Nolinear model predictive control using Hammerstein models

Nonlinear models that are composed of a linear dynamic element in series with a nonlinear static element prove to be very attractive in describing the behaviour of many chemical processes. In this paper, a model predictive control scheme is proposed using the Hammerstein model structure. Two simulation examples, a pH neutralization process and a binary distillation column, are used to demonstrate the effectiveness of the method.     

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.     

Quasiconvexity analysis of the Hammerstein model

In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.     

Identification of nonlinear systems using a piecewise-linear Hammerstein model

In the paper a method for nonlinear system identification is proposed. It is based on a piecewise-linear Hammerstein model, which is linear in the parameters. The model and the identification algorithm are adapted to allow the parameter identification in the presence of a special form of the excitation signal. The identification method is derived from a recursive least-squares algorithm, which is properly adapted to take into account the proposed model structure and the properties of the identification signal. The applicability of the approach is illustrated by an example in which a discontinuous nonlinear static function is connected to a dynamic block.     

Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.     

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.     

基于预测模型的浮选过程pH值控制

矿浆pH值是泡沫浮选过程中的一个非常重要的被控量.目前,多数选厂的矿浆pH值控制基本是依靠现场工人定期对矿浆样本进行pH值测量,凭主观经验对pH调整剂进行调整.由于操作工人的主观性和随意性的影响以及矿浆样本pH值测量与药剂调整间存在的较长的时间滞后,矿浆pH值波动频繁,很难使矿物浮选保持在一个稳定最优生产状态下运行.为了使矿浆pH值保持在一个期望的生产状态,基于浮选泡沫表面视觉信息提出了一种新的矿浆pH值控制方法,分别采用基于泡沫视觉信息的自适应遗传混合神经网络AG-HNN和自适应遗传PID(AG-PID)控制方法建立了矿浆pH值预测模型和pH值控制模型,基于所建立预测和控制模型对浮选药剂用量进行调整,解决了浮选矿浆pH值波动问题.工业浮选现场的实验结果表明该方法可以使矿浆pH值保持在一个期望的范围内,有效提高浮选性能.     

Quantifying the accuracy of Hammerstein model estimation

This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein model-structure estimate, and a key finding is that the process of estimating the non-linear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this effect is not explained simply by way of considering how the input spectrum is changed by the non-linearity. Instead, it arises that the linear model-estimate variability may be dominated by a term that depends on the frequency response of the linear system itself. Amongst other things, the main results derived here have experiment design implications for Hammerstein system estimation.     

Hammerstein模型辨识的回顾及展望

Hammerstein模型是一类具有特定结构的典型非线性模型,由静态非线性环节和动态线性环节串联而成,能较好地反映过程特征的特点,可以描述一大类非线性过程.本文结合Hammerstein模型辨识的基本过程和特点,从Hammerstein模型中间变量不可测量的角度出发,首先按静态非线性环节与动态线性环节同步辨识法和分步辨识法综述了Hammerstein模型的相关理论和方法;然后,分析了现有的基于Hammerstein模型的控制系统设计方案;最后对Hammerstein系统未来可能的研究提出若干看法.     

Identification of MIMO Hammerstein systems using cardinal spline functions

A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials.     

Design of a nonlinear controller based on a piecewise-linear Hammerstein model

This paper presents a nonlinear controller, which can be used to control single-input single-output nonlinear systems that can be approximated in terms of Hammerstein models. The proposed nonlinear controller is based on the principles of a classic linear pole placement controller and the piecewise-linear Hammerstein model. The controller can be used to control processes with highly nonlinear or even discontinuous static functions, while keeping simple controller structure and a very low computational burden.     

