基于ARMAX模型的子空间辨识算法

针对于子空间辨识算法辨识闭环系统时,由于输入信号与不可测噪声是相关的,往往会得到有偏估计的问题.提出一种采用自回归滑动平均模型(ARMAX)的闭环子空间辨识方法,通过扩展最小二乘方法(ELs)估计ARMAX模型中的马尔科夫(Markov)参数,使用预测的子空间辨识方法(PBSID)获取系统参数矩阵,避免了采用高阶自回归模型(ARX)所导致的过大的估计方差等问题.算法实例验证结果表明,改进方法能够获得较好的闭环系统一致性估计,辨识精度较高,有非常良好的应用前景.     

RBF-ARX模型在倒立摆系统建模中的应用

针对直线一级倒立摆控制系统的非线性特性,采用RBF-ARX模型对倒立摆系统的全局非线性动态特性进行建模.讨论了RBF-ARX模型结构的选取,模型参数辨识,RBF参数优化等问题.并且分别比较了该倒立摆系统的RBF-ARX模型与全局线性ARX模型,以及将RBF-ARX在某一工作点局部线性化后的模型与局部线性ARX模型的预测输出和模型误差,验证了RBF-ARX模型在倒立摆系统建模和辨识中的有效性.     

变风量空调末端双闭环系统的模型辨识和仿真

研究变风量空调末端部分控制系统的节能优化问题时,对于末端系统的优化控制应以系统中被控的风阀和房间模型为基础.采用西安建筑科技大学变风量空调实验平台,对末端风阀被控对象采用闭环间接法送行辨识.利用LabView软件对外环温度控制器进行在线仿真设计,创造闭环辨识性条件,建立被控室温房间对象模型.最后,在Sumlink工具箱中用辨识模型进行末端双闭环控制系统的仿真.仿真结果表明,辨识出的模型精确度较高.用于末端节能优化控制研究中可提升控制性能,并为变风量空调节能优化控制提供了参考依据.     

RBF-ARX模型在串级系统中的应用

针对液位串级系统的非线性特征,采用RBF-ARX模型对液位串级系统的非线性动态特性进行建模,讨论了RBF—ARX模型结构的选取,模型参数辨识,RBF参数优化等问题。采用了不同的序列作为状态向量,分别建立了液位串级系统的训练数据和测试数据的RBF—ARX模型,分析了各模型的可靠性。模型的预测输出和仿真结果表明,RBF—ARX模型在非线性系统建模和辨识中是有效的。     

一种新的ARX模型在磁悬浮系统建模中的应用

采用一种新的ARX模型（RBF-ARX模型）对磁悬浮系统进行离线建模,讨论了RBF-ARX模型的原理、结构的选取、模型参数辨识和RBF参数优化等问题。文章分别采用不同的序列作为状态变量,分别建立RBF-ARX模型,并分析了各模型的性能及可靠性。模型的预测输出和仿真结果,证实了RBF-ARX模型在非线性系统建模和辨识中的有效性。通过与ARX模型的比较,证明了RBF-ARX模型在非线性系统建模中效果更好。     

变焦遗传算法优化变增益模糊ARX模型研究

针对传统ARX模型对非线性系统模型辨识精度比较低的问题,进行了模糊模型和ARX模型相关优化算法的调查,介绍了遗传算法优化模糊模型的现状,提出采用改进变焦遗传算法优化变增益模糊ARX模型参数的方法以提高模型的辨识精度。改进的变焦遗传算法能在不同前代种群情况下更新不同数量基因,以提高搜索速度;用不同的概率选择交叉位置,可避免早熟现象,并能在较短时间内达到最优或次优解。变增益模糊ARX模型可根据非线性系统的变化改变其增益,使模型的辨识精度提高。利用改进变焦遗传算法的优点,对变增益模糊ARX模型的参数进行优化,并通过两入两出多时滞离散非线性系统进行试验仿真。试验结果证明了改进变焦遗传算法优化变增益模糊ARX模型参数的方法能提高模型辨识精度,表明了提出的优化方法的有效性,为变焦遗传算法与模糊模型的结合提供了一种途径。     

复杂多变量过程模型的闭环频域辨识

针对模型预测控制中模型辨识存在的问题,提出一种多变量过程闭环辨识方法.首先通过对多变量闭环系统正常运行产生的输入输出信号进行信号分解和频谱分析,得出多变量过程对象在重要频率段上的频率响应特性矩阵;然后采用最小二乘法,在幅值和相位两方面拟合一个二阶加纯滞后模型结构;最终获得一个多变量传递函数模型矩阵.仿真实验表明,该闭环辨识方法适用于广泛的多变量过程对象,具有很好的鲁棒性和精确性.     

变风量空调末端(VAV BOX)系统的辨识

介绍了变风量空调末端VAV BOX的基本结构和控制原理,运用最小二乘法,对变风量空调末端控制系统的两个未知模型(风阀模型和房间模型)分别采用了开环辨识和闭环辨识两种方法进行系统辨识,并用Matlab进行了模型的仿真验证。建立的模型可以根据不同房间的具体情况实现单独控制,以达到节能舒适的目的。     

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.

     

过程对象模型在线闭环频域辨识

提出一种过程对象在线闭环辨识方法.通过对控制回路中过程时象的输入输出信号进行信号分解和频谱分析,得出过程对象在重要频率段上的频率响应特性,然后采用最小二乘法在幅值和相位两方面去拟合一个二阶加纯滞传递函数,最终获得过程对象模型.辨识所需的数据是通过设定值改变后系统过渡变化的输入输出信号获得的,不需要中断现有正常运行而切换到某种试验状态,控制系统仍然工作在正常运行状态中.仿真实验验证了辨识方法的有效性和和精确性.     

