多变量输出误差系统的辅助模型随机梯度辨识算法

考虑了多变量输出误差系统的辨识问题. 使用系统可得到的输入输出数据构造一个辅助模型, 用辅助模型的输出代替信息向量中的未知变量, 提出了一个基于辅助模型的随机梯度辨识算法. 使用鞅收敛定理的收敛性分析表明: 提出的算法给出的参数估计收敛于它们的真值. 给出了带遗忘因子的辅助模型随机梯度算法来改进参数估计精度, 仿真结果证实了提出的结论.     

具有限定记忆的辅助变量参数辨识法与仿真研究

最小二乘参数辨识法可用于动态系统、静态系统、线性系统、非线性系统的参数估计。可用于离线估计,也可用于在线估计。最小二乘辨识法简单、实用,其递推算法收敛可靠,并且当模型噪声为白噪声时,可得到无偏、一致和有效的估计,从而得到广泛的应用。但当模型噪声是有色噪声时,最小二乘参数估计不是无偏、一致估计,并且随着数据的增长,最小二乘递推辨识算法将出现数据饱和现象,以致递推算法慢慢失去修正的能力。辅助变量递推算法解决了噪声的模型结构不确定且模型噪声是有色噪声时,最小二乘参数估计的元偏性和一致性问题,但依然存在数据饱和问题。为此在辅助变量递推算法的基础上引入限定记忆方式,获得了具有限定记忆的辅助变量参数估计递推算法,解决了辅助变量递推算法的数据饱和问题。仿真结果表明了该算法的有效性。     

有色噪声干扰下多变量系统的辅助模型辨识方法

针对有色噪声干扰的多输入多输出系统模型辨识问题,提出了多变量系统的FIR辅助模型辨识方法;其辨识思想是把传递函数矩阵中的子模型等价成辅助模型—有限脉冲相应(FIR)模型,然后利用辨识得到的辅助模型估计输出向量的子子模型,最后利用递推最小二乘算法或帕德近似化方法得到子子模型的参数估计,并通过仿真来验证算法的性能。     

基于辅助模型和数据滤波的伪线性回归系统参数估计方法

针对伪线性输出误差回归系统的辨识模型新息信息向量存在不可测变量的问题,首先通过构造一个辅助模型,用辅助模型的输出代替未知中间变量,推导得到的基于辅助模型的递推最小二乘参数估计算法计算量较大,但算法的辨识效果不佳。进一步采用估计的噪声模型对系统观测数据进行滤波,使用滤波后的数据进行参数估计,从而推导提出了基于数据滤波的递推最小二乘参数估计算法。仿真结果表明,所提算法能够有效估计伪线性回归线性输出误差系统的参数。     

基于辅助模型和数据滤波的伪线性回归系统参数估计方法

针对伪线性输出误差回归系统的辨识模型新息信息向量存在不可测变量的问题,首先通过构造一个辅助模型,用辅助模型的输出代替未知中间变量,推导得到的基于辅助模型的递推最小二乘参数估计算法计算量较大,但算法的辨识效果不佳。进一步采用估计的噪声模型对系统观测数据进行滤波,使用滤波后的数据进行参数估计,从而推导提出了基于数据滤波的递推最小二乘参数估计算法。仿真结果表明,所提算法能够有效估计伪线性回归线性输出误差系统的参数。     

基于辅助模型的多新息广义增广随机梯度算法

将辅助模型辨识思想与多新息辨识理论相结合,利用系统可测信忠建立一个辅助模型.分别用辅助模型输出和噪声估计值代替辨识模型信忠向量中未知真实输出变量和不可测噪声项,并引入新忠长度扩展标量新息为新息向量,提出了Box-lenkins模型的辅助模型多新忠广义增广随机梯度辨识方法.所提出方法重复使用系统数据,能够改善参数估计精度,加快算法的收敛速度.     

双输入多率系统辅助模型增广随机梯度算法

针对有色噪声干扰的双输入多率系统,为解决辨识模型信息向量中存在未知变量和不可测噪声项的问题,结合辅助模型思想和递推增广随机梯度算法的优点,用辅助模型的输出代替系统的未知变量,用估计残差代替信息向量中的不可测噪声项,进而提出了双输入多率系统的辅助模型增广随机梯度算法。为了提高辨识算法的收敛速度和改善参数估计精度,在算法中引入遗忘因子,得到相应的辅助模型带遗忘因子增广随机梯度算法。仿真实例说明,引入遗忘因子,能加快算法的收敛性,提高参数估计精度。     

基于辅助变量的压缩采样匹配追踪闭环系统辨识方法

针对被控对象和反馈通道均具有未知时滞的闭环系统,提出一种基于辅助变量的压缩采样匹配追踪辨识方法.该方法利用辅助变量方法对压缩采样匹配追踪算法进行改进,获得过参数化辨识模型稀疏参数向量的估计,根据稀疏向量的结构得到前向通道的参数估计和时滞估计,进而根据模型等价原理获得反馈通道的参数估计.仿真结果表明,所提出方法仅需少量的迭代即可获得这类闭环系统参数与时滞的有效估计.     

