基于即时交货的离散时间模型及其在炼油过程调度优化中的应用

针对炼油过程调度优化的离散时间与连续时间建模方法在应用中的局限性,提出一种基于即时交货的离散时间建模方法.该方法总体上采用离散时间表示,通过对成品油交货环节相关部分更加细致的描述,使得交货活动能够在时间片段内部发生,既可保证对实际问题的准确刻画,又能实现对模型规模的有效控制.以华北地区某炼油厂的改质及调合过程为例,分别建立离散时间模型、连续时间模型以及所提出的基于即时交货的离散时间模型,并结合对3个案例的求解,从不同的角度对3种模型进行对比研究.研究结果表明,所提出的模型兼具原离散时间模型和连续时间模型的优势,能够在不同的情形下保持稳定且优良的性能.     

反馈未知闭环系统的MRIV法辨识

本文提出了一种改进的精致辅助变量法.这种方法适合于反馈未知的闭环系统的参数估计.本文在理论上分析了此方法的一致性,并通过Monte Carlo仿真实验证实了方法的有效性.     

一种新的辅助变量法--重复实验辅助变量法

本文提出了一种新的辅助变量(IV)法--重复实验辅助变量(TRIV,Test-Repeat IV)法.这种方法不但适用于闭环系统辨识,而且具有精度高、速度快、算法简单及易于判阶等优点.在理论上分析了这种方法的一致性,并通过Monte Carl1o仿真实验证实了这种方法的有效性.     

舰艇发电机励磁系统参数辨识研究

介绍辅助变量法与粒子群优化算法在舰艇发电机励磁系统辨识中的应用,并在Labview软件中编写了相应的计算程序。以某舰艇发电机励磁系统为例,在Matlab/Simulink中搭建该励磁系统仿真模型,将采样获得的输入输出数据,输入Labview辨识软件中,估计各参数值。实验分别采用+10%、+50%阶跃响应,辨识系统线性模型和非线性模型,并比较不同噪声幅值情况下的PSO辨识结果。实验结果证明PSO算法在励磁系统参数估计中的有效性。     

Pseudo-canonical forms,identifiable parametrizations and simple parameter estimation for linear multivariable systems: Parameter estimation

In this paper the estimation of parameters for the pseudo-canonical, input-output models introduced in Corrêa, G. O. and Glover, K. (Automatica, 20, 429–442, 1984) is considered. Modifications of three equation-error type estimators (linear least-squares, ‘compensated’ and instrumental variable estimator) are derived, which incorporate the constraint on parameter values associated with these models. The consistency of the proposed estimators is analysed. Finally, results are presented on the construction of parametrization-independent instrumental variables.     

辅助变量最小二乘辨识的均方收敛性

利用随机过程理论,首次证明了递推辅助变量最小二乘(RIVLS)的收敛性,研究了RIVLS算法的收敛速率,给出估算RIVLS算法均方参数估计误差上界的计算公式.分析表明,当辅助矩阵与信息矩阵的乘积是非奇异阵,且关于辅助向量的弱持续激励条件成立时,均方参数估计误差以(1/t)的速率收敛于零.这一研究结果对于提高RIVLS算法的实际应用效果具有重要意义.数字仿真例子表明了该结论的正确性.     

离散时间系统变结构控制研究综述

本文介绍了近年来对离散滑模变结构控制的研究工作,着重对几类主要的控制方案进行了论述,分析了它们各自的特点与相互之间的联系,最后对其发展趋势作了分析与探讨．     

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．     

跟踪-微分器用于连续系统辨识

利用跟踪－ 微分器估计连续系统状态并用最小二乘法估计系统参数,直接对连续系统进行辨识。仿真结果表明,不论是线性系统,还是非线性系统,只要结构对参数是线性的,利用这种方法都能有效地将其参数辨识出来     

Indirect closed-loop identification by optimal instrumental variable method

Recently, a new bias-compensating least-squares (BCLS) method was proposed for the identification of a closed-loop system with high-order controller. The major feature of this method is that it can achieve consistent parameter estimation without modelling the coloured noises acting on the system. This paper studies the connection between the BCLS method and the instrumental variable (IV) family. It is shown that the BCLS method is a kind of weighted instrumental variable (WIV) method whose results do not depend upon the order of the controller.     

Efficient parameterisation of nonlinear system models: a comment on Nöel and Schoukens (2018)

Abstract

Nöel, J. P., & Schoukens, J. [2018. Grey-box state-space identification of nonlinear mechanical vibrations. International Journal of Control, 91, 1–22] discuss a methodology for the discrete-time state-space identification of nonlinear systems and apply this to experimental data from the well known Silverbox nonlinear circuit, producing a model characterised by 13 parameters. This model explains the data very well but the parameter estimates are not well defined in the optimisation results, with the very large confidence bounds suggesting that the model is over-parameterised. This comment shows that this is indeed the case and that the data can be explained equally well by an alternative continuous-time, State-Dependent Parameter (SDP) transfer function model with only 6 parameters, the estimates of which are well defined with very tight confidence bounds. The comment also raises questions about how the model form for nonlinear systems such as the Silverbox should be identified and suggests that the Data-Based Mechanistic (DBM) approach to modelling has some advantages in this regard.     

DISCRETE-TIME VSS CONTROLLER DESIGN

In this paper, a sliding sector for discrete-time systems is defined based on a norm of the system state. Inside the sliding sector, the norm decreases. The discrete-time VSS control law is designed to move the system state from the outside to the inside of the sliding sector. A second order discrete-time system and a second order sampled data system are considered in the simulation. The results show the effectiveness of the proposed VSS controller in both cases. © 1997 by John Wiley & Sons, Ltd.     

扩张状态观测器用于连续系统辨识

使用扩张状态观测器估计连续系统状态和最小二乘法估计系统参数，直接对连续系统进行辨识。仿真结果表明，不管是线性系统还是非线性系统，只要结构对参数是线性的，都能有效地将其参数辨识出来。     

Continuous-time system identification of the steering dynamics of a ship on a river

In this study, we consider the parameter estimation problem of a ship dynamics model. We consider two possible approaches to identify a continuous-time model from real data obtained on a river, where the presence of disturbances is a key issue. The first approach is identification through optimisation using a disturbance observer. The second approach corresponds to the refined instrumental variable method for linear parameter varying systems. In addition, we evaluate the accuracy of the parameter estimation through a sensitivity analysis. The obtained results show an improvement in the parameter estimates compared to identification procedures that do not consider the river disturbances. The application of the model for track-keeping control is also illustrated.     

Identification of continuous-time models for nonlinear dynamic systems from discrete data

A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed.     

Containment control of switched multi-agent systems

In this paper, we consider the containment control problem for the switched multi-agent system which is composed of continuous-time and discrete-time subsystems. Continuous-time protocol based on the relative state measurements of agents are designed for the switched multi-agent system with multiple stationary and dynamic leaders, respectively. By using graph theory and matrix theory, some necessary and sufficient conditions are obtained for solving the containment control problem under arbitrary switching. When the leaders are dynamic, impulsive protocol are also proposed for the switched multi-agent system. The simulation results are given to verify the effectiveness of the theoretical results.     

一类MIMO状态可测的非线性连续系统的激励辨识方法

研究一类 MIMO 状态可测的非线性连续系统的激励辨识问题. 输入激励信号采用高斯白噪声, 均匀采样获得输出状态数据. 根据 Girsanov 定理获得系统参数的渐近无偏估计. 数值仿真试验说明了该方法的有效性并发现耦合多变量系统辨识中的 NNR 现象. 最后给出该系统的分步激励辨识算法.