一类多变量Hammerstein模型的参数辨识方法

通过对系统输入信号的设计,使Hammerstein系统输出只反映系统的线性动态,并将非线性部分的静态影响有效地分离掉.利用最小二乘辨识得到系统的线性动态模型.基于此模型并依据系统的测量输出重构系统的中间输入,进而可估计出非线性部分的参数,据此给出了多变量Hammerstein系统辨识的动态分离方法.仿真结果表明所提出的方法是有效的.     

基于SVR的传感器Hammerstein模型辨识

提出一种基于支持向量回归机的非线性动态传感器Hammerstein模型辨识方法并给出了相关的数学理论及学习算法.在该模型中,用非线性静态子环节和线性动态子环节串联来描述传感器的非线性动态特性.再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型,并通过SVR求取中间模型参数.最后,推导出中间模型参数与传感器Hammerstein模型参数之间的关系,并由该关系实现非线性静态环节和线性动态环节的同时辨识.用实际力传感器动态标定实验数据进行测试,结果表明与常规非线性传感器辨识方法不同,所提方法只需进行一次动态标定实验就能给出非线性动态模型的数学解析表达式.且建立的力传感器Hammerstein模型阶次为4,而线性动态系统模型则需要6阶才能达到相同的精度.因此该研究为传感器非线性动态系统辨识又提供了一种可选方法.     

基于组合式信号源的Hammerstein-Wiener模型辨识方法

针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性.     

质子交换膜燃料电池的分数阶非线性状态空间模型研究

针对质子交换膜燃料电池(PEMFC)发电过程中的分数阶和非线性特性,本文提出了一种分数阶子空间辨识方法建立了PEMFC非线性状态空间模型.首先,为了降低建模复杂度,采用典型相关分析法和相关分析法确定了模型输入变量;其次,将分数阶微分理论与Hammerstein模型子空间辨识方法相结合,采用Poisson矩函数对输入输出数据进行预处理,构造了子空间辨识方法的输入输出矩阵,并引入分数阶短时记忆法减少辨识算法计算量;最后,选取多项式作为Hammerstein模型前端静态非线性环节,采用模糊遗传算法优化系统分数阶阶次和系数矩阵.仿真结果验证了算法的有效性,改进的辨识算法可以明显减小计算时间,所得PEMFC辨识模型能够准确地描述PEMFC的动态过程.     

基于高斯核函数的Hammerstein非线性系统辨识

提出了一种基于高斯核函数的Hammerstein非线性系统参数辨识方法。Hammerstein非线性系统由一个静态非线性模块和一个动态线性模块串联组成,利用高斯核函数神经网络和传递函数模型分别建立Hammerstein系统的静态非线性模块和动态线性模块。首先,基于可分离信号的输入输出数据,采用相关性分析方法估计动态线性模块的参数,有效抑制噪声的干扰。其次,针对Hammerstein非线性系统的不可测噪声项,利用残差的估计值代替不可测变量,推导了递推增广最小二乘辨识方法,根据随机信号的输入输出数据辨识静态非线性模块和噪声模型的参数。仿真结果表明,针对有色噪声干扰的Hammerstein非线性系统,所提方法具有较好的辨识精度和鲁棒性。     

状态空间模型下的Hammerstein系统的递推子空间辨识方法

一般来说, 单输入单输出情形下的Hammerstein系统可以由转移函数来表示, 而对于多输入多输出情形下的系统其输入与输出间的关系难以表示. 本文基于Hammerstein系统的状态空间模型, 研究了其子空间辨识方法. 在开环情形, 对Hammerstein系统给出了在其非线性函数可以由有限基函数线性表示的情形的子空间辨识方法, 及其递推实现. 并且初步分析了这些方法的渐近性质. 针对这一方法, 我们给出了一个数据模拟实例分析方法的优劣.     

