A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
This addresses the challenge of inertial parameter estimation for multi-payload deployers in space missions, representing an incremental improvement through the application of reinforcement learning to optimize actuation sequences.
The paper tackles the problem of estimating inertial parameters for cargo-carrying spacecraft without prior state knowledge by using causal learning from actuation responses, resulting in an optimized actuation sequence that enables parameter estimation and validation of configuration transitions.
This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different spacecraft configurations (inertial parameter sets) are simulated under different actuation profiles, in order to produce an optimised time-series clustering classifier that can be used to distinguish between them. The actuation is comprised of finite sequences of constant inputs that are applied in order, based on typical actuators available. By learning from the system's responses across multiple input sequences, and then applying measures of time-series similarity and F1-score, an optimal actuation sequence can be chosen either for one specific system configuration or for the overall set of possible configurations. This allows for both estimation of the inertial parameter set without any prior knowledge of state, as well as validation of transitions between different configurations after a deployment event. The optimisation of the actuation sequence is handled by a reinforcement learning model that uses the proximal policy optimisation (PPO) algorithm, by repeatedly trying different sequences and evaluating the impact on classifier performance according to a multi-objective metric.