Attitude Estimation and Control Using Linearlike Complementary Filters: Theory and Experiment

This brief proposes new algorithms for attitude estimation and control, based on fused inertial vector measurements using a linear complementary filters principle. First, $n$ -order direct and passive complementary filters combined with a TRIaxial Attitude Determination algorithm are proposed to give the attitude estimation solutions. These solutions that 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 the inertial vector measurements. Thus, instead of using noisy raw measurements in the control law, a new solution of control that includes a linearlike complementary filter to deal with the noise is proposed. The stability analysis of the tracking error dynamics based on the LaSalle’s invariance theorem proved that almost all trajectories converge asymptotically to the desired equilibrium. Simulations and experimental results, obtained with DIY Quad equipped with the APM2.6 autopilot, show the effectiveness and the performance of the proposed solutions.