Modelling of ultrasonic motor with dead-zone based on Hammerstein model structure

The ultrasonic motor (USM) possesses heavy nonlinearities which vary with driving conditions and load-dependent characteristics such as the dead-zone. In this paper, an identification method for the rotary travelling-wave type ultrasonic motor (RTWUSM) with dead-zone is proposed based on a modified Hammerstein model structure. The driving voltage contributing effect on the nonlinearities of the RTWUSM was transformed to the change of dynamic parameters against the driving voltage. The dead-zone of the RTWUSM is identified based upon the above transformation. Experiment results showed good agreement be- tween the output of the proposed model and actual measured output.

Digital Cancellation Scheme and Hardware Implementation for High-Order Passive Intermodulation Interference Based on Hammerstein Model

Passive intermodulation(PIM)interference urgently needs to be solved in the satellite communication system,owing to degrading the whole performance.Mainstream research contributions to the cancellation method for PIM were focused on the analog domain,however,the PIM distortion cannot be eliminated completely with the approaches.Meanwhile,some researchers attempt to tackle the problem through digital signal processing,nevertheless,the proposed methods were not suitable for the practical satellite communication scenario.In this paper,we present a general scheme for the adaptive feedforward PIM cancellation.High-order PIM signals at baseband are estimated by modeling the PIM distortion with Hammerstein model in the digital domain.Based on the reconstructed PIM signal,we adopt the least mean square algorithm to adaptively mitigate the PIM interference for tracking the variation of PIM.The time and frequency synchronization of PIM are based on the correlation of the peak of received signals with the corresponding reconstructed PIM signal.Practical experimental results show that the scheme can effectively cancel the PIM interference,and achieve an interference suppression gain more than 20dB.

Identification of fractional order Hammerstein models based on mixed signals

An algorithm based on mixed signals is proposed,to solve the issues of low accuracy of identification algorithm,immeasurable intermediate variables of fractional order Hammerstein model,and how to determine the magnitude of fractional order.In this paper,a special mixed input signal is designed to separate the nonlinear and linear parts of the fractional order Hammerstein model so that each part can be identified independently.The nonlinear part is fitted by the neural fuzzy network model,which avoids the limitation of polynomial fitting and broadens the application range of nonlinear models.In addition,the multi-innovation Levenberg-Marquardt(MILM)algorithm and auxiliary recursive least square algorithm are innovatively integrated into the parameter identification algorithm of the fractional order Hammerstein model to obtain more accurate identification results.A simulation example is given to verify the accuracy and effectiveness of the proposed method.

PEMFC Identification Based on a Fractional-Order Hammerstein State-Space Model with ADE-BH Optimization

Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.

基于Hammerstein模型的执行机构非线性参数辨识

针对火电机组中流过执行机构的介质流量难以测量,导致执行机构的非线性特性无法直接求取这一问题,提出用构建Hammerstein模型代替直接测量介质流量的间接测量法,进而求取执行机构的非线性特性,然后分别使用粒子群算法(PSO)和樽海鞘群算法(SSA),辨识所构建的Hammerstein模型的参数。另外,针对PSO算法和SSA算法辨识Hammerstein模型参数精度不高以及收敛速度慢的问题,提出了一种改进的粒子群-樽海鞘群的混合算法(IPS)。最后基于烟道挡板的指令数据与再热器出口温度数据对模型进行了仿真。仿真结果表明,提出的IPS算法能改善PSO算法的过早收敛问题,提高SSA算法的辨识速度。因此通过建立Hammerstein模型能够解决介质流量难以测量的执行机构非线性参数辨识问题,并且提出的IPS算法能准确且快速的辨识Hammerstein模型的各项参数。

Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.

Recursive least squares identification for piecewise affine Hammerstein models

Purpose-The purpose of this paper is to probe the recursive identification of piecewise affine Hammerstein models directly by using input-output data.To explain the identification process of a parametric piecewise affine nonlinear function,the authors prove that the inverse function corresponding to the given piecewise affine nonlinear function is also an equivalent piecewise affine form.Based on this equivalent property,during the detailed identification process with respect to piecewise affine function and linear dynamical system,three recursive least squares methods are proposed to identify those unknown parameters under the probabilistic description or bounded property of noise.Design/methodology/approach-First,the basic recursive least squares method is used to identify those unknown parameters under the probabilistic description of noise.Second,multi-innovation recursive least squares method is proposed to improve the efficiency lacked in basic recursive least squares method.Third,to relax the strict probabilistic description on noise,the authors provide a projection algorithm with a dead zone in the presence of bounded noise and analyze its two properties.Findings-Based on complex mathematical derivation,the inverse function of a given piecewise affine nonlinear function is also an equivalent piecewise affine form.As the least squares method is suited under one condition that the considered noise may be a zero mean random signal,a projection algorithm with a dead zone in the presence of bounded noise can enhance the robustness in the parameter update equation.Originality/value-To the best knowledge of the authors,this is the first attempt at identifying piecewise affine Hammerstein models,which combine a piecewise affine function and a linear dynamical system.In the presence of bounded noise,the modified recursive least squares methods are efficient in identifying two kinds of unknown parameters,so that the common set membership method can be replaced by the proposed methods.

