直流力矩电机PWM驱动卡的设计

针对某型直流力矩电机研制了基于PWM控制方式的驱动卡,对驱动系统的组成与电路结构进行了详细介绍,重点讨论了HIP4080A芯片的应用及其外围电路。试验表明,该驱动卡能有效改善系统的低速平稳性,有利于实现高性能的速率转台。     

基于LED光源的运动式太阳模拟器控制系统

为了解决编码式太阳敏感器装星后现场的功能测试,研制了一种基于LED光源的运动式太阳模拟器,为该测试提供模拟的太阳光信号和太阳光矢量信号.基于太阳模拟器组成与工作原理,对光源辐亮度和矢量运动的控制系统进行研究.根据太阳光信号要求,通过光源选取与功率计算确定LED型号和个数,并采用压控恒流源驱动技术对光源辐亮度进行线性调节.根据太阳光矢量信号要求,通过负载扭矩与功率计算选取GUS-60型超声电机,采用16位绝对式编码器对运动角度进行测量,以数字信号处理器为主要器件对电机进行闭环反馈控制.测试结果表明,光源控制系统能够实现辐亮度在0～527.4W·m^-2内线性可调,矢量运动装置在-15°～40°内的运动角度控制精度优于±0.01°,满足编码式太阳敏感器的测试要求.     

Design and development of a five-axis machine tool with high accuracy,stiffness and efficiency for aero-engine casing manufacturing

In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design,key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual driv...     

某型涡轴发动机喘振攻关与验证

针对某型涡轴发动机夏季试车时常发生加速喘振故障的问题,使用故障树初步分析引起喘振的主要原因,并通过径向扩压器叶片进行流场仿真、串装、多台发动机对比计量、试车试验等,对喘振原因进行深入分析。研究表明径向扩压器进口叶根倒角变大对发动机加速性能有一定程度的改善作用,大气条件对发动机加速性有一定影响,径扩面积越大、叶根圆角与进气边转接越顺滑,越有利于加速性能。     

光学元件数控单轴加工中的边缘效应控制建模方法

边缘效应严重制约了计算机控制光学表面成型技术（CCOS）的加工精度和加工效率，是亟待解决的技术难点之一。基于Preston方程，建立工具盘在加工工件边缘时的定量去除模型，是解决该问题的重要途径。将加工工件、工具盘尺寸，磨削压力、速度、时间，磨削点位等加工参量进行数学建模，建立边缘去除函数模型，精确计算边缘压力分布和磨削累计时间。随着工具盘在工件边缘露边量增加，工具盘压力呈指数级增长。工件磨削累计时间随工具盘中心点位不同呈现分段变化规律。研发单轴机数控设备，利用工具盘沿工件边缘母线点位移动方式，进行边缘效应控制研究。通过实验精确求出Preston方程比例系数k，验证模型仿真与实际加工结果吻合性，准确度达到91.9%。数控单轴机修边方法和建立的数学模型可以很好地指导实际研磨抛光过程。     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.