Data-driven Moving Horizon Estimation for Angular Velocity of Space Noncooperative Target in Eddy Current De-tumbling Mission

TL;DR

This paper introduces a novel data-driven moving horizon estimation method for accurately estimating the angular velocity of noncooperative space targets during eddy current de-tumbling, overcoming challenges of unknown models and limited data.

Contribution

It extends Willems' fundamental lemma to nonlinear autonomous systems and develops a model-free estimation algorithm with proven stability.

Findings

The proposed method accurately estimates angular velocity with limited data.

Experiments validate effectiveness in eddy current de-tumbling scenarios.

The algorithm demonstrates time-discount robust stability.

Abstract

Angular velocity estimation is critical for eddy current de-tumbling of noncooperative space targets. However, unknown model of the noncooperative target and few observation data make the model-based estimation methods challenged. In this paper, a Data-driven Moving Horizon Estimation method is proposed to estimate the angular velocity of the noncooperative target with de-tumbling torque. In this method, model-free state estimation of the angular velocity can be achieved using only one historical trajectory data that satisfies the rank condition. With local linear approximation, the Willems fundamental lemma is extended to nonlinear autonomous systems, and the rank condition for the historical trajectory data is deduced. Then, a data-driven moving horizon estimation algorithm based on the M step Lyapunov function is designed, and the time-discount robust stability of the algorithm is given. In order to illustrate the effectiveness of the proposed algorithm, experiments and simulations are performed to estimate the angular velocity in eddy current de-tumbling with only de-tumbling torque measurement.