一种多输入单输出Hammerstein系统的集成辨识方法

针对多输入单输出(MISO)Hammerstein系统提出了一种稳态与动态辨识相结合的集成辨识方法.该方法利用稳态信息获取稳态模型的强一致性估计,并通过稳态模型以神经网络获得其非线性逼近函数,再利用动态信息辨识获取多输入单输出(MISO)Hammerstein系统的线性子系统未知参数的一致性估计.仿真结果表明了该方法的有效性和实用性.     

多自由度非线性系统动态参数化模型建模方法研究

针对多自由度非线性系统的动态模型辨识问题,基于NARX(Non-linear Autoregressive with Exogenous inputs)模型的建模方法,考虑系统的物理设计参数,建立非线性系统动态参数化模型.首先,根据系统输入、输出数据建立系统不同参数下的NARX模型,并通过EFOR(Extended Forward Orthogonal Regression)算法对不同参数下NARX模型进行修正,以统一辨识得到的系统模型结构.随后,建立NARX模型系数与物理设计参数间的函数关系,得到多自由度非线性系统的动态参数化模型.以单输入、单输出两自由度非线性系统为例,根据数值仿真结果,对系统的动态参数化模型建模过程进行说明.最后,以带非线性涂层阻尼的悬臂梁作为试验对象,建立其动态参数化模型以反映其动力学特性.试验结果表明,非线性系统动态参数化模型能准确预测多自由度非线性系统的输出响应,为非线性系统的分析与优化设计提供了理论基础.     

非线性Hammerstein系统辨识的动态分离方法

利用同幅值的M序列和逆M序列作为输入信号, 对Hammerstein模型中的线性动态部分进行分离处理, 通过辨识得到一个线性动态模型. 基于此线性模型, 依据系统的测量输出重构出系统的中间输入. 最后由系统的测试输入和中间输入估计出非线性部分的参数. 仿真结果表明本方法的有效性.     

基于SVR的非线性动态系统建模方法研究

提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。用非线性静态子环节和线性动态子环节串联——Hammerstein模型来描述非线性动态系统。然后,通过函数展开将Hammerstein模型的非线性传递函数转换为等价的线性形式,从而建立起线性中间模型。再由SVR算法辨识出中间模型参数。最后,通过中间模型参数与Hammerstein模型参数之间的关系,实现原系统的非线性静态环节和线性动态环节的同时辨识。用非线性动态系统标定实验数据进行测试,建模结果表明所提方法具有如下优点：1)只需进行一次动态标定实验; 2)能给出非线性动态模型的数学解析表达式;3)充分利用SVR的优点,使所建模型具有更好的鲁棒性。该研究为非线性动态系统建模又提供了一种新方法。     

引射式跨声速风洞流场控制软件设计

目前提出的引射式跨声速风洞流场控制软件抽气端压力过大,导致排气阀气流排气速度变化过于剧烈;设计了一种新的引射式跨声速风洞流场控制软件,在风洞的控制程序中引入了马赫数和雷诺数,对控制质量进行试验检测,以实现风洞系统能够达到更精准快速的控制水平;在风洞流场控制系统中引入了解耦系统,对风洞测试各部分参数进行解耦筛选,提高各参数的准确度,有利于控制系统实现精准控制;实验结果表明,设计的引射式跨声速风洞流场控制软件能有效降低引射式跨声速风洞流场控制软件抽气端压力,使排气阀气流排气速度处于稳定状态。     

A multi-variate Hammerstein model for processes with input directionality

A new formulation of a block-structured model based on the Hammerstein operator is presented for the identification of multi-variate systems with input directionality. In contrast to the existing formulations for multi-variate Hammerstein models, the proposed structure offers the possibility to independently model the dynamic and nonlinear characteristics of the system and at the same time preserves the possibility to use the new efficient algorithms developed for the identification of single input Hammerstein models. Further, the formulation allows for a representation of arbitrary static nonlinear coupling of input variables with a considerably lower amount of parameters compared to existing formulations. The new model structure is applied to the identification of a fluid catalytic cracking (FCC) unit and significantly outperforms all previous multi-variate Hammerstein model structures by reducing the prediction error by over 50%.     

