多自由度非高斯随机振动控制

在振动试验台上进行多自由度(MDOF)随机振动激励时,传统的控制方法生成的驱动信号及试验台的响应信号都是高斯信号。但真实的振动干扰信号多是超高斯的;而相比于高斯激励,亚高斯激励可降低驱动信号的最大幅值。为实现多自由度亚高斯和超高斯振动控制,提出一种多自由度非高斯随机振动控制方法,该方法采用系统辨识解决系统耦合问题,而后通过选择特殊的相位生成非高斯伪随机驱动信号,再经过时域随机化得到真随机非高斯驱动信号。基于Hexapod平台的多自由度微振动试验台的亚高斯和超高斯实验表明,在试验台的响应功率谱(PSD)满足工程中常用的±3dB精度的同时,亚高斯驱动信号的最大幅值相比于高斯驱动信号的最大幅值降低了20%以上;超高斯响应信号的峭度与参考峭度的误差在0.2之内。实验结果验证了所提方法的有效性。     

MIMO窄带加宽带非高斯随机振动试验

提出了一种用于多输入多输出窄带加宽带非高斯随机振动试验方法。首先，详细分析了随机相位与非高斯随机信号偏度和峭度的关系，提出了一种可用于快速生成具有指定偏度和峭度的迭代相位调节法。其次，根据控制通道之间的耦合关系，利用各通道之间随机相位的不相关特性，实现了多通道非高斯随机信号生成的解耦。然后，将生成的非高斯随机信号作为参考输出响应，采用时域逆系统方法生成驱动信号，并利用控制算法分别对响应信号的功率谱、偏度和峭度进行控制。最后，通过三轴向振动台试验验证了本文提出的多输入多输出窄带加宽带非高斯随机振动试验方法的可行性。结果表明，控制点上加速度的功率谱被控制在了±3 dB的容差限内，满足预设的容差要求。同时，时域响应信号的峭度和偏度也满足给定的参考要求。     

MIMO随机振动试验控制的逆多步预测模型法

多输入多输出（MIMO）随机振动试验在产品可靠性验证中扮演重要角色。逆系统方法作为一种时域方法，直接由参考信号（即期望得到的响应信号）生成驱动信号，但容易产生不稳定的结果。本文以系统的有限差分模型为基础，推导了逆多步预测模型，并建立了该逆模型与矩阵幂次算法结合的MIMO随机振动试验方法。通过悬臂梁模型进行双输入双输出仿真试验，验证了逆多步预测模型进行MIMO随机振动试验驱动生成的可行性。在三轴振动台上进一步验证该方法进行MIMO随机振动试验控制的能力。     

多输入多输出正弦扫频试验控制方法研究及实现

振动环境试验是检验产品质量和可靠性的必须途径,对于一些大型的结构,单点振动试验不能充分模拟产品的使用环境,这就需要多点振动试验来模拟,其中多点正弦扫频试验是一个很重要的部分。本文根据多输入多输出系统的控制理论,基于正弦振动的基本特性,设计了多输入多输出正弦扫频试验控制系统的控制方案,同时给出控制修正的具体办法。并且对控制算法进行了仿真,实现了驱动信号发生、响应信号的采集和处理以及控制过程模拟,检验了算法的可行性。结果表明,算法可以使控制点的响应信号快速、稳定地收敛于参考信号,验证了本文设计的多输入多输出正弦扫频试验控制系统的有效性。     

基于回路整型设计的多轴振动控制

 多激励多轴向振动控制试验中,在频响函数矩阵呈现病态的频率点,控制点响应的自谱和互谱往往难以达到预定参考谱目标。针对这种情况,首先在控制频段对频响函数进行拟合修正,求得系统前置补偿矩阵,然后根据 H_∞回路整型理论对驱动谱矩阵进行整型,将整型后的驱动谱矩阵代替原驱动谱进入控制回路计算。通过试验验证表明,基于回路整型设计的修正算法,能够明显地抑制某些频段内频响函数的不良影响,自谱和互谱响应都能得到比较理想的控制结果。     

