Propulsionless planar phasing of multiple satellites using deep reinforcement learning

This work creates a framework for solving highly non-linear satellite formation control problems by using model-free policy optimisation deep reinforcement learning (DRL) methods. This work considers, believed to be for the first time, DRL methods, such as advantage actor-critic method (A2C) and proximal policy optimisation (PPO), to solve the example satellite formation problem of propellantless planar phasing of multiple satellites. Three degree-of-freedom simulations, including a novel surrogate propagation model, are used to train the deep reinforcement learning agents. During training, the agents actuated their motion through cross-sectional area changes which altered the environmental accelerations acting on them. The DRL framework designed in this work successfully coordinated three spacecraft to achieve a propellantless planar phasing manoeuvre. This work has created a DRL framework that can be used to solve complex satellite formation flying problems, such as planar phasing of multiple satellites and in doing so provides key insights into achieving optimal and robust formation control using reinforcement learning.     

基于强化学习的航空器机场智能静态路径规划

随着人工智能迅速发展以及“智慧机场”的提出,研究人工智能在机场如何有效地辅助机场管制人员,驾驶员指挥航空器在地面滑行具有重要意义。本文提出一种基于强化学习的滑行路径规划方法,构建航空器机场地面强化学习移动模型,并以海口美兰机场为案例采用 Python 内置工具包 Tkinter 进行场面仿真;在此基础上,考虑机场航空器滑行规则,采用 Off-Policy 中 Q-Learning 算法求解贝尔曼方程,实现航空器在 Model-based 环境中进行静态路径规划。结果表明：本文所提方法能够实现停机位到跑道出口智能静态路径规划     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic

High-Altitude Long-Endurance(HALE) solar-powered Unmanned Aircraft Vehicles(UAVs) can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers. Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day. In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL) framework is...     

二次奖罚学习自动机

研究了奖罚型学习自动机的一种非线性强化算法。与线性的奖罚模型（ＬＲＰ）不同,新模型的行动选择概率的更新函数为二次的。这使得该模型的学习性能优于ＬＲＰ,且对不同的环境,其具有不同的行为和特点。     

纳米隔热材料热辐射特性的理论计算

高温下纳米隔热材料内热辐射的影响将显著增强,其热辐射特性对热辐射传热有很大影响。为了认识高温纳米隔热材料的热辐射特性,采用Mie理论建立了掺杂纤维增韧剂和遮光剂的纳米隔热材料热辐射特性理论计算方法,编写了纳米隔热材料热辐射特性计算程序,对某纳米隔热材料的热辐射特性进行了理论研究,得到了光谱衰减系数、光谱散射反照率以及全光谱平均辐射特性参数及散射相函数。理论模拟结果表明:在3～9.5μm波长范围内,纳米隔热材料对热辐射具有强烈的衰减作用,对3～7μm的热辐射呈现强烈的散射特征,对7～9.5μm的热辐射,随波长增大散射特征逐渐减弱,对9.5μm的热辐射呈现较强的吸收特征。在300～1 300 K,该纳米隔热材料全光谱平均衰减系数>5×104 m-1,平均散射反照率>0.96,具有较强的前向散射特征,这些特征来源于遮光剂粒子,增韧剂影响很小。     

高性能与高功能纤维的发展

概述了用于复合材料增强体的高性能纤维和具有各种物理与化学功能用途的高功能纤维的进展现状并推测其发展趋势。     

复合材料层合板开口补强技术试验研究

国内外学者对复合材料开口非对称补强做了很多研究,但主要集中在小开口的插层补强及共固化补强上。对用机械连接补强技术和插层补强技术补强后的层合板,进行拉伸和剪切试验;并采用有限元方法对补强后的复合材料层合板进行数值仿真,仿真结果与试验结果吻合得较好。经试验研究和数值仿真,得到各补强方案在各工况下的最大应变位置,并将不同补强方案进行对比。结果表明:插层补强方案优于机械补强方案。     

粘钢加固在增强结构强度中的应用

本文结合工程实例介绍了采用粘钢加固法来增强结构强度的设计与施工 ,初步探讨了结构增层改建中采用粘钢加固的计算方法、施工工艺及取得的实际效果。     

强夯法加固湿陷性黄土地基实例分析

通过陕西渭南开发区某建筑地基的加固实例，对使用强夯法消除黄土湿陷性，提高黄土地基强度的施工技术与质检方法进行了分析研究，得出了在湿陷性黄土地区含水量适中，地下水位较深时采用强夯法效果显著，对实际施工具有一定的指导意义。