2016/03/14 by Maziar Izadi, Sasi Prabhakaran Viswanathan, Izadi, Maziar +7
Engineering · #Adaptive Control of Nonlinear Systems #Astronomical Observations and Instrumentation #FOS: Mathematics #Inertial Sensor and Navigation #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1603.04519
openalex publication_date 2016/03/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Estimation of rigid body attitude motion is a long-standing problem of\ninterest in several applications. This problem is challenging primarily because\nrigid body motion is described by nonlinear dynamics and the state space is\nnonlinear. The extended Kalman filter and its several variants have remained\nthe standard and most commonly used schemes for attitude estimation over the\nlast several decades. These schemes are obtained as approximate solutions to\nthe nonlinear optimal filtering problem. However, these approximate or near\noptimal solutions may not give stable estimation schemes in general. The\nvariational attitude estimator was introduced recently to fill this gap in\nstable estimation of arbitrary rigid body attitude motion in the presence of\nuncertainties in initial state and unknown measurement noise. This estimator is\nobtained by applying the Lagrange-d'Alembert principle of variational mechanics\nto a Lagrangian constructed from residuals between measurements and state\nestimates with a dissipation term that is linear in the angular velocity\nmeasurement residual. In this work, the variational attitude estimator is\ngeneralized to include angular velocity measurements that have a constant bias\nin addition to measurement noise. The state estimates converge to true states\nalmost globally over the state space. Further, the bias estimates converge to\nthe true bias once the state estimates converge to the true states.\n