多输入多输出随机振动试验变参数PID控制

 对多输入多输出(MIMO)随机振动试验控制的基本过程进行了系统分析。定义了一种新的在对数坐标下的控制误差表达式,然后在控制算法中引入了新的比例-积分-微分(PID)控制策略,并给出了PID参数随误差变化的规律。以悬臂梁为控制对象,完成了两输入两输出随机振动试验。结果表明：变参数PID控制方法能在保证快速均衡的基础上,有效地减小功率谱和参考谱之间的相对误差以及加速度总均方根值的控制误差,提高随机振动试验的控制精度。     

航空发动机振动环境谱统计归纳方法及振动试验台复现

为满足航空发动机及机载产品研制过程贴近使用环境的振动考核试验需求,需根据发动机实测振动数据给出振动考核试验所需的输入谱图。依据GJB/Z 126-99中给出的环境测量数据归纳方法,建立了发动机实测振动环境谱统计归纳方法并通过程序实现。利用发动机多架次实测试飞振动数据统计归纳得到发动机测点位置的振动实测谱。基于能量等效及信号频域特征分布一致原则,将归纳得到的实测谱转化为可用于振动台输入的振动环境谱,并在振动台上进行了振动信号的复现试验。结果表明：振动台输出信号与发动机实测振动信号频域分布特征一致,在统计频率带宽范围内振动总量最大相差5.7%,证明了转化方法是合理的,为航空发动机机载设备贴近使用环境的振动考核试验方法提供了真实的输入谱图。     

航空发动机外部管路的振动响应分析

针对航空发动机外部管路的动力学设计需求,采用基于试车实测数据的振动响应求解方法,对典型管路的振动响应特性进行了计算分析。研究结果表明:在风扇机匣位置的管路主要承受转子不平衡和风扇气动激励,表现为典型的简谐激励特征,可采用谐响应分析方法求解其振动响应。低阶振型对管路的动态特性起决定作用,振动能量输入导管结构后,响应输出方向可能发生变化。在燃烧室及尾喷口位置的管路主要承受来自于燃烧室火焰脉动和气动噪声激励,表现为典型的随机激励特征,可采用动力学谱密度方法求解其振动响应,得到功率谱密度响应曲线及具有一定置信度的位移分布和应力分布。     

多输入多输出随机加正弦控制系统研究

针对多输入多输出随机加正弦振动控制试验,首先阐述了控制算法的理论框架,然后采用相关积分法完成正弦信号和随机信号的分离,并分别对随机信号进行自功率谱控制,对正弦信号进行幅值修正,完成闭环控制。文中进行了算法仿真和实验验证,结果表明,本文算法能以高精度分离多点正弦和随机信号,分别满足二者的控制目标。     

利用FIR滤波器生成随机振动试验驱动信号的新方法

 阐述了一种利用有限冲击响应(FIR) 滤波器生成随机振动试验驱动信号的新方法。该方法通过设计一个FIR 滤波器对白噪声序列进行滤波,使滤波器输出序列的功率谱密度满足试验驱动信号的要求。在随机振动试验控制中,将FIR 滤波器和试验的受控对象组成增广系统,这将有助于简化对控制系统的分析和设计。     

Control method for multi-input multi-output non-Gaussian random vibration test with cross spectra consideration

A control method for Multi-Input Multi-Output(MIMO) non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.     

Generation of Non-Gaussian Random Vibration Excitation Signal for Reliability Enhancement Test

During environment testing, the time histories of some dynamic environments follow non-Gaussian distribution. It is always assumed that the random vibration simulated follows Gaussian distribution, because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian. Yo simulate the real environment of product, a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density （PSD）, skewness and kurtosis by shot noise. In this way, non-Gaussian random vibration can be produced on traditional electrodynamic shaker. It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker. At last, the wavelet is used to analyze the non-Gaussian signal     

