A virtual closed loop method for closed loop identification

Indirect methods for the identification of linear plant models on the basis of closed loop data are based on the use of (reconstructed) input signals that are uncorrelated with the noise. This generally requires exact (linear) controller knowledge. On the other hand, direct identification requires exact plant and noise modelling (system in the model set) in order to achieve accurate results, although the controller can be non-linear. In this paper, a generalized approach to closed loop identification is presented that includes both methods as special cases and which allows novel combined methods to be generated. Besides providing robustness with respect to inexact controller knowledge, the method does not rely on linearity of the controller nor on exact noise modelling. The generalization is obtained by balancing input-noise decorrelation against noise whitening in a user-chosen flexible fashion. To this end, a user-chosen virtual controller is used to parametrize the plant model, thereby generalizing the dual-Youla method to cases where knowledge of the controller is inexact. Asymptotic bias and variance results are presented for the method. Also, the benefits of the approach are demonstrated via simulation studies.     

脉冲响应模型的闭环子空间辨识方法

针对实际情况中难以采集控制系统开环数据的问题，对脉冲响应模型的闭环辨识问题进行了研究，提出了一种基于正交分解理论的脉冲响应模型闭环子空间辨识方法。通过使用正交分解得到联合输入输出信号的确定部分（Deterministic Components），把闭环问题转化成开环问题。利用Toeplitz矩阵下三角结构形式，对由脉冲响应模型系数组成的子空间矩阵分块分解，通过求取子空间矩阵元素的平均值来获取脉冲响应模型参数的估计。通过采用PID控制器的单输入单输出（SISO）数值仿真、多输入多输出（MIMO）数值仿真和Wood-Berry蒸馏过程仿真实验3个仿真实例，对比研究了所提方法与PBSID_OPT、CVA三种方法。仿真结果表明了所提出的方法具有良好的辨识性能。对于实际工业过程的建模问题，该研究所提的闭环子空间辨识方法具有实际的参考价值和一定的指导意义。     

Subspace identification of closed loop systems by the orthogonal decomposition method

In this paper, we consider a problem of identifying the deterministic part of a closed loop system by applying the stochastic realization technique of (Signal Process. 52 (2) (1996) 145) in the framework of the joint input-output approach. Using a preliminary orthogonal decomposition, the problem is reduced to that of identifying the plant and controller based on the deterministic component of the joint input-output process. We discuss the role of input signals in closed loop identification and the realization method based on a finite data, and then sketch a subspace method for identifying state space models of the plant and controller. Since the obtained models are of higher order, a model reduction procedure should be applied for deriving lower order models. Some numerical results are included to show the applicability of the present technique.     

A CLOSED‐LOOP SUBSPACE IDENTIFICATION METHOD FOR CONTINUOUS‐TIME SYSTEMS BASED ON δ‐OPERATOR MODEL

This paper is concerned with a subspace identification of a continuous‐time plant operating in closed‐loop in the framework of the joint input‐output approach. The main procedure consists of two steps. Firstly, the dual‐Youla parametrization of the plant is used for obtaining an equivalent open‐loop problem to the original closed‐loop identification problem. Then, a δ‐operator based IV‐MOESP type subspace identification algorithm is developed to estimate the state space model for the joint input‐output process, whereby a higher‐order state space model of the plant is obtained by an algebraic operation. Subsequently, a model reduction procedure is employed to derive a lower‐order plant model removing irrelevant modes from the higher order model. Simulation results by using numerical and chemical plant models demonstrate the feasibility of the proposed method.     

An approach to closed-loop subspace identification by orthogonal decomposition

A method of identifying closed-loop systems is developed by using the orthogonal decomposition (ORT) method. The idea is to project the input and output data onto the space of exogenous inputs by using the LQ decomposition to obtain their deterministic components. The ORT-based method is then applied to deterministic components like the direct approach in order to derive state-space models of the plant. We also show that the present method is a subspace version of the two-stage method for transfer function estimation from closed loop data. Some simulation results are included to show the applicability of the present method.     

