闭环系统的输出过采样辨识方法及其估计精度分析

传统闭环系统辨识方法的可辨识性受到参考设定信号和控制器结构的限制．提出了一种通过对输出过采样实现线性离散时间闭环系统辨识的方法，输出过采样提供了更多的系统结构信息，在传统辨识方法的可辨识条件不满足的情况下，仍能正确辨识系统参数，针对有色噪声干扰，分析其在不同过采样率下的估计精度，得出最优估计的过采样率计算方法．辨识方法实现简单、运算量小、估计精度高．仿真试验验证了其有效性．     

基于闭环子空间辨识的直升机横向模型修正

针对闭环条件下数据相关性所导致的辨识偏差问题,提出一种基于闭环子空间辨识的直升机模型修正方法.通过闭环子空间辨识理论以及投影算法,利用操纵指令的激励信息中蕴含的动态信息,实现闭环条件下的模型无偏估计.并通过正则化方法,实现模型先验知识与数据后验信息的融合,进一步提高直升机模型修正结果的准确性与实用性.提出了基于闭环飞行数据的直升机模型修正两步法,在闭环条件下的应用结果验证了方法的有效性.     

连续时间模型的闭环子空间辨识

本文对直接使用采样数据进行连续系统的闭环子空间辨识问题进行了研究．将线性滤波方法与基于主 元分析的子空间辨识相结合,利用参考输入或者外部激励信号的高阶滤波变换的正交投影变量作为辅助变量,提出 了一种新的连续时间系统闭环子空间辨识算法．数值仿真表明了与其他算法相比,本文提出的算法具有很好的辨识 效果．     

输出信号量化的双率采样系统参数辨识方法

针对双率采样和信号量化(signal quantization)[BFQB]的控制系统,采用随机重复性试验测量信息,提出基于辅助模型的双率采样量化控制系统辨识方法.分析了在随机重复试验和放松估计误差方差条件下,双率采样量化系统的模型特征并给出了分两步辨识的策略,推导了进行参数辨识所满足的持续激励条件,并给出了基于辅助模型的双率采样量化控制系统量化辨识递推算法;接着分析了所给出量化辨识递推算法的收敛性,得到了双率采样量化系统参数估计误差上界的计算式,最后数字仿真验证了该算法及结论的有效性.     

基于子空间辨识的模型算法控制器设计

提出了一种基于子空间辨识的模型算法控制器设计方法.在输入信号满足持续激励的条件下,先利用子空间辨识方法提取出系统有限单位脉冲响应序列,再利用单位脉冲序列构成的矩阵设计预测控制器.该方法将独立的对象辨识和控制器设计整合在一起,简化了控制器的设计过程,是一种完全的数据驱动方式.工业过程对象的仿真实验详细给出了该算法的设计过...     

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。     

基于辅助变量的闭环系统子空间辨识

提出一种基于辅助变量的子空间辨识方法,适用于控制器信息未知以及参考输入已知的闭环系统参数辨识.通过将输入-输出数据块正交投影到辅助变量的行空间,直接得到扩展观测矩阵垂空间的估计.由此可从闭环系统中提取出对象模型信息,同时由SVD分解得到扩展观测矩阵与下三角Toeplitz矩阵的估计.给出了系统参数矩阵、噪声矩阵的计算方法.将所提出的子空间辨识方法应用于闭环动态的系统参数估计,其结果表明了该方法的有效性.     

基于输入扩张的闭环系统子空间辨识及其强一致性分析

针对闭环条件下的子空间辨识问题, 结合线性代数和几何学的基本概念, 将输入输出误差序列包含至输入子空间中, 基于输入扩张的状态空间构造方法, 提出一种新的闭环辨识算法；解决开环算法应用于闭环系统辨识时产生有偏估计, 甚至不能正确辨识的问题；实现闭环条件下对系统状态空间矩阵的强一致估计, 并理论证明该辨识算法的强一致性；最后通过仿真实例验证本算法的有效性.     

闭环子空间辨识方法的分析与比较

在实际应用中,辨识方法的辨识精度和辨识效率一直是人们关注的指标,也是人们选择辨识方法的主要依据。针对多种闭环子空间辨识方法的辨识精度和辨识效率问题的研究,首先归纳和总结了基于正交分解和基于正交投影闭环子空间辨识方法的理论和实现;然后扩展提出了基于正交分解的闭环子空间辨识方法 ORT_POMOESP、ORT_N4SID和基于正交投影的闭环子空间辨识方法 CSOPIM_W2;最后考虑系统输入输出测量噪声,针对过程噪声为白噪声和有色噪声两种情况下,通过仿真算例以数值分析的形式,对比研究了多种闭环子空间辨识方法的辨识精度和辨识效率。该研究不仅对子空间辨识方法应用于实际工业过程的建模具有实际的参考价值,而且对实际工程应用中闭环子空间辨识算法的选用具有一定的指导意义。     

基于辅助变量的闭环系统子空间辨识

提出一种基于辅助变量的子空间辨识方法,适用于控制器信息未知以及参考输入已知的闭环系统参数辨识.通过将输入-输出数据块正交投影到辅助变量的行空间,直接得到扩展观测矩阵垂空间的估计.由此可从闭环系统中提取出对象模型信息,同时由SVD分解得到扩展观测矩阵与下三角Toeplitz矩阵的估计.给出了系统参数矩阵,噪声矩阵的计算方法.将所提出的子空间辨识方法应用于闭环动态的系统参数估计,其结果表明了该方法的有效性.

