Aerospace Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2737
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Item Bioinspired sensing and control for underwater pursuit(2019) Free, Brian Anderson; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fish in nature have several distinct advantages over traditional propeller driven underwater vehicles including maneuverability and flow sensing capabilities. Taking inspiration from biology, this work seeks to answer three questions related to bioinspired pursuit and apply the knowledge gained therein to the control of a novel, reaction-wheel driven autonomous fish robot. Which factors are most important to a successful pursuit? How might we guarantee capture with underwater pursuit? How might we track the wake of a flapping fish or vehicle? A technique called probabilistic analytical modeling (PAM) is developed and illustrated by the interactions between predator and prey fish in two case studies that draw on recent experiments. The technique provides a method for investigators to analyze kinematics time series of pursuit to determine which parameters (e.g. speed, flush distance, and escape angles) have the greatest impact on metrics such as probability of survival. Providing theoretical guarantees of capture become complicated in the case of a swimming fish or bioinspired fish robot because of the oscillatory nature fish motion. A feedback control law is shown to result in forward swimming motion in a desired direction. Analysis of this law in a pursuit scenario yields a condition stating whether capture is guaranteed provided some basic information about the motion of the prey. To address wake tracking inspiration is taken from the lateral line sensing organ in fish, which is sensitive to hydrodynamic forces in the local flow field. In experiment, an array of pressure sensors on a Joukowski foil estimates and controls flow-relative position in a Karman vortex street using potential flow theory, recursive Bayesian filtering, and trajectory-tracking, feedback control. The work in this dissertation pushes the state of the art in bioinspired underwater vehicles closer to what can be found in nature. A modeling technique provides a means to determine what is most important to pursuit when designing a vehicle, analysis of a control law shows that a robotic fish is capable of pursuit engagements with capture guarantees, and an estimation framework demonstrates how the wake of a swimming fish or obstacle in the flow can be tracked.Item Analysis of the Stochastic Stability and Asymptotically Stationary Statistics for a Class of Nonlinear Attitude Estimation Algorithms(2018) Galante, Joseph Marshall; Sanner, Robert M; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Attitude estimation algorithms are critical components of satellite control systems, aircraft autopilots, and other applications. Attitude estimation systems perform their task by fusing attitude and gyroscope measurements; however, such measurements are typically corrupted by random noise and gyroscopes may have significant bias. Variations of the extended Kalman filter are commonly used, but this technique relies on instantaneous linearization of the underlying nonlinear dynamics and global stability cannot be guaranteed. Nonlinear attitude observers with guaranteed global stability have been derived and experimentally demonstrated, but only for the deterministic setting where no stochastic effects are present. The first part of this thesis extends a deterministic nonlinear attitude estimator by introducing additional dynamics that allow learning variations of gyro bias as a function of operating temperature, a common source of bias variation in rate gyro readings. The remainder of the thesis formally addresses the problem of stochastic stability and asymptotic performance for this family of estimators when the measurements contain random noise. Analysis tools from stochastic differential equation theory and stochastic Lyapunov analysis are used together to demonstrate convergence of the filter states to a stationary distribution, and to bound the associated steady-state statistics as a function of filter gains and sensor parameters. In many cases these bounds are conservative, but solutions have been found for the associated stationary Fokker-Planck PDEs for two cases. When only the gyro measurement contains noise, the attitude estimation errors are shown to converge to a bipolar Bingham distribution. When the gyro measurement is further assumed to have constant bias, the estimation errors are shown to converge to a joint bipolar Bingham and multivariate Gaussian distribution. Knowledge of the stationary distributions allow for exact computation of steady-state statistics. Further, the analysis suggests a method for modeling a continuous quaternion noise process with specified statistics on SO(3); this model is used for analyzing estimator performance when both the gyro and the attitude measurements contain noise. Bounds and exact predictions for the different noise models are validated using a high fidelity numerical integration method for nonlinear stochastic differential equations.