Modeling of a distillation column based on NARMAX and Hammerstein models

The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior.This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input(NARMAX)model,and a Hammerstein model to approximate the evolution of the overhead temperature in a separation system.The model development and validation are studied through experiments carried out on a distillation plant of laboratory scale.Three model order selection criteria such as Aikeke’s Information Criterion(AIC),Root Mean Square Error(RMSE)and Nash–Sutcliffe Efficiency(NSE)are used to evaluate the prediction performance of the process behavior.The results illustrate that both models produce acceptable predictions but the NARMAX model outperforms the Hammerstein model.

The Predictive Control Based on Hammerstein Model with NU=1

In acs paper,the generalized predictive control(GPC)law for Hammerstein model with control horizon NU=1 is presented and the algebraic equation satisfied by the GPC law is derived.Also,the simulation study shows tha tthe GPC based on Hammerstein system is such and algorithm which can be controlled by numerical computer with rather strong Robustness but without strict demand for the model.

基于滤波的分段线性Hammerstein系统的递推辨识方法

Hammerstein模型具有结构简单、能很好地反映典型非线性特性等优点,一直是控制领域的重要研究内容之一.本文主要研究输出误差自回归Hammerstein系统的辨识问题,系统的输入非线性部分采用分段线性函数拟合,并引入切换函数和位置函数将其表示为线性参数表达式.为克服有色噪声的干扰,本文通过关键项分离和数据滤波技术,建立系统的滤波辨识模型.在此基础上,文中提出了基于滤波的遗忘梯度算法,基于滤波的递推广义最小二乘算法和基于滤波的多新息遗忘梯度算法估计未知参数.本文通过仿真实例验证了所提算法的有效性,证明了多新息理论的应用可以有效地提高递推算法的辨识性能.

Hammerstein-Wiener时变系统的带遗忘因子学习辨识算法

针对一类有限区间上重复运行的Hammerstein-Wiener非线性时变系统,将Hammerstein-Wiener系统输出非线性部分进行多项式展开以构造回归模型,采用带遗忘因子迭代学习梯度算法和带遗忘因子迭代学习最小二乘算法,估计系统的时变参数。当系统参数沿时间轴快变、沿迭代轴缓变时,修正遗忘因子提高算法的辨识精度。文中分别给出了2种算法的推导过程并进行仿真对比验证,结果表明,带遗忘因子迭代学习最小二乘算法收敛速度更快、精度更高,达到相同性能指标时所需迭代次数更少,验证了所提学习算法的有效性。

基于Hammerstein-Wiener模型的CSTR反应器辨识

针对化工过程中广泛应用的连续搅拌反应釜(CSTR)反应器,提出一种新的基于极限学习机的Hammerstein-Wiener模型的辨识建模方法。其中,Hammerstein-Wiener模型的两个非线性环节采用两个不同的极限学习机逼近,线性环节采用自回归ARX模型。因极限学习机的特殊结构,此模型可以表示成线性回归的形式,最终利用广义最小二乘法求解模型的参数。此方法辨识过程简单,辨识过程的计算量较小。最后对CSTR的辨识结果表明,在相同条件下与基于多项式的Hammerstein模型和ARX-LSSVM Hammerstein模型相比,该方法具有较高辨识精度,表明了该方法的有效性。

基于Hammerstein模型的风力发电系统建模与辨识

为建立风力发电系统的高精度模型,研究了一种基于Hammerstein模型的风力发电系统的建模与辨识方法。使用3σ准则对异常数据进行剔除,利用剔除后的数据训练风力发电系统的标称模型。利用Hammerstein模型建立风力发电系统的数据驱动模型,并将可分离信号和实际风速构成的组合式信号作为Hammerstein模型输入,可分离信号经过标称模型的输出和实际功率作为Hammerstein模型输出,基于组合式信号的输入和输出数据,利用相关分析法和带遗忘因子的递推增广随机梯度方法分别辨识Hammerstein模型中静态非线性子系统和动态线性子系统的参数。采用实际风速数据进行仿真实验,提出的方法与增广随机梯度方法的平均绝对百分比误差分别为4.99%和14.73%,与增广随机梯度方法相比,提出方法的平均绝对百分比误差减少了9.74%。仿真结果表明,提出的方法能够有效辨识Hammerstein模型风力发电系统,具有较好的预测性能。

An improved constrained model predictive control approach for Hammerstein-Wiener nonlinear systems

Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.

