Sensors, Vol. 18, Pages 4331: Reinforcement Learning-Based Satellite Attitude Stabilization Method for Non-Cooperative Target Capturing

Sensors, Vol. 18, Pages 4331: Reinforcement Learning-Based Satellite Attitude Stabilization Method for Non-Cooperative Target Capturing Sensors doi: 10.3390/s18124331 Authors: Zhong Ma Yuejiao Wang Yidai Yang Zhuping Wang Lei Tang Stephen Ackland 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 tr...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research