Robust method to provide exponential convergence of model parameters solving linear time-invariant plant identification problem

The scope of this research is a problem of parameters identification of a linear time-invariant plant, which (1) input signal is not frequency-rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.     

分段多项式拟合法测量光纤几何参数

光纤几何参数是影响光纤性能的一个重要指标。灰度法为光纤几何参数测量的一种常用方法,测量时需要对光纤通光照明以区分纤芯和包层。由于光并不完全集中于纤芯传播（部分光在包层中传播）,导致纤芯与包层的界面难以区分。为了准确找出纤芯边缘,本文利用两段多项式分别拟合纤芯和包层区域的光强灰度分布,求得两段多项式的交点对应的灰度值作为纤芯和包层的分界点,从而得到纤芯的边缘数据。利用分段三次Hermite插值对测量数据进行矫正,降低误差点对拟合的影响。通过对两组成像质量不同的光纤端面图像进行测量,用标准仪器先后测得纤芯的直径和不圆度为10.068µm、0.616%和10.397µm、0.766%,本文方法的测量值为9.999µm、0.716%和10.020µm、0.857%。实验表明,本文方法对光纤几何参数的测量具有较好的准确性和稳定性,而且从理论上讲,本文方法较之常用的灰度法更具有物理意义和测量原理上的合理性。     

Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.     

基于改进HHT的高拱坝模态参数识别方法研究

水工结构出现损伤会使结构特定部分的质量或刚度发生损失,进而改变结构动力特性,这些都将在结构的模态信息中有所反映,因此如何精确地获取结构的模态信息是水工结构运行状态监测的重要前提。本文针对原型动力试验激励难的问题,结合水工结构环境激励荷载的特点,研究基于改进HHT（Hilbert-Huang Transform）的高拱坝模态参数方法。针对密频结构响应信号EMD分解过程中出现的模态混叠现象,提出了带通滤波和伪信号处理技术相结合的方法,对泄洪振动信号的EMD过程进行控制,得到结构的各阶模态分量,然后应用随机减量法提取出各阶模态分量的自由衰减信息,最后对自由衰减信息进行Hilbert变换和最小二乘拟合,得到拱坝的模态参数。     

油纸绝缘变压器介质响应电路参数辨识的研究

基于介质响应理论的回复电压测量方法能够有效地无损诊断变压器的绝缘状态,评估变压器绝缘纸中的水分含量。其中回复电压初始斜率特征量与热老化有着密切的关系,因此它也是评估绝缘老化的重要特征量。本文阐述了回复电压初始斜率在辨识介质响应等值电路参数方面具有重要的作用,并建立了利用回复电压初始斜率特征量求解介质响应等值电路参数的数学模型,该模型为含有指数函数的非线性方程组。本文利用最小二乘方法建立了数学优化模型,并应用智能优化算法求解。研究结果表明本文建立的模型是可行的,为评估变压器老化和研究新的特征量提供了新思路。     

基于方差变化率判据-四分位的风电场功率异常数据识别

风电场运行中产生了数量巨大的历史数据,而提升历史数据的质量是实现风电场高效智能运维的前提。为此,文中分析了风电场风功率数据的分布特征和形成机理,提出基于方差变化率判据-四分位法组合的风电场风功率异常数据识别方法。首先,利用物理规则对原始风功率曲线进行预处理,剔除明显异常的数据;然后,利用风功率方差变化率判据法识别并清洗风功率曲线的堆积型异常功率数据点,判据的阈值借助箱型图自动获取;同时,利用四分位法识别并清洗剩余的离散型异常数据点;最后,通过算例验证了所提算法的可行性。研究结果表明所提算法具有易实现、效率高和通用性强的优点,其异常识别效果优于局部离群因子(local outlier factor,LOF)算法和Thompson tau-四分位算法,其耗时比LOF和Thompson tau-四分位算法分别减少9.6 s和0.49 s,且在5个不同位置的风电场验证了所提算法的通用性。