含有色噪声的神经模糊Hammerstein模型分离辨识

针对实际工业过程中普遍存在的有色噪声,本文提出一种基于递推增广最小二乘算法的神经模糊Hammerstein模型辨识方法,突破了传统的Hammerstein模型迭代分离算法.首先,利用多信号源实现Hammerstein模型中静态非线性环节和动态线性环节的分离,大大简化了辨识过程,提高了串联环节参数的分离精度.其次,利用长除法将噪声模型用有限脉冲响应模型逼近,采用增广递推最小二乘法进行线性环节的参数估计.最后,采用神经模糊模型拟合静态非线性环节,同时设计了神经模糊模型参数的非迭代优化算法,改善了模型的使用范围.该方法保证了模型的预测精度,对含有色噪声的非线性系统具有较好的拟合效果.仿真结果验证了上述方法的有效性.     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

Recursive identification for dynamic systems with backlash

A recursive algorithm for identification of nonlinear dynamic systems with backlash is proposed in this paper. In this method, the backlash, which is a non‐smooth function, is decomposed into a combination of a group of piecewise linearized models so that all the parameters of the backlash can be estimated separately. Moreover, the model of the backlash is embedded into a Hammerstein‐type model. Thus, a pseudo‐Hammerstein model with backlash is constructed. The estimation of the parameters for such a non‐smooth nonlinear system can be implemented through a so‐called recursive general identification algorithm (RGIA). Then, the corresponding convergence analysis of the RGIA for the model with backlash is also investigated. After that, two examples are presented to show the performance of the proposed method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fixed point iteration in identifying bilinear models

Inspired by fixed point theory, an iterative algorithm is proposed to identify bilinear models recursively in this paper. It is shown that the resulting iteration is a contraction mapping on a metric space when the number of input–output data points approaches infinity. This ensures the existence and uniqueness of a fixed point of the iterated function sequence and therefore the convergence of the iteration. As an application, one class of block-oriented systems represented by a cascade of a dynamic linear (L), a static nonlinear (N) and a dynamic linear (L) subsystems is illustrated. This gives a solution to the long-standing convergence problem of iteratively identifying LNL (Winer–Hammerstein) models. In addition, we extend the static nonlinear function (N) to a nonparametric model represented by using kernel machine.     

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model

A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities.The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone,based on which a universal-type parametric model can be established to approximate the entire system.This model can be established without separating the nonlinear part from the linear part.The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm.Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.     

Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

A new recursive algorithm is proposed for the identification of a special form of Hammerstein–Wiener system with dead-zone nonlinearity input block. The direct motivation of this work is to implement on-line control strategies on this kind of system to produce adaptive control algorithms. With the parameterization model of the Hammerstein–Wiener system, a special form of model estimation error is defined; and then its approximate formula is given for the following derivation. Based on these, a recursive identification algorithm is established that aims at minimizing the sum of the squared parameter estimation errors. The conditions of uniform convergence are obtained from the property analysis of the proposed algorithm and an adaptive setting method for a weighted factor in the algorithm is given, which enhances the convergence of the proposed algorithm. This algorithm can also be used for the identification of the Hammerstein systems with dead-zone nonlinearity input block. Three simulation examples show the validity of this algorithm.     

基于Hammerstein模型描述的非线性系统辨识新方法

Hammerstein模型常用来描述pH值或具有幂函数、死区、开关等特性的过程,本文提出了一种辨识此类对象模型结构和参数的新方法,首先将非线性静态部分和线性动态部分分别用非线性基和Laguerre级数表示,然后通过最小二乘法、矩阵特征值分解和矩阵扩维,辨识出两部分参数．并证明了该方法在输出端存在白噪声情况下误差的收敛性．此方法仅需假设输入为持续激励,适用范围广,计算简单,辨识精度高．最后通过pH中和滴定实验验证了以上结论．