A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers

TL;DR

This paper introduces a causal learning method utilizing reinforcement learning to optimize actuation sequences for in-orbit inertial parameter estimation of multi-payload spacecraft, enabling accurate configuration identification without prior state knowledge.

Contribution

It presents a novel approach combining causal learning, time-series clustering, and reinforcement learning to improve in-orbit inertial parameter estimation for spacecraft configurations.

Findings

Optimized actuation sequences improve classification accuracy.

Reinforcement learning with PPO effectively tunes actuation for parameter estimation.

Method enables validation of configuration transitions after deployment.

Abstract

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.