磁悬浮柔性转子系统变学习速率单神经元PID控制

磁悬浮轴承柔性转子系统的转速范围较大,固定的控制参数很难同时保证系统所需的低频和高频特性。本文在一般单神经元PID控制的基础上,将转速信号引入磁悬浮轴承数字控制器,在不同转速段选择不同的学习速率以调节控制参数得到合适的磁悬浮轴承的刚度和阻尼。试验结果表明,变学习速率单神经元自适应PID控制能够明显减小转子在各阶临界转速时的振幅,有助于磁悬浮轴承柔性转子系统的安全稳定运行。     

Hopper concepts for small body landers

The investigation of small bodies, comets and asteroids, can contribute substantially to our understanding of the formation and history of the Solar System. In situ observations by landers play an important role in this field.     

高职计算机专业学生自主学习能力培养策略

计算机专业课程具有实践性与操作性非常强的特点,采用传统的先教后学的教学方式学习效率低,不能适应时代的发展。自主学习方式可以更好的引导计算机专业学生从做中学,从学中做,提高学习效率,培养学生终身学习能力。在探讨自主学习理论的基础上,分析了自主学习的意义及高职计算机专业学生自主学习现状,并提出了适合高职计算机专业学生的自主...     

Trajectory Tracking Control for a GMM Actuator Based on a Heuristic ILC Method

A heuristic iterative learning control （ILC） method is presented and applied to the trajectory tracking control of a giant magnetostrictive material （GMM） actuator. A GMM actuator is used as experimental equipment for micro-displacement trajectory tracking control. The advantage of the presented approach lies in quitting the model of the GMM actuator. The experimental results attest to the high efficiency of the presented method for the micro-displacement trajectory tracking control.     

一种改进的适应性超媒体学习单元模型

在netUniversité这一平台上,结合IMS LD标准建立学习单元模型,并以此标准根据新的教学需求对已有模型进行改进.将HBDI 全脑模型结合入学习内容管理工具,利用设计时工具Reload Editor和运行时工具CopperCore Player构造学习单元模型.     

大学英语课堂教学有效任务的设计

本文探讨了目前在英语教学界影响较大的任务型教学法中任务的设计与运用,介绍了在课堂教学中的一些有效任务及其优越性。     

基于DHNN人工神经网络的指纹识别技术

给出一种基于DHNN人工神经网络的人的指纹识别技术 ,讨论了DHNN实现指纹识别的基本原理 ,在一定领域内不失为一种有效的识别手段。     

A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise

In data-driven fault diagnosis for turbo-generator sets, the fault samples are usually expensive to obtain, and inevitably with noise, which will both lead to an unsatisfying identification performance of diagnosis models. To address these issues, this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network (W-ENN). W-ENN is a novel neural network which has three types of connection weights and an improved correlation function. The performance of the proposed model is validated against Extension Neural Network (ENN), Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Extreme Learning Machine (ELM) based models. The results indicate that, on noisy small sample sets, the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability. The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.     

一种小天体探测器绕飞轨道主动抗扰控制方法

针对小天体绕飞探测过程中,探测器受到的非球形引力摄动及太阳光压等多源扰动问题,结合绕飞探测的周期性特点,提出一种小天体探测器绕飞轨道主动抗扰控制方法。首先,通过迭代学习控制对小天体探测器受多源扰动影响产生的误差进行抑制,然后设计扰动观测器估计系统总扰动,构造自适应律实时估计扰动上界,进而设计控制律对总扰动进行主动补偿。最后以243Ida作为目标天体进行绕飞探测仿真分析,结果表明该控制方法能更加有效地提高小天体探测器的轨道跟踪控制精度。     

小天体柔性附着状态协同估计方法

针对小天体附着过程易发生倾覆、反弹导致任务失败问题,提出“柔性附着”概念,替代传统小天体探测的刚性附着模式,为提高小天体附着任务可靠性提供新的解决思路和技术途径。在此基础上,针对柔性着陆器的状态估计问题展开研究。首先建立了柔性着陆器近似模型,提出了柔性着陆器“等效面”概念及柔性着陆器姿态的近似表征方式,进而提出了柔性附着状态协同估计方法,并通过数值仿真检验了柔性着陆器状态协同估计的可行性。