PMSM电流预测控制参数误差量化分析方法

交流永磁同步电机（PMSM）电流环控制性能是制约交流伺服系统性能的关键。电流预测控制拥有更快的动态响应、更低的电流谐波和优良的转矩响应，但该算法依赖精确的电机模型，参数失配会引发稳态电流误差，无法输出额定转矩，进而导致电机转矩输出效率降低。根据永磁同步电机电流预测模型，详细分析了d、q轴电流静差产生的原因，以及电流预测控制对电机参数误差的敏感性，提出了一种参数误差量化分析方法。该方法引入了电机参数偏差因子，量化描述了电机电感、磁链参数误差与电流静差之间的制约关系。通过仿真分析，验证了所提方法的合理性，为高性能永磁伺服电流预测数字控制技术打下了良好基础。     

基于双滤波器的高精度目标跟踪估计方法

针对连续推力的合作航天器，采用双重无迹卡尔曼滤波(DUKF)算法估计其状态和加速度。通过状态滤波器和参数滤波器的配合，提升滤波精度，完成运动状态和参数的估计，从而实现合作目标的运动轨迹跟踪。与合作航天器相比，非合作航天器存在大小未知、发生时刻未知的机动，无法获得加速度，且信息获取和运动状态的估计难度大。针对非合作航天器，基于简化的相对运动方程，结合天基平台获得目标的观测信息，采用两个扩展卡尔曼滤波(EKF)及基于半正焦弦的机动检测策略实现多未知脉冲机动的运动状态的估计。仿真结果表明：相比于无迹卡尔曼滤波（UKF），DUKF在对合作航天器的状态和加速度估计方面具有更快的收敛速度和更高的滤波精度；对于存在未知机动的非合作航天器，通过对比验证机动检测策略与滤波器切换策略相结合的方法的有效性，该方法能够检测到多次机动并且减少误判。     

TDICCD相机在轨成像参数自主调整方法

为了改善光学遥感卫星的成像质量，提升卫星操控的灵活性，基于遥感成像理论，提出了依据太阳高度角的卫星在轨成像参数自主调整方法。文中首先讨论了影响卫星遥感成像质量的因素，分析了入瞳辐亮度随太阳高度角的变化规律及在不同侧摆角时，一个轨道周期内相机积分时间的变化范围，并据此建立了太阳高度角、侧摆角与积分级数、增益的对应关系。然后分析了太阳高度角和侧摆角的最佳分挡策略，最终得到了可用于卫星在轨成像参数自主调整的二维查找表，以实现卫星在轨成像参数自主调整。利用该方法可提升图像的辐射质量，极大减少了地面上注的数据量。     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

Experimental and numerical investigation on surface quality for two-point incremental sheet forming with interpolator

The unsatisfied surface quality seriously impedes the wide application of incremental sheet forming (ISF) in industrial field. As a novel approach, the interpolator method is a promising strategy to enhance the surface quality in ISF. However, the mechanism for the improvement of surface quality and the influence of interpolator properties on surface roughness are not well understood. In this paper, the influences of process variables (i.e. tool diameter, step size and thickness of interpolators) on the forming process (e.g. surface roughness, forming force and geometric error) are investigated through a systematic experimental approach of central composite design (CCD) in two-point incremental sheet forming (TPIF). It is obtained that the increase in thickness of interpolators decreases the surface roughness in direction vertical to the tool path while increases the surface roughness in direction horizontal to the tool path. Nevertheless, the combined influence between thickness of interpolators and process parameters (tool diameter and step size) is limited. Meanwhile, the placement of interpolator has little influence on the effective forming force of blank. In addition, the geometric error enlarges with the increase of step size and thickness of interpolator while decreases firstly and then increase with an increase in tool diameter. Finally, the influencing mechanism of the interpolator method on surface quality can be attributed to the decrease of the contact pressure due to the increase of contact area with the unchanged contact force. Meanwhile, the interpolator method eliminates the sliding friction on the surface of blank due to the stable relative position between the blank and the interpolator.     

连续式风洞二喉道调节马赫数控制策略

为了降低连续式跨超声速风洞压力波动,提高马赫数稳定性,需要对二喉道调节马赫数控制方式进行研究,针对现有文献鲜少对该控制策略描述等问题,以0.6m连续式跨声速风洞为例,对二喉道控制马赫数的原理进行分析,基于运动控制器加伺服驱动器双PID(proportion-integral-derivative)控制模式实现二喉道位置精确控制,提出了二喉道和压缩机转速的组合控制流程,并采用分段变参数模糊PID加串级控制的算法实现马赫数精确控制,最后进行了试验验证。结果表明马赫数控制精度优于0.001,且每个马赫数极曲线(9个攻角阶梯)的时间可控制在4min以内,证明所提出的控制策略是有效的,可为连续式跨超声速风洞的设计调试提供参考。      

基于CMPSO算法的永磁同步电机转动惯量识别研究

永磁同步电机(PMSM)是一种具有很强非线性的动态系统,在工业驱动应用中发挥着重要作用。实时辨识转动惯量对于高精度PMSM系统控制和稳定性状态监测具有重要意义,但传统转动惯量识别方法精度较低。在传统离散模型参考自适应理论基础之上,用柯西变异粒子群优化(CMPSO)算法代替待辨识参数自适律设计环节,以实现PMSM转动惯量识别。该转动惯量辨识方法充分利用了CMPSO算法的快速高效收敛性,仿真结果和试验结果表明了可行性、正确性和准确性。     

一种小天体旋转参数估计方法

小天体旋转参数是科学数据，也是小天体测绘，着陆导航的基础数据.研究一种在小天体探测接近段过程中使用的基于图像上特征点跟踪和扩展卡尔曼滤波器的小天体旋转参数估计方法.该方法首先建立小天体旋转关系模型，表示小天体在相机坐标系中的姿态变化；然后定义小天体旋转的状态方程，推到了扩展卡尔曼滤波器的计算过程.通过对观测图像序列上的特征点跟踪，利用扩展卡尔曼滤波器方法得到小天体旋转轴指向和自转角速度估计.实验中，在仿真相机与小天体相机100 km距离上，分析了相机坐标系小天体坐标系之间的四元数初值，图像上特征点跟踪精度，相机的观测指向等因素对小天体旋转参数估计精度的影响.实验结果表明，提出的基于图像特征点跟踪和扩展卡尔曼滤波器的小天体旋转参数估计方法能够得到具有较高精度的估计结果.     

相位检测误差对核磁共振陀螺输出频率的影响

针对核磁共振陀螺中采用相位检测方案时可能引起额外频率误差的问题,提出了通过控制电子顺磁共振失谐量及静磁场扰动来抑制额外频率误差的方案。基于Bloch方程,推导了惰性气体原子系综输出频率的表达式,并将相位检测的过程包含在内。建立了考虑相位检测误差的核磁共振陀螺频率误差方程,给出了相位检测引入的额外频率误差表达式并进行了数值仿真。仿真结果表明,通过设定合适的共振失谐量,其额外频率误差至少可以抑制1个数量级,而通过精确地抑制静磁场的一阶及二阶扰动,可以进一步抑制1～3个数量级,将额外频率误差降低到nHz量级。     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.