Spacecraft inertial parameters estimation using time series clustering and reinforcement learning

• URL: http://arxiv.org/abs/2408.03445v1
• Date: Tue, 6 Aug 2024 20:53:02 GMT
• Title: Spacecraft inertial parameters estimation using time series clustering and reinforcement learning
• Authors: Konstantinos Platanitis, Miguel Arana-Catania, Leonardo Capicchiano, Saurabh Upadhyay, Leonard Felicetti,
• Abstract summary: This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations.
• Score: 0.504868948270058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations.

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