基于深度学习的非合作航天器姿态估计

针对空间非合作航天器姿态测量时受光照和地球背景影响大的问题,提出了一种基于卷积神经网络的端到端姿态估计方法.在该方法中,主干网络采用AlexNet与ResNet.首先,移除主干网络末端的全连接层,并列连接3个全连接层,采用三分支网络分别对姿态角进行估计.然后,设计了将分类问题与回归问题相结合的损失函数,通过分类方法将姿态估计限定在某一范围内,再使用回归方法进一步微调姿态.姿态分类损失函数确定姿态角度基准点,姿态回归损失函数对估计角度进行微调.相较于仅采用回归方法进行姿态估计,此方法能够有效减小姿态估计平均绝对误差、标准差与最大误差.实验对比了不同主干网络的测量精度,平均绝对误差在0.376°～0.746°之间,最优标准差为0.474°.     

Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.     

An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size....     

Supervised learning with probability interpretation in airfoil transition judgment

Transition prediction has always been a frontier issue in the field of aerodynamics. A supervised learning model with probability interpretation for transition judgment based on experimental data was developed in this paper. It solved the shortcomings of the point detection method in the experiment, that which was often only one transition point could be obtained, and comparison of multi-point data was necessary. First, the Variable-Interval Time Average (VITA) method was used to transform the fluctuating pressure signal measured on the airfoil surface into a sequence of states which was described by Markov chain model. Second, a feature vector consisting of one-step transition matrix and its stationary distribution was extracted. Then, the Hidden Markov Model (HMM) was used to pre-classify the feature vectors marked using the traditional Root Mean Square (RMS) criteria. Finally, a classification model with probability interpretation was established, and the cross-validation method was used for model validation. The research results show that the developed model is effective and reliable, and it has strong Reynolds number generalization ability. The developed model was theoretically analyzed in depth, and the effect of parameters on the model was studied in detail. Compared with the traditional RMS criterion, a reasonable transition zone can be obtained using the developed classification model. In addition, the developed model does not require comparison of multi-point data. The developed supervised learning model provides new ideas for the transition detection in flight experiments and other experiments.     

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments....     

攻击角度约束下的分布式强化学习制导方法

为提高导弹在攻击角度约束下对目标的打击效能，提出了一种基于深度确定性策略梯度算法的分布式强化学习制导策略。为了最大限度地减小攻击角度误差，设计了一种新的奖励函数，使导弹在满足视场角约束的同时，视线角向期望值收敛。此外，为了增强强化学习模型的泛化能力，提出了一种分布式探索策略，提高了模型训练过程中对环境的探索效率。仿真结果验证了所提出的分布式强化学习制导方法能够在固定攻击角度约束下实现对目标的精准打击。与传统制导律相比，所提制导方法的攻击角度误差更小，收敛速度更快。     

基于深度学习光流法的荧光油膜全局速度测量

针对基于先验的传统光流法存在前提条件苛刻的问题，提出使用基于深度学习的光流法进行荧光油膜全局速度测量。采用数值仿真试验对基于先验的改进HS光流法和基于深度学习的FlowNet2光流法进行对比，结果显示：在不外加干扰时，改进HS光流法和FlowNet2光流法的平均端点误差分别为0.458 7像素/s和0.381 7像素/s；在亮度变化、噪声干扰或不同的演化时间下，FlowNet2光流法的平均端点误差均明显低于改进HS光流法，平均端点误差差值最大可达5.19像素/s；风洞试验进一步证明，FlowNet2光流法能够获得正确、清晰、定量的荧光油膜全局速度场，较改进HS光流法鲁棒性更高，对风洞工程应用具有一定的参考价值。     

迭代学习方法在PID控制器参数自动调节中的应用及收敛性分析

针对PID控制器参数在具有不确定项或输出干扰项的非线性时变系统中的调节困难,提出了应用迭代学习方法整定PID控制器参数的方法,利用系统的跟踪误差信息自动整定控制器参数,通过理论分析得到此设计方法收敛的充分条件。此方法设计简单且具有普遍性,仿真结果验证了此设计方法的有效性。     

边缘网络下多无人机协同计算和资源分配联合优化策略

针对边缘网络环境下多人机之间存在计算负载不均，造成卸载任务失败的问题，提出了一种多无人机间协作的智能任务卸载方案。通过联合考虑多无人机任务分配、计算资源分配和无人机飞行轨迹，引入公平性指数建立了无人机公平负载最大化和能量消耗最小化问题。基于多智能体深度强化学习框架，提出了融合轨迹规划和任务卸载的分布式算法。仿真结果表明，所提出的多无人机协作方案可以显著提高任务完成率和负载公平度，并且有效适用于大规模用户设备场景。     

Multi-agent Q-Learning control of spacecraft formation flying reconfiguration trajectories

This paper presents a novel approach based on multi-agent reinforcement learning for spacecraft formation flying reconfiguration tracking problems. In this scheme, spacecrafts learn the control strategy via transfer learning. For this matter, a new generalized discounted value function is introduced for the tracking problems. Due to the digital nature of spacecraft computer systems, local optimal controllers are developed for the spacecrafts in discrete-time. The stability of the controller is proven. Two Q-learning algorithms are proposed, in each of which the optimal control solution is learned on-line without knowledge about the system dynamics. In the first algorithm, each agent learns the optimal control independently. In the second one, each agent shares the learned information with other agents. Next, the collision avoidance capability is provided. The effectiveness of the presented schemes is verified through simulations and compared with each other.