一种含控制滞后的Wiener模型的辩识方法

研究了含控制滞后Ｗｉｅｎｅｒ模型的辨识。在一定条件下，这一模型满足一组回归函数。利用Ｆｏｕｒｉｅｒ 级数部分和可以求得回归函数在若干点上的估计。     

基于通用学习网络的预估控制

利用通用学习网络具有所有节点互连，任意两节点之间可以有多重连接，且连接允许有任意延迟时间的特点，对典型非线性、大滞后系统进行了辨识．结合PID控制器对pH中和过程实现了高精度的预估控制．通过与传统的Smith预估控制的比较，证明该网络能有效地应用于大滞后系统，利用该网络进行系统辨识是对未知对象模型控制的一种有效新方法．     

基于支持向量回归的非线性系统辨识

本文将支持向量回归方法应用于非线性系统辨识问题．基于高斯支持向量回归及ε不敏感损失函数的基本思想，本文提出一个非线性系统辨识的新算法，并将其与用于系统辨识的径向基函数神经网络进行了比较．模拟实验表明，支持向量回归方法可以成为非线性系统辨识的有力工具．     

一种通用学习网络自适应算法及其在预测控制中的应用

针对黑箱过程的辨识与控制,本文提出了一种选择通用学习网络(universal learning network,ULN)节点间延迟时间参数的自适应算法,并将其应用于对控制对象中的纯滞后参数的辨识．将通用学习网络与PID控制器相结合,应用于包含大滞后的系统的模型预测控制(model predictive control,MPC)中．仿真结果证明通用学习网络能够有效地辨识被控对象的纯滞后时间,并能够作为预估器应用于模型预测控制系统中．     

基于支持向量机N4SID辨识模型的非线性预测控制

针对工业控制领域中非线性系统的模型辨识与预测控制问题,采用最小二乘支持向量机回归方法构造非线性函数,运用状态子空间（N4SID）模型辨识方法辨识非线性状态空间模型.在此基础上建立非线性预测控制器,利用拟牛顿算法进行非线性预测控制律的求解,从而实现了一种新的基于支持向量机N4SID辨识模型的非线性预测控制算法.仿真实验验证了该算法的有效性和可行性.     

过程控制常用连续模型的直接辨识法及应用

针对工业过程中最常用的一阶加滞后、二阶加滞后、二阶加零点、二阶加零点及滞后、积分惯性加滞后等环节，给出了基于阶跃响应的连续模型参数直接辨识算法。由传递函数的拉普拉斯逆变换式和对象阶跃响应的采样数据构成模型参数回归表达式，用最小二乘法或辅助变量法直接辨识对象的连续时间传递函数模型参数。仿真与实际应用结果表明，该算法提高了模型辨识精度，减小了对过程的扰动，并且对输出测量噪声不敏感，鲁棒性强，容易编程实现，可提高实际PID控制器参数整定质量。     

基于非线性规划的指数自回归模型的辨识

讨论了指数自回归模型的辨识问题，证明了该模型最小二乘估计的目标函数的非凸性，并给出了使该函数为凸的条件，最后给出了辨识该模型的算法及该算法的收敛性，并以数值例子加以说明。     

基于灵敏度的大滞后过程对象自适应PI控制器

针对较难控制的大滞后过程对象,提出一种基于灵敏度鲁棒性能指标的自适应PI控制器,使控制回路在运行过程中始终保持在最佳运行状态,最终提高工业过程设备的运行效率.利用控制回路在正常运行过程中产生的过程对象输入和输出信号,通过信号分解和傅立叶分析运算在线辨识过程对象在重要频率点上的频率响应特性,然后通过同一阶加纯滞后模型在幅值和相位两方面的匹配获得一个可以很好地描述大滞后对象的传递函数模型.基于模型计算出满足灵敏度性能指标的PI控制器参数,实现了大滞后过程对象的自适应PI控制.所提出的PI控制器的自适应过程不需要过程对象的任何先验知识,也不需要中断控制回路的正常运行.仿真实验表明模型的在线辨识精确,而且自适应PI控制可以保证系统的鲁棒性能和预期的控制性能.     

滞后不确定系统的无辨识自适应智能控制方法

针对带有大纯滞后、不确定性的工业过崔，提出一种无需辨识的自适应智能控制方法。该方法不需要对过程建立数学模型，只要检测过程的实际输出和期望输出，通过模糊预测控制来自正单神经元自适应PSD控制律，即可以对滞后不确定、建模困难的工业过程实现自适应控制。仿真结果表明用该方法控制滞后不确定系统具有简单、实用、鲁棒性强的特点．     

基于高斯微粒群优化的动态神经网络延迟系统辨识

为提高神经网络对未知非线性大滞后动态系统的泛化能力,提出一种基于高斯微粒群优化的自适应动态前馈神经网络.在输入层与隐含层之间、隐含层与输出层之间分别加入动态延迟算子,可以高效地辨识出系统纯滞后时间,建立精确系统模型.此外,采用高斯函数和混沌映射方法平衡微粒群算法全局寻优能力,以克服提前收敛的缺陷,从而快速有效地自适应优化网络中的参数.仿真实验表明了该方法在非线性人滞后系统辨识中的有效性.     

