Deep Neural Networks Based Real-time Optimal Control for Lunar Landing. (August 2019)

• Record Type: Journal Article
• Authors: Zhu, Lingchao; Ma, Jian; Wang, Shuquan
• Is Part Of: IOP conference series. Volume 608(2019)
• Publication Date: 2019-08
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/608/1/012045
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Recent research on deep learning control, a new control algorithm based on machine learning able to learn deep architectures, has shown excellent performance on robots and drones. With the development of intelligent control like deep learning and reinforcement learning, accuracy, real-time, adaptability, robustness and autonomy of control algorithm have been achieved by the intelligent controls. Traditional control methods have difficulties to achieve nice performance in complex situations. Deep learning offers powerful algorithms to real-time search near-optimal controllers of lunar landing spacecraft with nonlinear dynamics. In terms of lunar landing control system, deep architectures offer the possibility to get an approximate solution of co-state equation without time-consuming iterative process. Furthermore, real-time optimal thrust during lunar landing may be derived directly through deep neural networks. As a single infrastructure for machine learning in both production and research, TensorFlow is chosen for training the deep artificial neural networks in this paper. Numerical simulations demonstrate the effectiveness of deep neural networks. The results of deep neural networks based optimal control are contrasted with traditional optimal algorithm, whose main idea is to track the pre-designed optimal trajectory by ground station. This research provides an effective approach to cope with the lunar landing problem.