Stationary non-Gaussian random vibration control: A review

This paper presents a review of the various methods for the stationary non-Gaussian random vibration control. Random vibration tests can be divided, according to the number of exciters, in single-shaker tests and multiple-shaker tests. In the stationary non-Gaussian random vibration test, the time and frequency domain characteristics of the responses should be controlled independently and simultaneously. Skewness and kurtosis are usually selected as the non-Gaussian time control references (targets) while power spectral density is the frequency domain control procedure before it recalls the concepts of non-Gaussianity. Then, the generation of a one frame stationary non-Gaussian random signal for both the single and multiple shakers are reviewed. The commonly used methods for the single non-Gaussian random signal generation in the random vibration test are memoryless nonlinear transformation, phase modification and Filtered Poisson process. For the multiple-shaker case, the sequential phase modification and memoryless nonlinear transformation are used to generate one frame coupled multi-channel non-Gaussian random signal. In order to obtain a stationary and consecutive dynamic input, the time domain randomization procedure is introduced with high computational efficiency and its influences on the skewness and kurtosis are analyzed. Finally, two existing problems in the non-Gaussian random vibration control are addressed.     

液压管路系统随机振动下疲劳分析

民用飞机液压管路系统受到飞机振动环境的影响,可能产生振动引发的管路及安装结构疲劳破坏。为了分析液压管路系统在飞机振动环境下的承受能力,参考RTCA/DO-160G中对于固定翼飞机振动环境的定义及要求,对某民用飞机液压管路进行振动模态和随机振动响应分析。通过有限元分析得出了液压管路及安装结构的随机振动Von Mises均方根应力,采用基于高斯分布和Miner线性累计损伤定律的三区间法,计算管路系统的随机振动疲劳损伤,得到了疲劳薄弱部位及疲劳分析结果,为民用飞机液压管路在随机振动环境下的疲劳分析提供参考。     

Multi-input multi-output random vibration control using Tikhonov filter

Noises always disturb the control effect of an environment test especially in multi-input multi-output (MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces will be amplified and the response spectral lines may awfully exceed their tolerances. Most of the major biases between the response spectra and the reference spectra are pro-duced by the amplified noises. However, ordinary control algorithms can hardly reduce the level of noises. The influences of the noises on both the auto-and cross-power spectra are analyzed in this paper. As a conventional frequency domain method on the inverse problem, the Tikhonov filter is adopted in the environment test to suppress the exceeding spectral lines. By altering regularization parameters gradually, the auto-power spectra can be improved in a closed control loop. Instead of using the traditional way of selecting regularization parameters, we observe the coherence change to estimate noise eliminations. Incidentally, the requirement of coherence control can be realized. The errors of the phase are then studied and a phase control algorithm is introduced at the end as a sup-plement of cross-power spectra control. The Tikhonov filter and the proposed phase control algo-rithm are tested numerically and experimentally. The results show that the noises in the vicinity of lightly damped resonant peaks are more stubborn. The response spectra are able to be greatly improved by the combination of these two methods.     

Matrix Power Control Algorithm for Multi-input Multi-output Random Vibration Test

 Both auto-power spectrum and cross-power spectrum need to be controlled in multi-input multi-output (MIMO) random vibration test. During the control process with the difference control algorithm (DCA), a lower triangular matrix is derived from Cholesky decomposition of a reference spectrum matrix. The diagonal elements of the lower triangular matrix (DELTM) may become negative. These negative values have no meaning in physical significance and can cause divergence of auto-power spectrum control. A proportional root mean square control algorithm (PRMSCA) provides another method to avoid the divergence caused by negative values of DELTM, but PRMSCA cannot control the cross-power spectrum. A new control algorithm named matrix power control algorithm (MPCA) is proposed in the paper. MPCA can guarantee that DELTM is always positive in the auto-power spectrum control. MPCA can also control the cross-power spectrum. After these three control algorithms are analyzed, three-input three-output random vibration control tests are implemented on a three-axis vibration shaker. The results show the validity of the proposed MPCA.     

Computer generation of correlated non-Gaussian radar clutter

We develop computer simulation procedures which enable us to generate any correlated non-Gaussian radar clutter that can be modeled as a spherically invariant random process (SIRP). In most cases, when the clutter is a correlated non-Gaussian random process, performance of the optimal radar signal processor cannot be evaluated analytically. Therefore, in order to evaluate such processors, there is a need for efficient computer simulation of the clutter. We present two canonical simulation procedures for the generation of correlated non-Gaussian clutter. A new approach for the goodness-of-fit test is proposed in order to assess the performance of the simulation procedure