线性时变反馈系统的状态空间模型和模态参数递推辨识

针对闭环系统中时变状态空间模型和模态参数的辨识问题, 提出一种递推辨识格式, 将这种格式与递推子空间方法结合, 得到一种辨识方法. 该方法通过重建输入输出数据之间的关系, 递推辨识得到闭环系统的时变状态空间模型和模态参数. 算例研究了系统在模态参数突变和周期变化两种情况下的辨识问题, 仿真结果表明, 所提出算法能有效辨识线性时变反馈系统的状态空间模型和模态参数.

     

基于修正闭环子空间辨识–分段线性结构的环管式丙烯聚合反应过程非线性模型预测控制（英文）

针对环管式聚丙烯生产过程装置多变量、耦合和非线性等特性容易导致过程控制不稳定及质量指标波动问题,本文提出了一种基于修正闭环子空间辨识–分段线性(MSSARX--PWL)维纳(Wiener)模型结构的非线性模型预测控制算法.利用修正的闭环子空间辨识方法(MSSARX)辨识对象在闭环工况下的线性状态空间模型,并将该线性模型与多变量分段线性化(PWL)方法辨识得到的非线性稳态模型结合,建立双环管丙烯聚合反应动态过程的非线性预测模型,而后进一步将非线性模型转化为线性模型,在线性预测控制算法框架下用二次线性规划方法(LQP)优化控制器,无须用非线性规划方法(NLP)求解.从双环管丙烯聚合反应过程仿真例子表明,该算法不仅能保证模型和控制精度,而且能提高计算效率.     

第六届IFAC系统辨识学术讨论会评述(二)

“随机系统的建模” (邓自立) H.El.Sherief的论文综述了多变量系统的建模和辨识及其应用的新近结果。 多变量系统的建模和辨识近几年来引起很大的兴趣并广泛应用于过程控制、电力系统、生物系统、太阳能和石油勘探等领域。 对于线性离散多变量系统有四种常用模型:(i)传递函数矩阵模型;(ii)脉冲响应模型;(iii)输入输出差分方程模型;(iv)状态空间模型,这些模型     

质子交换膜燃料电池的分数阶非线性状态空间模型研究

针对质子交换膜燃料电池(PEMFC)发电过程中的分数阶和非线性特性,本文提出了一种分数阶子空间辨识方法建立了PEMFC非线性状态空间模型.首先,为了降低建模复杂度,采用典型相关分析法和相关分析法确定了模型输入变量;其次,将分数阶微分理论与Hammerstein模型子空间辨识方法相结合,采用Poisson矩函数对输入输出数据进行预处理,构造了子空间辨识方法的输入输出矩阵,并引入分数阶短时记忆法减少辨识算法计算量;最后,选取多项式作为Hammerstein模型前端静态非线性环节,采用模糊遗传算法优化系统分数阶阶次和系数矩阵.仿真结果验证了算法的有效性,改进的辨识算法可以明显减小计算时间,所得PEMFC辨识模型能够准确地描述PEMFC的动态过程.     

基于PEM的三自由度直升机模型辨识

针对三自由度(3-DOF)直升机平台的特点,提出了一种基于预测误差法(PEM)的模型频域辨识方法,建立了机理模型,运用扫频技术得到巡航飞行状态直升机3个通道的输入-输出数据;分析了偏相干函数和复合窗函数,通过PEM进行了模型的频域辨识,得到了状态空间方程的待辨识参数和直升机的参数化模型.通过时域飞行和模型预测响应的对比,验证了该模型的准确性和该辨识方法的有效性.     

基于子空间方法的精馏塔系统辨识建模研究

子空间辨识方法作为一种有效的针对多输入-多输出系统(MIMO)的辨识建模方法近年来受到广泛的重视.目前主要采用的子空间辨识算法只能适用于白噪声环境,而实际的工业现场数据很多是受到较大有色噪声干扰的.针对问题采用了一种新的子空间辨识算法,利用马尔可夫参数用于处理随机性部分,同时引入辅助变量用以去除噪声的干扰,能够适用于存在较大有色噪声干扰情况下的辨识建模,并可得到对象的无偏模型,建模的精度优于通常所采用的子空间辨识算法.通过对精馏塔仿真模型的辨识结果证明了该方法的可行性和有效性,以及在实际工业过程对象建模中良好的应用前景.     

一种完全数据驱动的子空间辨识与鲁棒预测控制器设计

针对线性时变多变量系统,在可能存在输入输出数据噪声的情况下,不需已知系统的先验结构信息,提出一种完全数据驱动的子空间辨识及控制器设计方法.在子空间在线辨识基础上,利用不确定性模型更好地建模被控系统,结合鲁棒控制策略进行预测控制器的设计;将系统建模与鲁棒控制器的设计包含在一个控制系统设计框架内,对模型不确定性具有更好的鲁棒性;最后给出仿真实例验证算法的有效性.     

