Neural-adaptive Stochastic Attitude Filter on SO(3)
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by
Hashim A. Hashim, Mohammed Abouheaf, Kyriakos G. Vamvoudakis
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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