Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions
In this paper, we consider the problem of precise attitude control for geodetic missions, such as the GRACE Follow-on (GRACE-FO) mission. Traditional and well-established control methods, such as Proportional-Integral-Derivative (PID) controllers, have been the standard in attitude control for most space missions, including the GRACE-FO mission. Instead of significantly modifying (or replacing) the original PID controllers that are being used for these missions, we introduce an iterative modification to the PID controller that ensures improved attitude control precision (i.e., reduction in attitude error). The proposed modification leverages Gated Recurrent Units (GRU) to learn and predict external disturbance trends derived from incoming attitude measurements from the GRACE satellites. Our analysis has revealed a distinct trend in the external disturbance time-series data, suggesting the potential utility of GRU's to predict future disturbances acting on the system. The learned GRU model compensates for these disturbances within the standard PID control loop in real time via an additive correction term which is updated at regular time intervals. The simulation results verify the significant reduction in attitude error, verifying the efficacy of our proposed approach.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Leveraging Product as an Activation Function in Deep Networks
Product unit neural networks (PUNNs) are powerful representational models with a strong theoretical basis, but have proven to be difficult to train with gradient-based optimizers. We present windowed product unit neural …
On the stability properties of Gated Recurrent Units neural networks
The goal of this paper is to provide sufficient conditions for guaranteeing the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS) of Gated Recurrent Units (GRUs) neural networks. T…
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM)…
Music ModelingRecurrent Neural Network-based Internal Model Control design for stable nonlinear systems
Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. …
Gated Feedback Recurrent Neural Networks
In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling…
Language ModelingLanguage Modelling