a wide variety of nontrivial phenomena while remaining simple enough for detailed mathematical analyses. However, as a result of the mathematical simplifications in the construction of the model, the utility of the KM is somewhat restricted in its usual form. In this thesis we discuss extensions of the KM that allow it to be utilized in a wide variety of physical and biological problems. First, we discuss an extension of the KM that describes the dynamics of theta neurons, i.e., quadratic-integrate-and-fire neurons. In particular, we study networks of such neurons and derive a mean-field description of the collective neuronal dynamics and the effects of different network topologies on these dynamics. This mean-field description is achieved via an analytic dimensionality reduction of the network dynamics that allows for an efficient characterization of the system attractors and their dependence not only on the degree distribution but also on the degree correlations. Then, motivated by applications of the KM to the alignment of members in a two-dimensional swarm, we construct a Generalized Kuramoto Model (GKM) that extends the KM to arbitrary dimensions. Like the KM, the GKM in even dimensions continues to demonstrate a transition to coherence at a positive critical coupling strength. However, in odd dimensions the transition to coherence occurs discontinuously as the coupling strength is increased through 0. In contrast to the unique stable incoherent equilibrium for the KM, we find that for even dimensions larger than 2 the GKM displays a continuum of different possible pretransition incoherent equilibria, each with distinct stability properties, leading to a novel phenomenon, which we call `Instability-Mediated Resetting.' To aid the analysis of such systems, we construct an exact dimensionality reduction technique with applicability to not only the GKM, but also other similar systems with high-dimensional agents beyond the GKM.