A Bayesian optimization hyperband-optimized incremental deep belief network for online battery behaviour modelling for a satellite simulator

Abstract

Simulation tools play crucial roles in the stable implementation of space missions during satellite operations; they are typically utilized for satellite behaviour monitoring by comparing obtained telemetry values and predicted values according to pretrained prediction models. However, as telemetry data streams arrive in a chunk-by-chunk manner, a common practice is to retrain the employed simulation tool based on the newly arrived data, which results in the consumption of many computing resources and time lags. Therefore, an incremental learning algorithm is required to achieve accurate and fast satellite behaviour prediction. This paper proposes a Bayesian optimization hyperband-optimized incremental learning-based deep belief network (BOHB-ILDBN) to reproduce battery voltage behaviours, where the BOHB algorithm is utilized to obtain a group of optimal hyperparameter configurations to initialize a DBN model, the DBN model is incrementally updated by a fine-tuning process, and the variance difference between the actual and forecasted values is taken as the criterion for determining the completion of model training. Finally, the effectiveness and robustness of the model are verified on telemetry data obtained from an on-orbit sun-synchronous remote sensing satellite, the China–Brazil Earth Resources Satellite (CBERS-4A).