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.     

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     

基于Haar 小波变换的连续时间系统鲁棒参数辨识

给出用Ｈａａｒ小波对连续时间系统的鲁棒辨识方法。该方法在用Ｈａａｒ小波对系统输入和输入展开时,通过极小化一个鲁棒指标来减少噪声对展开系数的影响。因此对连续时间系统可获得鲁棒参数估计。仿真结果表明了该算法的有效性。     

Adaptive Control Scheme for Large‐Scale Interconnected Systems Described by Hammerstein Models

This paper proposes an adaptive algorithm for the online control of discrete‐time large‐scale nonlinear systems, which reduces the noise effects acting on the system output (regulation problem) and allows the system output to keep track of a time‐varying trajectory (tracking problem). We consider a large‐scale nonlinear system that can be decomposed into single‐input single‐output (SISO) interconnected nonlinear subsystems with known structure variables (orders, delays) and unknown time‐varying parameters. Each interconnected subsystem is described by block‐oriented models, specifically a discrete‐time Hammerstein model. Parameter adaptation is performed using a recursive parametric estimation algorithm based on the adjustable model method and the least squares techniques. Simulation results of an interconnected petroleum process are provided to demonstrate the effectiveness of the developed control scheme.     

一类双线性Hammerstein模型的集成辨识方法

对于一类静态非线性增益具有原点对称特性的双线性Ｈａｍｍｅｒｓｔｅｉｎ模型,提出了一种稳态与动态辨识相结合的集成辨识方法。该方法利用稳态信号获得稳态模型的强一致性估计,并通过稳态模型获得非线性增益的估计,再利用动态信息辨识获得双线性Ｈａｍｍｅｒｉｓｔｅｉｎ模型的双线性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。     

A computational method for solving optimal control and parameter estimation of linear systems using Haar wavelets

In this article, a computational method based on Haar wavelet in time-domain for solving the problem of optimal control of the linear time invariant systems for any finite time interval is proposed. Haar wavelet integral operational matrix and the properties of Kronecker product are utilized to find the approximated optimal trajectory and optimal control law of the linear systems with respect to a quadratic cost function by solving only the linear algebraic equations. It is shown that parameter estimation of linear system can be done easily using the idea proposed. On the basis of Haar function properties, the results of the article, which include the time information, are illustrated in two examples.     

辨识Hammerstein模型的两步法

本文利用稳态和动态信息提出了一种辨识Ｈａｍｍｅｒｓｔｅｉｎ模型的新方法－两步法，该方法利用稳态信息获取非线性增益的强一致性估计，利用动态信息获取线性子系统未知参数的强一致估计，该方法具有计算简单和辨识精度高等优点，最后的仿真结果说明了该方法的有效性和实用性。     

Identification of linear time varying systems by Haar wavelet

This study addresses the identification of linear time varying systems. The identification is based on the expansion of all time functions in the state equations by Haar wavelets. The unknown time function can thus be identified in terms of Haar wavelets. A Haar wavelet is a set of complete, orthogonal basis and is easy to use computations. Several good properties of Haar wavelets are utilized in the algorithm. Both numerical and experimental results verify the analysis.     

图像的Haar小波变换系数范围的研究

本文研究了图像Haar小波变换的系数范围，包含本级中的系数的关系和不同级之间的关系。     

图像的Haar小波变换系数范围的研究

本文研究了图像Haar小波变换的系数范围,包含本级中的系数的关系和不同级之间的关系.     

一种新型非线性Hammerstein系统动态矩阵控制算法

将动态矩阵控制策略(DMC)推广到由一个非线性静态多项式函数和一个线性动态阶跃响应环节组成的非线性Ham-merstein系统,详细地给出了该新型非线性Hammerstein系统动态矩阵控制算法(NLH-DMC).把NLH-DMC应用于一套强非线性pH中和过程,给定值跟踪和抗干扰仿真结果表明,NLH-DMC比线性DMC(LDMC)和过程控制领域常用的非线性PID(NL-PID)具有更好的控制性能.进一步的仿真实验证实,NLH-DMC不仅具有良好的控制响应,而且在存在较大模型误差时仍具有很好的稳定性及鲁棒性.     

