Data-driven Moving Horizon Estimation for Angular Velocity of Space Noncooperative Target in Eddy Current De-tumbling Mission
This work addresses a critical issue for space debris removal missions, offering a novel estimation approach for noncooperative targets, though it appears incremental as it extends existing methods to a specific domain.
The paper tackled the problem of estimating angular velocity for space noncooperative targets in eddy current de-tumbling missions, where unknown models and limited data challenge traditional methods, by proposing a Data-driven Moving Horizon Estimation method that achieves model-free state estimation using only one historical trajectory data, with experiments and simulations demonstrating its effectiveness using only de-tumbling torque measurement.
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.