Robust Motion Prediction of a Free-Tumbling Satellite with On-Ground Experimental Validation

Abstract

The task of approaching and capturing a free-tumbling satellite on-orbit presents open challenges for autonomous guidance and control strategies. One of these is to robustly predict the satellite’s tumbling motion in view of measurement errors and of unfavorable free-body dynamic effects. A comparative study of solutions proposed in the literature is presented, considering tumbling scenarios that might offer low observability of the related parameters. To this end, this paper extends and compares nonlinear and linear least-squares batch techniques with an extended Kalman filter recursive technique to identify the necessary state and inertial parameters of a satellite for the purpose of motion prediction. These estimation methods are fed with attitude measurements generated by a model-based image-processing algorithm, which is applied to images produced on ground with two dedicated experimental facilities. It is shown that the attitude measurements present a non-Gaussian error distribution. Through experimental validation, the nonlinear least-squares method is shown to be the most robust for five representative tumbling states of the target satellite. The output of a statistical identification procedure provides an estimate of the motion prediction dispersion for long prediction times, which is a key input in robust tracking control methods.