基于Hammerstein 模型的感应电机变频器调速系统神经网络控制

针对感应电机变频器调速系统的非线性特点,提出一种基于Hammerstein模型的神经网络控制方法。 Hammerstein模型由静态非线性模块和动态线性模块组成。首先,利用ARMA模型实现对感应电机变频器调速系统的线性动态模块辨识;然后,基于该辨识模型,实现调速系统非线性静态模块神经网络逆模型辨识与系统直接逆控制;最后,针对控制过程中存在的电机负载扰动问题,设计了神经网络直接逆控制器在线学习与控制策略。仿真实验表明,所提出的控制策略可以获得满意的控制效果。     

采用Hammerstein模型的非线性预测控制

对于象ｐＨ中和，高纯度分离以及化学反等过程的控制，由于其过程本身的严重非线性而变得十分困难。本文提出了一种采用Ｈａｍｍｅｒｓｔｅｉｎ模型的预测控制方法来控制诸如上述的非线性过程，Ｈａｍｍｅｒｓｔｅｉｎ模型用两种方法进行辨识：联立辨识法与序贯识法，特别地，本文提示了一种改进型Ｈａｍｍｅｒｓｔｅｉｎ模型用于克服常规Ｈａｍｍｅｒｓｔｅｉｎ模型在控制器设计时的不足之处，对－ｐＨ中和过程的仿真结果表明，基于     

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.     

废气氧传感器Hammerstein模型结构的确定

研究了废气氧(EGO)传感器Hammerstein模型结构辨识方法。静态非线性函数选用双曲正切与多项式组合形式,动态线性环节分别选用带外生变量的自回归(ARX)模型、输出误差(OE)模型和Box-Jenkins(BJ)模型结构。采用交叉准则法进行参数估计和阶次选择,通过仿真比较对模型进行检验。结果表明:最终输出误差(FOE)准则和最终预报误差(FPE)准则均适用于用估计数据选择阶次,但前者比后者更可靠。基于预测误差法的3阶OE模型和BJ模型均可用于EGO传感器Hammerstein模型动态线性环节的建模。     

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%.     

A noniterative neuro-fuzzy based identification method for Hammerstein processes

In this paper, a noniterative identification procedure for neuro-fuzzy based Hammerstein model is presented. The proposed method not only avoids the inevitable restrictions on static nonlinear function encountered by using the polynomial approach, but also overcomes the problems of initialization and convergence of the model parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the identification of Hammerstein model. To construct the neuro-fuzzy based model, a clustering algorithm is presented to estimate the centers and widths of the model, and an analytical solution is developed to calculate the weights of the model in a noniterative manner. Examples are used to illustrate the applicability of the proposed method and a comparison with polynomial approach is made.     

具有Hammerstein模型描述的非线性系统的基于混合神经网络的预测控制

本文针对多输入多输出Hamrnerstein模犁提出了一种基于混合神经网络的模犁预测控制策略,控制器采用线性优化机构和高斯径向基神经网络串联.该策略不需要假设Hammerstein模型的非线性部分由多项式构成,避免了已有研究在无根或重根情况下存在导致预测控制的优化特征丧失问题,而采用混合神经网络则避免了采用传统神经网络拟合动态映射时存在的网络规模大和实时性差的不足.     

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.     

Augmented Hammerstein model for the calibration of six‐port based dual band wireless receivers

Two architectures of concurrent dual‐band six‐port‐based receiver (SPR), which are modeled and calibrated using the augmented Hammerstein model (AHM) are proposed for the first time in this article. The receivers are based on six‐port junctions with one or two local oscillators (LO). The proposed single step calibration algorithms achieve the recovery of the two in‐phase (I₁ and I₂) and quadrature (Q₁ and Q₂) components of an RF signal with two frequency components (RF₁ and RF₂). Experimental validations have been performed to verify the performance of the proposed concurrent dual‐band receivers and to test the efficiency of the AHM based calibration algorithms. As a performance metric, the Error Vector Magnitude (EVM) has been measured to compare the transmitted and recovered baseband signals and to evaluate the performance and efficiency of the proposed calibration algorithms for the two receiver topologies. The IQ data has been recovered with EVMs no higher than 2% for the two LOs based receiver excited with a QAM modulated dual‐band RF signal. The single LO based receiver has been tested with a dual‐band LTE signal and the recovered IQ data exhibited EVMs no higher than 4%.