基于模型的工业过程控制系统假数据注入攻击识别方法设计

随着国家两化融合的不断推进,网络安全问题也日益突出。在控制系统闭环辨识基础上,设计了一种基于模型的工业过程控制系统假数据注入攻击识别方法。该方法能够结合工业过程控制系统特点,通过在线闭环辨识过程模型,更加准确清晰地对系统中存在的攻击信号进行监测和识别。仿真结果证明,提出的方法可识别假数据注入攻击的具体类型,具有较高的准确性。     

预报辨识、映照模型及自校正极点配置鲁棒控制系统

本文以反馈通道存在延时的实际系统为背景,提出了一种简单的参数预报辨识算法,分析证明了该算法的区域收敛性。此外,文中还引入了映照模型的概念,并以此为基础,设计了一种算法简单的自校正极点配置鲁棒控制器,证明了映照模型的跟踪性质和整个闭环系统的稳定性。理论分析及仿真的结果表明,本文给出的方法是可行的。     

A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros

ABSTRACT

This paper presents a numerical investigation of three direct architectures and three indirect architectures for identifying a plant operating in closed loop. Motivated by adaptive control of systems with nonminimum-phase (NMP) zeros and taking advantage of the fact that zeros are not moved by feedback, the performance metric is the accuracy of the estimates of the NMP zeros of the plant. Assuming known plant order, single-input, single-output, infinite-impulse-response models are constructed in the presence of process and sensor noise. Least squares provides the baseline estimation technique, and prediction error methods are used to account for correlation between the model input and noise. The goal is to compare the accuracy of the NMP-zero estimates obtained from each method and for each architecture.     

Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer

It is commonly observed that over the lifetime of most model predictive controllers, the achieved performance degrades over time. This effect can often be attributed to the fact that the dynamics of the controlled plant change as the plant ages, due to wear and tear, refurbishment and design changes of the plant, to name a few factors. These changes mean that re-identification is necessary to restore the desired performance of the controller. An extension of existing predictive controllers, capable of producing signals suitable for closed loop re-identification, is presented in this article. The main contribution is an extensive experimental evaluation of the proposed controller for closed loop re-identification on an industrial depropanizer distillation column in simulations and in real experiments. The plant experiments are conducted on the depropanizer during normal plant operations. In the simulations, as well as in the experiments, the updated models from closed loop re-identification result in improvement of the performance. The algorithm used combines regular model predictive control with ideas from applications oriented input design and linear matrix inequality based convex relaxation techniques. Even though the experiments show promising result, some implementation problems arise and are discussed.     

一种多输入多输出系统模型辨识方法及其应用

目前的辨识方法一般需要在系统输入端加入激励信号，而且多输入多输出系统的在线辨识仍很困难。本文提出一种基于牛顿迭代法的多输入、多输出对象模型迭代辨识方法，模型参数更新的依据是使模型预测输出与全部采样时刻的对象实际输出之间的均方差递减，直到收敛。这种基于全局数据迭代的辨识方法可进行闭环辨识，无需外加激励信号，适用于多输入多输出对象的在线辨识。对一个两输入、两输出对象模型的仿真研究和某电厂300MW机组负荷被控对象的计算结果表明，辨识效果令人满意。     

Closed loop identification and control loop reconfiguration: an industrial case study

The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.     

闭环系统辨识的偏差最小二乘法

研究有色噪声扰动下反馈未知的闭环系统的无偏辨识问题,提出了一种偏差补偿最 小二乘法.应用这种方法,在噪声未建模的情况下,即可获得前向通道中对象模型和反馈通道 中控制器模型参数的渐近无偏估计.     

Improving convergence of Iterative Feedback Tuning

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.     