Wiener型非线性系统的La-RBF组合模型预测控制

针对Wiener型非线性系统,本文提出了一种基于Laguerre函数模型与RBF神经网络模型的组合模型的预测控制策略,研究结果表明该组合模型兼具两者的优点,适用范围广,对系统变时延,变阶次及变非线性都具有良好的控制效构     

Wiener系统中线性函数辨识的渐近性分析

针对Wiener系统中的两类未知参数以相互结合的形式出现在非线性函数中,通过预测误差法辨识此两类未知参数,进而确定Wiener系统中线性部分的系统对象模型的渐近方差矩阵形式。在白噪声激励的作用下,推导出Wiener系统中线性部分的渐近方差表达式。此渐近方差表达式中不包含有模型阶数的存在,其利用某个由正交基构成的生成核函数来替换原模型阶数,使得在已知某些先验信息知识的前提下,该渐近方差式能更精确地接近于各自对应的真实采样值。最后用仿真算例验证本文方法的有效性和可行性。     

Wiener sliding-mode control for artificial pancreas: a new nonlinear approach to glucose regulation

Type 1 diabetic patients need insulin therapy to keep their blood glucose close to normal. In this paper an attempt is made to show how nonlinear control-oriented model may be used to improve the performance of closed-loop control of blood glucose in diabetic patients. The nonlinear Wiener model is used as a novel modeling approach to be applied to the glucose control problem. The identified Wiener model is used in the design of a robust nonlinear sliding mode control strategy. Two configurations of the nonlinear controller are tested and compared to a controller designed with a linear model. The controllers are designed in a Smith predictor structure to reduce the effect of system time delay. To improve the meal compensation features, the controllers are provided with a simple feedforward controller to inject an insulin bolus at meal time. Different simulation scenarios have been used to evaluate the proposed controllers. The obtained results show that the new approach outperforms the linear control scheme, and regulates the glucose level within safe limits in the presence of measurement and modeling errors, meal uncertainty and patient variations.     

Adaptive control of a Wiener type system: application of a pH Neutralization reactor

In this paper the problem of on-line identification and adaptive control of a Wiener type non-linear system is studied. First, a Wiener model is defined whose linear and non-linear parts are described using Laguerre and piecewise linear basis functions, respectively. Then, an adaptive identification algorithm for this model is presented. A local convergence analysis for the adaptive identification is performed. The model obtained is used to adapt the parameters of a controller designed for the specific structure of the model. The complete scheme is applied to a simulation of a pH neutralization reactor subject to several perturbations. The results show the improved behaviour of the proposed scheme compared with other approaches found in the literature.     

Subspace based approaches for Wiener system identification

We consider the problem of Wiener system identification in this note. A Wiener system consists of a linear time invariant block followed by a memoryless nonlinearity. By modeling the inverse of the memoryless nonlinearity as a linear combination of known nonlinear basis functions, we develop two subspace based approaches, namely an alternating projection algorithm and a minimum norm method, to solve for the Wiener system parameters. Based on computer simulations, the algorithms are shown to be robust in the presence of modeling error and noise.     

一种基于Wiener模型的非线性预测控制算法

针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号，将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wiener模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性.     

Recursive identification of errors-in-variables Wiener systems

This paper considers the recursive identification of errors-in-variables (EIV) Wiener systems composed of a linear dynamic system followed by a static nonlinearity. Both the system input and output are observed with additive noises being ARMA processes with unknown coefficients. By a stochastic approximation incorporated with the deconvolution kernel functions, the recursive algorithms are proposed for estimating the coefficients of the linear subsystem and for the values of the nonlinear function. All the estimates are proved to converge to the true values with probability one. A simulation example is given to verify the theoretical analysis.     

Input‐to‐state stability of switched nonlinear systems with delay in the active mode detection

A switched nonlinear system subject to disturbances is considered in this paper. The switching signal admits an average dwell time and a state feedback control depending on the system operating modes, detected with a maximum time delay, is applied to the system. In this framework, the input‐to‐state stability problem of the closed‐loop system is addressed. Based on some established existence conditions of mode‐dependent Lyapunov‐like functions, the values of the maximum time delay and the average dwell time that allow to achieve the input‐to‐state stability of the closed‐loop system are determined. In order to obtain more tractable results, the existence conditions of the mode‐dependent Lyapunov‐like functions are given in terms of sum‐of‐squares programming in the case of polynomial nonlinearities. In the linear case, they are expressed in terms of linear matrix inequalities and a procedure for the synthesis of the mode‐dependent controller is provided in this situation. The established theoretical results are illustrated through a control problem of a building ventilation system and a switched control problem of a vehicle suspension system.     

Generalised Hammerstein–Wiener system estimation and a benchmark application

This paper examines the use of a so-called “generalised Hammerstein–Wiener” model structure that is formed as the concatenation of an arbitrary number of Hammerstein systems. The latter are taken here to be memoryless non-linearities followed by linear time invariant dynamics. Hammerstein, Wiener, Hammerstein–Wiener and Wiener–Hammerstein models are all special cases of this structure. The parameter estimation of this model is investigated using a standard prediction error criterion coupled with a robust gradient based search algorithm. This approach is profiled using a Wiener–Hammerstein Benchmark example, which illustrates it to be effective and, via Monte-Carlo simulation, relatively robust against capture in local minima.