  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.