     

Closed-loop identification via output fast sampling

This paper addresses a challenge: Is a closed-loop system without external excitation identifiable? The so-called fast-sampling direct approach provides a positive answer. It removes a traditional restrictive identifiability condition for linear output feedback closed-loop systems, i.e., an external persistently exciting test signal is not required. Identifiability is analyzed using the lifting technique, the bifrequency map and bispectrum concepts. The proposed approach is further investigated and evaluated by simulation.     

Minimal required excitation for closed-loop identification: Some implications for data-driven,system identification

The development and effective use of all available data is extremely important. Previous work has shown that it is possible to identify process models using closed-loop data even if the reference signal was not being excited. However, such results require that the system have a sufficiently large time delay or alternatively a fast sampling time. Therefore, this paper seeks to examine and provide general results for identifiability of a process using closed-loop data with or without changes in the reference signal. Similarly to the previous case, it is shown that the complexity of the required reference signal depends strongly on the sampling time and time delay. However, since many fast processes without time delay can be modelled as first-order systems, they can indeed be identified when the excitation in the reference signal is a simple step function or sequence of such functions. Using numerical simulations as well as the Tennessee Eastman process, the effect on the continuous time parameters is investigated for different sampling times and excitations signals. It is shown that as expected an external reference signal can identify previously difficult-to-identifiable cases.     

基于正交分解的递推子空间闭环辨识方法

为实现闭环系统在线辨识,提出递推正交分解闭环子空间辨识方法(RORT)。首先,根据闭环系统状态空间模型和数据间投影关系,构建确定-随机模型,并利用GIVENS变换实现投影向量的递推QR分解;然后,引入带遗忘因子的辨识算法,构建广义能观测矩阵的递推更新形式,以减少子空间辨识算法中QR分解和SVD分解的计算量;最后,针对某型号陀螺仪闭环系统进行实验。实验结果表明, RORT法的辨识拟合度高于91%,能够对陀螺仪闭环系统模型参数进行在线监测。     

数据驱动闭环子空间预测控制方法研究与应用

提出一种完全数据驱动的闭环子空间辨识及预测控制器设计方法. 该方法完全由闭环系统的输入输出数据辨识子空间矩阵, 通过子空间矩阵的拆分, 排除了与扰动相关的模型输入, 进而获取子空间矩阵参数的无偏估计; 将辨识得到的闭环系统子空间矩阵描述直接作为预测模型, 设计预测控制器; 将其应用于某钢铁集团焦炉炭化室压力控制系统, 取得了良好的控制效果.

     

多干扰情况下的电力系统闭环辨识研究

电力系统中多处负荷投切与变化等随机性质小扰动,导致系统响应始终存在类似噪声的小幅波动.本文提出利用广域测量类噪声信号辨识被控电力系统模型,及时准确地反映系统当前运行特性,进而解决因仿真模型及参数误差造成的系统分析及控制器设计可信度难以评估等难题.重点对多干扰情况下的系统模型闭环辨识问题进行理论性研究,包括闭环可辨识性、辨识一致性及收敛性等方面,分析得出类噪声环境下要实现系统闭环辨识需具备的条件,并对理论研究结果进行了仿真验证.     

Model Predictive Control oriented experiment design for system identification: A graph theoretical approach

We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.     

Improved closed-loop subspace identification with prior information

It has been proven that combining open-loop subspace identification with prior information can promote the accuracy of obtaining state-space models. In this study, prior information is exploited to improve the accuracy of closed-loop subspace identification. The proposed approach initially removes the correlation between future input and past innovation, a significant obstacle in closed-loop subspace identification method. Then, each row of the extended subspace matrix equation is considered an optimal multi-step ahead predictor and prior information is expressed in the form of equality constraints. The constrained least squares method is used to obtain improved results, so that the accuracy of the closed-loop subspace can be enhanced. Simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.     

Closed-loop identification of multivariable systems: With or without excitation of all references?

The accuracy of plant parameters estimated in closed-loop operation is investigated for a class of multivariable systems and for the situation where only some of the reference inputs are excited. This issue is important for at least two reasons: (i) there are control applications where it is preferable not to excite all references in order to avoid performance degradation, and (ii) it is not clear whether the results in the context of open-loop identification, where the existence of common parameters between the various transfer functions is a condition for improved accuracy with additional excitation, also hold in the closed-loop case. The paper examines the effect of the non-excitation of some reference inputs on the variance of the estimated parameters. The proposed expressions are valid for all conventional model structures used in prediction error identification. Although exciting all reference inputs is not necessary for identifiability, this work shows that, regardless of the parametrization, the excitation of all references never worsens and, in most cases, improves the accuracy of the parameter estimates. The analytical results developed in this work are illustrated by two simulation examples.     

Closed-loop identification condition for ARMAX models using routine operating data

In industrial process control engineering, using routine operating data obtained from closed-loop operation of a process for model identification would be extremely valuable in applications such as control performance monitoring, root cause diagnosis, and controller retuning. However, the conditions for closed-loop identifiability using routine operating data are still largely unknown or untried. In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived. The theoretical criteria that are developed for the closed-loop identification of an ARMAX process are compared with Monte Carlo simulations and previous theoretical results. It is shown that the newly-proposed theoretical results are in agreement with the simulation results.