Hammerstein模型的改进新型神经动力学辨识方法及其在混合建模中的应用

推导了多输入多输出Hammerstein模型的矩阵格式,并提出了一种改进的新型神经动力学算法.应用此算法,可同时辨识出Hammerstein模型的多组未知参数,提高了收敛精度和速度.首先,对改进的新型神经动力学算法进行了参数的收敛性分析.之后,推导了基于Hammerstein模型的混合模型,并利用其建立实际模型与机理模型之间的偏差模型,具有很好的补偿效果.由于改进的新型神经动力学方法可以在线调整Hammerstein模型参数,所以混合模型可以准确地模拟复杂过程在大范围内的动态行为.实验表明该方法的合理性和有效性.     

一类偶型Hammerstein模型辨识的相关算法

在介绍了一类输出只与其输入的平方有关的偶型H模型之后,分析了现有相关算法无法估计此类模型参数的原因,证明了若采用非过零逆重复M序例做激励信号,则仍然可以用相关方法估计其模型参数,并导出了这一模型参数估计的新相关算法。此算法还可用于估计一般的H模型参数。     

应用Hammerstein模型辨识受相关噪声扰动的非线性系

本文提出了一种用于Ｈａｍｍｅｒｓｔｅｉｎ模型参数估计的偏差补偿最小二乘法。当观测数据被未知有色噪声污染时，应用本文方法可在缺少任何有关噪声先验信息的情况下实现参数的渐近无偏估计。     

应用退火演化算法辨识哈默斯坦模型

本文介绍了一种基于进化策略的新型模拟退火算法--退火演化算法,该算法将进化策略引入模拟退火算法中,通过变异和选择等步骤不断改善解的群体,而不是像传统的模拟退火算法那样仅仅采取单点迭代的方法。退火演化算法加强了对解的构成空间的整体把握,提高了搜索效率和精度,尤其对多峰函数具有良好的最优解搜索能力,并且具有较强的鲁棒性和灵活性。本文将其应用于哈默斯坦模型的参数辨识,仿真结果说明了该方法的有效性。     

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.     

一种辨识Hammerstein模型的新方法

本文结合参数估计理论和函数逼近论中的一些理论结果,提出了一种开环辨识Hammerstein模型的新方法.这种方法能够克服目前广泛采用的辨识Hammerstein模型的方法中的缺点,获得模型线性动态部分参数的渐近无偏估计及无记忆非线性特性,具有接近最佳逼近效果和较好收敛性的逼近多项式.     

非线性动态传感器系统的H模型神经网络辨识

针对非线性动态传感器模型辨识问题,提出一种新的Hammerstein模型神经网络结构辨识法。非线性动态传感器系统采用Hammerstein模型描述,将系统分解为非线性静态增益串接线性动态环节。再设计一种网络权系数对应于相应的Hammerstein模型参数的新型神经网络结构,推导了基于反向传播的网络权系数调整方法。通过网络迭代训练同时得到静态与动态两个环节的模型参数。最后通过一个H模型的数值仿真来验证方法的有效性,仿真结果表明所提辨识方法是有效的。     

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

A novel parameter learning scheme using multi-signal processing is developed that aims at estimating parameters of the Hammerstein nonlinear model with output disturbance in this paper. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the multi-signals are devised to estimate separately the nonlinear block parameters and the linear block parameters; the parameter estimation procedure is greatly simplified. Firstly, in view of the input–output data of separable signals, the linear block parameters are computed through correlation analysis method, thereby the influence of output noise is effectively handled. In addition, model error probability density function technology is employed to estimate the nonlinear block parameters with the help of measurable input–output data of random signals, which not only controls the space state distribution of model error but also makes error distribution tends to normal distribution. The simulation results demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the effectiveness of the developed approach.     

Two-step output feedback predictive control for Hammerstein systems with networked-induced time delays

This paper is mainly concerned with the design problem of two-step model predictive control (MPC) for nonlinear systems represented by Hammerstein model, where the network-induced time delays exist between sensor to controller (S2C) and controller to actuator (C2A) links. We assume that the system state is not measurable, so the state observer is employed to estimate the state. The intermediate variable for the linear part of the system is calculated by minimising the quadratic performance function. The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example.