In this work, a highly accurate navigation device is proposed for unmanned underwater vehicle navigation. A six degree of freedom, open loop underwater vehicle model is generated and is used as the motion platform in this study. The new navigation system, previously developed at the Rochester Institute of Technology, requires real-time body angular acceleration terms as inputs to the algorithm. To address this requirement, real-time signal differentiation techniques were investigated. The differentiation of real-world, noisy signals is a difficult task due to the inherent numerical differentiation and subsequent noise amplification. A sliding mode differentiation scheme is proposed with a fuzzy adaptive controller to aid the accuracy of the signal differentiator and minimize noise amplification. The device algorithms are then implemented in the underwater vehicle model and navigation estimates are compared against theoretical motion. The result is an accurate representation of underwater vehicle attitude and velocity without the aid of global positioning satellite data. Although inertial position estimates obtained from noisy signals suffer from drifting, the filtering techniques used in this work minimize this effect. The navigation estimates show the best results on dynamic maneuvers which do not induce a rolling motion as the underdamped rolling motion requires higher steady state noise for estimation. When assessed against current technologies for underwater vehicle navigation that do not use GPS, the proposed system provides comparable estimation results while creating a reduction of cost, weight and removing the dependence on the speed of sound in water.
Library of Congress Subject Headings
Remote submersibles; Navigation; Sliding mode control
Department, Program, or Center
Mechanical Engineering (KGCOE)
Szklany, Steven, "A Feasibility assessment of a new navigation system for unmanned underwater vehicles with adaptive gain sliding mode differentiation" (2012). Thesis. Rochester Institute of Technology. Accessed from
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