基于整体辨识策略的非线性自适应控制方法

针对一类离散时间下的未知动态非线性系统,为解决传统自适应控制方法在交替辨识非线性系统时由于辨识精度低而导致的控制性能差的问题,本文提出了一种基于整体辨识策略的未建模动态补偿的自适应控制方法.利用随机向量函数链接(RVFL)网络的直链与增强结构特性挖掘其与低阶线性模型和高阶未建模动态项的等价对应关系,并融入权值偏差惩罚项,设计了网络模型参数在线更新算法辨识非线性系统参数.根据在线辨识的线性模型参数和未建模动态估计量,采用一步超前最优控制策略设计线性控制器和未建模动态补偿器.数值仿真表明,所提方法优于交替辨识下的非线性自适应控制方法,并通过工业应用的仿真研究验证所提方法在工业上的可用性.最后,对本文控制方法在实际应用中的潜在问题及理论受限条件的放松进行分析和展望.     

基于趋势点状态模型的时间序列预测算法

针对传统的时间序列线性预测算法对时间序列的线性程度要求高,而非线性方法一般建模复杂且计算量大,提出了一种基于趋势点状态模型的时间序列预测算法.该算法无须考虑时间序列是否具有显著线性特征,通过序列间耦合度挖掘时间序列上的相似子序列,找出相对应的相似序列趋势点,建立趋势点状态模型并求出预测值.算法建模简单,复杂度较低.通过模拟实验,结果表明该算法性能良好,尤其对具有周期性的时间序列预测精度很高.     

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.     

Dual-mode predictive control algorithm for constrained Hammerstein systems

In the process industry, there exist many systems which can be approximated by a Hammerstein model. Moreover, these systems are usually subjected to input magnitude constraints. In this paper, a multi-channel identification algorithm (MCIA) is proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with a singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of coloured output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, this MCIA is converted into a two stage single-channel identification algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode non-linear model predictive control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the non-linear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the traditional algorithms, the present method has a maximal stable region, a better steady-state performance and a lower computational complexity. Finally, simulation results on a heat exchanger are presented to show the efficiency of both the identification and the control algorithms.     

Subspace-based methods for the identification of linear time-invariant systems

Subspace-based methods for system identification have attracted much attention during the past few years. This interest is due to the ability of providing accurate state-space models for multivariable linear systems directly from input-output data. The methods have their origin in classical state-space realization theory as developed in the 1960s. The main computational tools are the QR and the singular-value decompositions. Here, an overview of existing subspace-based techniques for system identification is given. The methods are grouped into the classes of realization-based and direct techniques. Similarities between different algorithms are pointed out, and their applicability is commented upon. We also discuss some recent ideas for improving and extending the methods. A simulation example is included for comparing different algorithms. The subspace-based approach is found to perform competitive with respect to prediction-error methods, provided the system is properly excited.     

Data-driven simulation and control

Classical linear time-invariant system simulation methods are based on a transfer function, impulse response, or input/state/output representation. We present a method for computing the response of a system to a given input and initial conditions directly from a trajectory of the system, without explicitly identifying the system from the data. Similar to the classical approach for simulation, the classical approach for control is model-based: first a model representation is derived from given data of the plant and then a control law is synthesised using the model and the control specifications. We present an approach for computing a linear quadratic tracking control signal that circumvents the identification step. The results are derived assuming exact data and the simulated response or control input is constructed off-line.     

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.     

Multivariable identification of an activated sludge process with subspace-based algorithms

This paper is aimed at identifying a linear time-invariant dynamical model (LTI model with lumped parameters) of an activated sludge process. Such a system is characterized by stiff dynamics, nonlinearities, time-variant parameters, recycles, multivariability with many cross-couplings and wide variations in the inflow and the composition of the incoming wastewater. In this simulation study, a discrete-time identification approach based on subspace methods is applied in order to estimate a nominal MIMO state-space model around a given operating point, by probing the system in open-loop with multi-level random signals. Six subspace algorithms are used and their performances are compared based on adequate quality criteria, taking into account identification/validation data. As a result, the selected model is a very low-order one and it describes the complex dynamics of the process well. Important issues concerning the generation of the data set and the estimation of the model order are discussed.     

基于SMI辨识的航空发动机模型建立

文中采用子空间模型辨识结合预报误差的方法在某一稳态点对某型航空涡轮发动机的动态模型进行了辨识,建立了该型航空涡轮发动机在该稳态点的'小偏离'动态状态空间模型,以满足在航空发动机性能分析以及故障诊断等领域对动态模型的需要.仿真结果表明,所采用的辨识方法很好地融合了子空间方法的简单性和预报误差法的最优性,用于航空发动机模型辨识是可行的.采用该辨识方法所得的发动机模型具有较高的精度,可以用于航空发动机性能分析,发动机控制以及故障诊断等领域.     

Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant

In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.     

Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation

The control of blast furnace ironmaking process requires model of process dynamics accurate enough to facilitate the control strategies. However, data sets collected from blast furnace contain considerable number of missing values and outliers. These values can significantly affect subsequent statistical analysis and thus the identification of the whole process, so it becomes much important to deal with these values. This paper considers a data processing procedure including missing value imputation and outlier detection, and examines the impact of processing to the identification of blast furnace ironmaking process. Missing values are imputed based on the decision tree algorithm and outliers are identified and discarded through a set of multivariate outlier detection methods. The data sets before and after processing are then used for identification. Two classic identification methods, N4SID (numerical algorithms for state space subspace system identification) and PEM (prediction error method) are considered and a comparative study is presented.     

Adaptive parameter estimation for a general dynamical system with unknown states

This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.     

Subspace identification of Hammerstein systems using least squares support vector machines

This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static nonlinearities follow from a low-rank approximation of a matrix obtained from this regression problem.