Borstar双峰聚合反应产品性质分析及模型建立

通过对Borstar双峰聚合反应过程产品性质实验分析,Borstar双峰聚乙烯技术超临界环管反应器生产聚合物熔融指数相对于分子量呈指数规律变化,气相反应器生产的聚合物熔融指数随分子量增加而呈一定规律的减少,聚合物分子量继续增加聚合物熔融指数减少趋势缓慢;在实验分析的基础上,基于聚合反应机理应用MATLAB软件建立工业装置聚合过程聚乙烯熔融指数及密度等产品性质计算模型和确定模型参数,通过模型拟合值和实际值的比较,模型能够准确预测熔融指数和密度的变化趋势,有关聚乙烯在环管中的熔融指数、在流化床内的熔融指数和密度的质量模型相对平均误差分别为2.4%,0.5%和0.3%。通过产品质量预测模型的推导和分析,环管部分熔融指数主要受温度、[H2]/[C2H4]影响;而气相部分熔融指数既受环管聚合物来料性质影响,又受气相反应器温度和[H2]/[C2H4]和[C4H8]/[C2H4]的影响     

单锤气动式振动台系统建模方法

目前国际上比较先进的可靠性实验设备是一种气动式振动台,该振动台在国外可靠性强化实验中已得到广泛应用。该文在对该振动台进行相关分析的基础上,首先设计了单锤气动式振动台系统,然后以单锤气动式振动台为对象,针对其振动控制技术展开研究,提出了建立系统数学模型的实验建模方案,采用广义最小二乘法,得到了系统的参数模型。最后通过检验,证明了建模方法的可行性与模型的正确性,为实现单锤气动式振动台的闭环控制奠定了基础。     

Frequency Response Estimation from Impulse or Step‐like Response by Virtual Experiments

This paper considers the problem of estimating the frequency response of an unknown plant from impulse or step response measurements, possibly with non‐ideal step signals. The proposed method is direct (no model identification of the plant is needed) and it can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. The frequency response of the plant, at a desired frequency, is estimated by a virtual experiment generating a filtered version of the data set. Numerical simulations show the effectiveness of the method.     

A practical global multi-stage method for fully automated closed-loop identification of industrial processes

A method for automated closed-loop identification is presented. The method is focused on practical deployment and has evolved based on academic developments, personal experience in industrial applications and feedback from seasoned practitioners who have a wealth of experience in implementing multivariable predictive control (MPC) and optimization projects. In essence, the basic idea here is that no single method will be adequate for the range of conditions encountered in the target industries. Hence this approach utilizes a family of prediction error model derived structures and consequently a family of model orders for each structure. These families are then searched for the most effective model for the given application. Key in this search is the definition of metrics not exclusively dependent on asymptotic theory. Extensive plant data are used to illustrate important features of the approach and overall performance.     

几种PID控制器设计方法的比较

现代过程工业中PID控制器的使用仍然占据统治地位.文章针对被控对象模型未知的情况,给系统加入白噪声,测得输出数据,通过输入输出数据估计模型阶次与延迟时间,从而得到被控对象模型.仿真结果表明,辨识模型能较好地反映原过程的动态特性.在此基础上,利用内模法、直接综合法及频域法这三种方法进行了PID控制器的设计,同时用随机性能指标和确定性指标对系统性能进行了评价.根据评价结果,对这三种PID控制器设计方法的适用条件给出了一些建议.     

Automatic controller tuning via unfalsified control

In this paper, we present a new algorithm for the automatic adaptation of linear single-loop controllers. The algorithm is based on the idea of unfalsified control, where a set of controllers is defined and using measured data, the controller from the set is chosen online which provides the best performance. This scheme is extended here by an adaptation of the set of controllers after a change of the operating point of the plant. The original concept of unfalsified control relies on the computation of the performance of controllers that are not in the loop using so-called fictitious signals. However, the proposed cost function leads to erroneous results, in particular, it is not possible to detect closed-loop instability that may result from inserting a controller. Therefore a different cost function and a new method for online evaluation of controllers that are not in the loop without an explicit plant model are proposed. With this new cost function, the set of controllers can be adapted as well which is performed using an evolutionary algorithm. The method is demonstrated for a well-known example, the nonlinear non-minimum phase CSTR model with van der Vusse reaction scheme and PID controllers.