MISO系统辅助模型多新息增广随机梯度算法

对于有色噪声干扰的输出误差多输入单输出(MISO)系统,常规的递推最小二乘辨识方法给出的参数估计是有偏的.为了提高随机梯度辨识方法的收敛精度和速度,用辅助模型的输出代替辨识模型信息向量中的未知不可测变量,推导出其辅助模型增广随机梯度辨识算法;再引入新息长度扩展标量新息为新息向量,提出了基于辅助模型的MISO系统多新息增广随机梯度辨识算法.所得算法在每一次的迭代中不仅使用了当前数据和新息,而且使用了过去数据和新息,提高了参数估计精度和收敛速度.仿真例子验证了算法的有效性.     

一种闭环条件下的多变量系统辨识方法

为得到带有色噪声的闭环多变量系统参数无偏估计,提出两阶段随机梯度辨识算法,此算法利用中间模型将闭环问题转化成两个开环问题,并基于随机梯度算法和递阶辨识原则,用最速下降法极小化预测误差准则来得到参数估计.算法只需一个测试信号,不需要控制器的先验知识,计算简便,能获得满意的结果,适用于闭环多变量系统的在线辨识.     

Modelling of an hydraulic excavator using simplified refined instrumental variable （SRIV） algorithm

Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator＇s arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable （SRIV） identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the.real machine motion control exoeriments.     

Modelling of an hydraulic excavator using simplified refined instrumental variable (SRIV) algorithm

Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator's arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable (SRIV) identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the real machine motion control experiments.     

反馈未知闭环系统的MRIV法辨识

本文提出了一种改进的精致辅助变量法.这种方法适合于反馈未知的闭环系统的参数 估计.本文在理论上分析了此方法的一致性,并通过Monte Carlo仿真实验证实了方法的 有效性.     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

System identification of the upper part of Murray River

Data based modelling is an important tool for obtaining models of rivers. In this paper we consider several identification methods, namely Prediction Error Method, Maximum Likelihood, continuous time system identification, Refined Instrumental Variable method (used within the context of Data-Based Mechanistic modelling) and Subspace Identification Method, and apply them to real data from the Murray River in Australia. Both Multiple-Input Single-Output models where the output is the water level in a lake and Multiple-Input, Multiple-Output models where in addition a flow is also modelled, are considered. The models are compared in terms of their accuracy on validation data and on how easily the methods can incorporate prior knowledge.     

舰艇发电机励磁系统参数辨识研究

介绍辅助变量法与粒子群优化算法在舰艇发电机励磁系统辨识中的应用,并在Labview软件中编写了相应的计算程序。以某舰艇发电机励磁系统为例,在Matlab/Simulink中搭建该励磁系统仿真模型,将采样获得的输入输出数据,输入Labview辨识软件中,估计各参数值。实验分别采用+10%、+50%阶跃响应,辨识系统线性模型和非线性模型,并比较不同噪声幅值情况下的PSO辨识结果。实验结果证明PSO算法在励磁系统参数估计中的有效性。     

A generalized instrumental variable estimation method for errors-in-variables identification problems

The errors-in-variables identification problem concerns dynamic systems whose input and output variables are affected by additive noise. Several estimation methods have been proposed for identifying dynamic errors-in-variables models. In this paper it is shown how a number of common methods for errors-in-variables methods can be put into a general framework, resulting into a Generalized Instrumental Variable Estimator (GIVE). Various computational aspects of GIVE are presented, and the asymptotic distribution of the parameter estimates is derived.     

A unified framework for EIV identification methods when the measurement noises are mutually correlated

In this paper, the previously introduced Generalized Instrumental Variable Estimator (GIVE) is extended to the case of errors-in-variables models where the additive input and output noises are mutually correlated white processes. It is shown how many estimators proposed in the literature can be described as various special cases of a generalized instrumental variable framework. It is also investigated how to analyze the common situation where some of the equations that define the estimator are to hold exactly, and others to hold approximately in a least squares sense, providing a detailed study of the accuracy analysis.     

Lattice implementation of the instrumental variable method: Shift and delta operator formulations

The lattice forms of the Instrumental Variable (IV) method is derived, for the standard shift operator. The extension to the delta operator formulation, for cases where high sampling rates cause numerical difficulties, is also discussed. For the sake of brevity, however, some of the routine technical details are omitted. The derivation yields the complete multi-channel and vector-channel (or multi-experiment) lattice algorithms. The overall approach is based on the non-orthogonal projections, and employs some of the techniques used in the lattice derivation of the least-square problem, where the projections are orthogonal. Complete algorithms, with appropriate end conditions, are presented, as well as an example to compare the estimates obtained from the basic least square methods to those from the instrumental variable method.     

一种离散全程滑模变结构控制方法

针对离散时间系统提出了一种全程滑模变结构控制器设计方法。通过选择合适的滑模切换函数，使系统的轨线一开始便落在滑模面上，有效地缩短了系统状态到达滑动模态的时间，使系统从初始状态到平衡点的全过程都具有鲁棒性。针对利用离散趋近律方法设计全程滑模变结构控制器系统存在抖振的缺点，本文利用常规等效控制方法设计离散全程滑模变结构控制器，消除了系统的抖振，改善了系统的动态性能。并基于倒立摆模型进行了仿真，结果验证了其有效性。