Model predictive control synthesis algorithm based on polytopic terminal region for Hammerstein-Wiener nonlinear systems

An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.

有色噪声干扰下Hammerstein非线性模型辨识

针对非线性Hammerstein模型存在滑动平均噪声干扰问题,提出了一种智能分离辨识方法.利用多项式模型和状态空间模型分别建立Hammerstein模型的非线性子系统和线性子系统,并设计组合信号分离辨识Hammerstein模型的静态非线性子系统和动态线性子系统.首先,分析了二进制信号不激发非线性子系统特性,推导出基于辅助变量的增广最小二乘方法辨识动态线性子系统和噪声模型的参数,有效削弱了滑动平均噪声的干扰.其次,为了提高随机梯度辨识方法的辨识精度和收敛速度,推导了遗忘因子增广随机梯度方法,基于随机信号的输入/输出数据辨识静态非线性子系统的参数.仿真结果表明,本文推导的方法能够辨识滑动平均噪声干扰下Hammerstein模型,与递推增广最小二乘方法、迭代梯度方法和辅助模型递推增广最小二乘方法相比,提出的方法能够取得较高的辨识精度.

基于Wiener模型和Wiener-Hammerstein模型的非线性动态过程完整建模方法

基于Wiener模型和Wiener-Hammerstein模型提出Wiener结构神经网络(WienerNN)模型和Wiener-Hammerstein结构神经网络(WHNN)模型,推导了Wiener模型和Wiener-Hammerstein模型的泛化能力,并给出了模型神经网络化的全过程。将所推导出的WienerNN模型和WHNN模型用于单变量和多变量非线性动态过程建模,验证了模型的有效性。

Hammerstein系统的子迭代更新多新息梯度估计方案

针对Hammerstein系统阶次未知和多新息长度限制辨识性能问题,提出行列式比确定阶次信息和基于子迭代更新多新息梯度辨识算法。结合半分解技术,构建线性和非线性参数相互分离的辨识模型。为便于后续参数辨识,基于参考模型原理和原始系统设计行列式比算法确定系统阶次信息。根据多次迭代更新理论,将给定多新息更新分解为子新息多次迭代更新,改善辨识性能,解决多新息长度限制辨识性能问题。基于殃差收敛定理,从理论上严格分析提出方案的收敛性能。结果表明,与已有的辨识方案相比,本文方案能有效辨识系统的参数信息,具有较高的辨识性能。

压电陶瓷作动器的卡尔曼滤波输出反馈预测控制

为了抑制压电陶瓷的迟滞、非线性特性对压电微定位平台精度的影响,建立反映压电陶瓷作动器频率依赖迟滞非线性特性的Hammerstein模型:利用非对称Bouc-Wen模型来表示静态迟滞非线性和利用动态线性模型表征信号频率的影响。针对迟滞补偿器不能完全的进行补偿,并且存在噪音干扰实验设备,采用基于卡尔曼滤波的预测控制,提高压电微定位平台的控制精度。预测控制用来减小逆补偿误差以及建模误差等模型不确定性的影响,卡尔曼滤波用来获得系统的状态估计值。实验结果显示,该控制器在正弦波信号下的相对跟踪误差小于0.68%,三角波信号下相对跟踪误差小于0.70%。在迟滞补偿的基础上,采用卡尔曼滤波的预测控制,该方法可以有效地完成对微定位平台的高精度跟踪。

基于一类非线性时间序列模型的锂离子动力电池建模研究

伴随着新能源产业的飞速发展,锂离子动力电池作为一种高效的储能方式,已成为电动汽车的重要组成部分。在电池管理系统的功能中,电池的高精度建模至关重要。在实际应用中,电池不是一个线性系统,其输入和输出由于外部扰动等原因表现出非线性特征,从而直接影响参数识别效果,进而影响模型精度。鉴于此,本文对锂离子动力电池进行了Hammerstein-ARMAX(Autoregressive MovingAverage with Extra Input)模型构建,并对模型参数的估计方法进行研究,旨在提高模型的准确性。实验结果表明了该方法的有效性。