MIMO closed-loop identification of an MSW incinerator

In this paper, the application of a specific system identification procedure to a municipal solid waste (MSW) incinerator is discussed. This procedure is a combination of, on the one hand, a particular closed-loop identification method called the two-stage method and, on the other hand, the approach of high-order multiple input multiple output (MIMO) ARX model estimation followed by model reduction. MIMO ARX model estimation is performed by means of a, so-called, multiple data set identification method, i.e. a method by means of which it is possible to estimate a model on the basis of several data sets instead of just one data set. Model reduction is applied to each transfer function of the resulting MIMO ARX model separately. It is shown that with the proposed identification procedure a model of the MSW incinerator is obtained which, according to system identification validation measures, is good. Using the estimated model, the influence of the disturbances on the identification and control of an MSW incinerator is discussed. Furthermore, the validation of a first-principles model of the MSW incineration process by means of the resulting low-order SISO models is discussed. The results show that the proposed way of validating a first-principles model is a powerful tool for determining its quality.     

A methodology for control-relevant nonlinear system identification using restricted complexity models

A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.     

Model identification of a micro air vehicle in loitering flight based on attitude performance evaluation

This paper presents a model identification of a micro air vehicle in loitering flight, based on the input-output data collected from flight experiments on a homemade 1-m-sized aircraft. A miniature flight-control system, which consists of the onboard and the ground sections, is equipped with a multichannel data logger associated with the data acquisition software. Modeling and performance analysis are carried out, based on the flight-test data using a system identification technique. Two fourth-order autoregressive with exogenous input (ARX) models are identified to present the attitude characteristics of the longitudinal (pitch) and the lateral (roll) control channels, respectively. The validity of the identified model is verified by both time-domain model prediction and frequency-domain spectral analysis. Based on the proposed ARX models, two compensators are further designed using a frequency-domain method, and then added to the closed-loop control systems to improve the transient performance of the pitch- and roll-control channels. Simulations and experiments demonstrate that the flight performance obtained by the proposed ARX model-based compensation control can be improved.     

Practical application of model identification based on ARX models with transfer functions

A novel model identification methodology for ARX models based on transfer functions has been proposed. The identification approach converts transfer functions to ARX models with no approximation, except zero-order hold. Model parameters of the transfer functions are estimated directly. Model identification for process controls, especially MPCs, is of great importance for achieving the highest performance from them. However, step testing for model identification is a time-consuming task. Model identification techniques are necessary to save time for step tests. Therefore, a closed-loop identification method of multivariable systems is useful and helpful for time-saving. Herein, the proposed method, with control by model predictive controllers, is suited for a closed-loop identification technique and is applied in an industrial chemical plant.     

Design and application of generalized predictive control strategy with closed-loop identification for burn-through point in sintering process

This paper presents a generalized predictive control (GPC) strategy with closed-loop model identification for burn-through point (BTP) control in the sintering process. First, the dynamic Auto-Regressive eXogenous (ARX) model structure is defined to describe sintering process. Considering the economy and security, a closed-loop identification method is adopted to update the parameters of the model. Then, BTP predictive control model is established based on GPC algorithm to predict BTP accurately and to calculate the strand velocity. Finally, a BTP control system is established and implemented in an iron and steel plant. The running results show that the system effectively guarantees the stability of sintering process and suppresses the fluctuation of BTP.     

Closed-loop identification of systems with uncertain time delays using ARX-OBF structure

Closed-loop identification of systems with known time delays can be effectively carried out with simple model structures like Autoregressive with Exogenous Input (ARX) and Autoregressive Moving Average with Exogenous Input (ARMAX). However, when the system contains large uncertain time delay, such structures may lead to inaccurate models with significant bias if the time delay estimate used in the identification is less accurate. On the other hand, conventional orthonormal basis filter (OBF) model structures are very effective in capturing the dynamics of systems with uncertain time delays. However, they are not effective for closed-loop identification. In this paper, an ARX-OBF model structure which is obtained by modifying the ARX structure is shown to be effective in handling closed-loop identification of systems with uncertain time delays. In addition, the paper shows that this advantage of ARX-OBF models over simple ARX model is considerable in multi-step ahead predictions.     

Estimation of modelling errors of a closed-loop system and application to identification

A method for estimating the modelling errors of a process using closed-loop data is proposed. The theoretical analysis and implementation of the method are illustrated. A new algorithm of system identification which identifies the plant as an impulse response sequence or a transfer function model using the output of a feedback control system is derived. An illustrative example which shows the use of the proposed method for the estimation of modelling errors and its application to closed-loop identification is also included.     

Design of Diesel Engine Mathematical Model Oriented to Speed Control

We provide the synthesis procedure for a mathematical model of the 20ChN26.5/31 diesel engine treated as a part of 20EDG500 diesel-electric set (power 6.3 MW). The model is designed for synthesis of engine speed control and simulation of closed-loop control systems in generator sets. The model structure is based on the fundamental laws of physics, while its parameters and static functions are obtained using least squares approach and the data taken during experimental testing of the set. The results of model verification are presented, and the model outputs are compared with the experimental data. Simulation of the control system closed by proportional-integral-differential law is presented as an example of the model application.