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Sensors 2018, 18(12), 4331; https://doi.org/10.3390/s18124331

Reinforcement Learning-Based Satellite Attitude Stabilization Method for Non-Cooperative Target Capturing

1
Xi’an Microelectronics Technology Institute, Xi’an 710065, China
2
Centre for Computational Intelligence, De Montfort University, Gateway House, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
Received: 19 September 2018 / Revised: 30 November 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
(This article belongs to the Section Remote Sensors)
PDF [1219 KB, uploaded 7 December 2018]

Abstract

When a satellite performs complex tasks such as discarding a payload or capturing a non-cooperative target, it will encounter sudden changes in the attitude and mass parameters, causing unstable flying and rolling of the satellite. In such circumstances, the change of the movement and mass characteristics are unpredictable. Thus, the traditional attitude control methods are unable to stabilize the satellite since they are dependent on the mass parameters of the controlled object. In this paper, we proposed a reinforcement learning method to re-stabilize the attitude of a satellite under such circumstances. Specifically, we discretize the continuous control torque, and build a neural network model that can output the discretized control torque to control the satellite. A dynamics simulation environment of the satellite is built, and the deep Q Network algorithm is then performed to train the neural network in this simulation environment. The reward of the training is the stabilization of the satellite. Simulation experiments illustrate that, with the iteration of training progresses, the neural network model gradually learned to re-stabilize the attitude of a satellite after unknown disturbance. As a contrast, the traditional PD (Proportion Differential) controller was unable to re-stabilize the satellite due to its dependence on the mass parameters. The proposed method adopts self-learning to control satellite attitudes, shows considerable intelligence and certain universality, and has a strong application potential for future intelligent control of satellites performing complex space tasks.
Keywords: deep reinforcement learning; satellite attitude control; dynamic environment; Deep Q Network; parametric uncertainty deep reinforcement learning; satellite attitude control; dynamic environment; Deep Q Network; parametric uncertainty
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ma, Z.; Wang, Y.; Yang, Y.; Wang, Z.; Tang, L.; Ackland, S. Reinforcement Learning-Based Satellite Attitude Stabilization Method for Non-Cooperative Target Capturing. Sensors 2018, 18, 4331.

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