Software defined satellite attitude control algorithm based on deep reinforcement learning

Deep reinforcement learning (DRL) technique is a new kind of machine learning based control algorithm, which shows its outstanding performance in the area of robotics and unmanned aerial vehicle. Meanwhile, in the area of satellite attitude control, traditional PID control algorithm is still widely used. As satellites become smaller and more intelligent and software defined satellite emerges, traditional control methods are even harder to meet the needs of adaptability, autonomy and robustness. To deal with these problems, a deep reinforcement learning based attitude control algorithm is proposed. It is a kind of model-based algorithm, which has much faster convergence speed than model-free algorithm. Compared with traditional method, this algorithm does not need prior knowledge of satellite's physical or orbit parameters and has better adaptability and autonomy, which make it possible for software defined satellite to adapt to different hardware environments and to be developed and deployed much faster. Furthermore, through introducing target network and parallelized heuristic search algorithm, the proposed algorithm has higher network accuracy and faster computation speed. The simulation experiment verifies these improvements.