POD-based Flow Estimation and Its Application in Control of Underwater Robots



Dang, Fengying

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Flow estimation plays an important role in the control and navigation of autonomous underwater robots. It is challenging for underwater robots because of the complex and dynamic fluid environment. Scientists and engineers have been making great efforts in improving flow estimation capability of underwater robots over the past years. There are two main methods to sense the flow field: (1) using flow sensors to measure flow fields directly; and (2) assimilating other sensor measurements (e.g, pressure) through flow estimation algorithms to estimate the flow field. Since the existing flow measurement equipment, such as pitometer log, is hulking, research about using on board sensors to do the flow estimation has attracted more and more attention. However, most of these algorithms can only be used for a specified shape of underwater vehicle. This thesis presents a novel flow estimation approach that assimilates distributed pressure measurements through coalescing recursive Bayesian estimation and flow model reduction using proper orthogonal decomposition (POD). The proposed flow estimation approach does not rely on any analytical flow model and is thus applicable to many and various complicated flow fields for arbitrarily shaped underwater robots while most of the existing flow estimation methods apply only to well-structured flow fields with simple robot geometry. This thesis also analyzes and discusses the flow estimation design in terms of reduced order model accuracy, relationship with conventional flow parameters, and distributed senor placement. To demonstrate the effectiveness of the proposed distributed flow estimation approach, two simulation studies, one with a circular-shaped robot and one with a Joukowskifoil- shaped robot, are presented. The application of flow estimation in closed-loop angle-ofattack regulation is also investigated through simulation.



Underwater robot, Reduced-order flow model, Angle-of-attack control, Flow estimation, Bayesian estimation, Navigation and control