553 research outputs found
Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment
This paper proposes new algorithms for attitude estimation and control based
on fused inertial vector measurements using linear complementary filters
principle. First, n-order direct and passive complementary filters combined
with TRIAD algorithm are proposed to give attitude estimation solutions. These
solutions which are efficient with respect to noise include the gyro bias
estimation. Thereafter, the same principle of data fusion is used to address
the problem of attitude tracking based on inertial vector measurements. Thus,
instead of using noisy raw measurements in the control law a new solution of
control that includes a linear-like complementary filter to deal with the noise
is proposed. The stability analysis of the tracking error dynamics based on
LaSalle's invariance theorem proved that almost all trajectories converge
asymptotically to the desired equilibrium. Experimental results, obtained with
DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the
performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results
related to this work have been presented in IEEE-ROBIO-201
A global observer for attitude and gyro biases from vector measurements
We consider the classical problem of estimating the attitude and gyro biases
of a rigid body from vector measurements and a triaxial rate gyro. We propose a
simple "geometry-free" nonlinear observer with guaranteed uniform global
asymptotic convergence and local exponential convergence; the stability
analysis, which relies on a strict Lyapunov function, is rather simple. The
excellent behavior of the observer is illustrated through a detailed numerical
simulation
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