基于频响函数反演法的涡轮增压器基础激励辨识

发动机运转时涡轮增压器轴承座处的振动响应用常规方法是不可测量的。使用频响函数反演法,通过测量涡轮增压器压缩室外壳的可测量点的振动响应,可以利用频响函数反演得到轴承座的响应,从而为涡轮增压器的转子动力学研究提供了基础激励。通过自由悬挂实验和振动台实验,证实频响函数反演法是有效可行的。最后,分析了影响精度的因素。

一类MIMO状态可测的非线性连续系统的激励辨识方法

研究一类MIMO状态可测的非线性连续系统的激励辨识问题.输入激励信号采用高斯白噪声,均匀采样获得输出状态数据.根据Girsanov定理获得系统参数的渐近无偏估计.数值仿真试验说明了该方法的有效性并发现耦合多变量系统辨识中的NNR现象.最后给出该系统的分步激励辨识算法.

一类MIMO状态可观测非线性系统的激励辨识

研究一类多输入多输出(MIMO)状态可观测非线性系统的激励辨识问题,输入激励信号采用高斯白噪声,均匀采样获得输出状态数据;根据Girsanov定理获得系统参数的渐近无偏估计;数值仿真试验说明了该方法的有效性并发现多变量耦合辨识中的噪噪比(NNR)现象;最后给出该系统的分步激励辨识算法。

考虑测量误差影响的改进随机激励辨识方法

为研究考虑测量误差影响的随机激励辨识方法,首先,在逆虚拟激励法中引入模型测量误差和响应测量误差,推演总结影响随机激励辨识精度的因素;其次,分析提高辨识精度的途径;最后,引入系数矩阵来减弱频率响应函数矩阵的条件数对随机激励辨识精度的影响。以悬臂梁结构为对象进行多点随机激励辨识的数值仿真研究;以复合板结构为对象,进行多点随机振动试验研究。结果表明,与传统的逆虚拟激励法相比,此改进方法在考虑测量误差的同时能有效地提高某些固有频率附近的识别精度。

大型泵站压力管道激励源辨识研究

针对甘肃景泰电力提灌总干二泵站存在的压力管道振动问题,基于DASP振动测试系统获得振动信号,通过时域分析法和频谱分析法分析各工况下的振动信号,得到各工况下振源的主要频率;运用功率谱法,分析出压力管道振动存在的主要振源。分析结果表明,引起管道振动的主要原因是离心泵等设备引起的机械振动,水流激励次之,并针对此结论提出相应的减振措施。

Iterative Identification of Robot Dynamic Parameters Based on Logistic Function

The dynamic parameter identification of the robot is the basis for the design of the controller based on the dynamic model.Currently,the primary method for solving angular velocity and angular acceleration is to filter and smooth the position sequence and then form a differential signal.However,if the noise and the original signal overlap in the frequency domain,filtering the noise will also filter out the valuable information in the frequency band.This paper proposes an excitation trajectory based on Logistic function,which fully uses the information in the original signal and can accurately solve the angular velocity and angular acceleration without filtering and smoothing the position sequence.The joint angle of the excitation trajectory is mapped to the joint angular velocity and angular acceleration one by one so that the joint angular velocity and joint angular acceleration can be obtained directly according to the position.The genetic algorithm is used to optimize the excitation trajectory parameters to minimize the observation matrix’s condition number and further improve the identification accuracy.By using the strategy of iterative identification,the dynamic parameters identified in each iteration are substituted into the robot controller according to the previous position sequence until the tracking trajectory approaches the desired trajectory,and the actual joint angular velocity and angular acceleration converge to the expected value.The simulation results show that using the step-by-step strategy,the joint angular velocity and joint angular acceleration of the tracking trajectory quickly converge to the expected value,and the identification error of inertia parameters is less than 0.01 in three iterations.With the increase of the number of iterations,the identification error of inertial parameters can be further reduced.

FIR Systems Identification Under Quantized Output Observations and a Large Class of Persistently Exciting Quantized Inputs

This paper investigates the FIR systems identification with quantized output observations and a large class of quantized inputs. The limit inferior of the regressors' frequencies of occurrences is employed to characterize the input's persistent excitation, under which the strong convergence and the convergence rate of the two-step estimation algorithm are given. As for the asymptotical efficiency,with a suitable selection of the weighting matrix in the algorithm, even though the limit of the product of the Cram′er-Rao(CR) lower bound and the data length does not exist as the data length goes to infinity, the estimates still can be asymptotically efficient in the sense of CR lower bound. A numerical example is given to demonstrate the effectiveness and the asymptotic efficiency of the algorithm.