Neural-adaptive Stochastic Attitude Filter on SO(3) release_awknsuft5vcuba6pjyyrtvserq

by Hashim A. Hashim, Mohammed Abouheaf, Kyriakos G. Vamvoudakis

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2021  

Abstract

Successful control of a rigid-body rotating in three dimensional space requires accurate estimation of its attitude. The attitude dynamics are highly nonlinear and are posed on the Special Orthogonal Group SO(3). In addition, measurements supplied by low-cost sensing units pose a challenge for the estimation process. This paper proposes a novel stochastic nonlinear neural-adaptive-based filter on SO(3) for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties. keywords / index-terms: Neuro-adaptive, stochastic differential equations (SDEs), Brownian motion process, attitude estimator, Special Orthogonal Group, Unit-quaternion, SO(3), IMU, MARG.
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Date   2021-11-09
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arXiv  2110.08889v2
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