机场巴士运行过程子空间建模与优化

针对机场巴士运行过程影响因素复杂、难以预测运行时间的问题,建立了一种基于子空间辨识算法的机场巴士运行时间预测模型.首先根据运行过程中所产生的多源大数据,考虑不同时段的乘坐人数、发车间隔、道路拥挤度等因素,建立机场巴士运行过程状态空间模型;然后提取适合描述机场巴士运行过程的特征变量作为模型的输入输出,通过子空间辨识方法对模型进行求解;最后以首都机场巴士的一条实际运营路线作为案例进行仿真分析.计算结果表明,该模型预测平均绝对百分误差和均方误差分别为2.25％和4.77,表现均好于传统的BP神经网络预测模型和最小二乘法辨识模型,具有较好的预测精度,有一定的实际应用价值.     

基于在线辨识的特征模型预测函数控制研究

工业生产过程中运用模型控制问题,对于传统的预测函数控制,一旦预测模型与实际生产过程失配就会造成控制系统的控制性能下降.由于传统的预测函数控制算法中的预测模型通常是离线获得的,因此模型易出现失配.即使采用在线辨识方法来获取预测模型,由于在线辨识需花费时间,影响系统的实时性.通过构建实时数据库的监控层,实时采集生产过程控制参数,在监控层利用在线辨识方法辨识出被控过程的特征模型,然后将辨识出的特征模型作为预测函数控制算法的预测模型传至过程控制回路.采用获得在线辨识预测模型,不会影响系统的实时性.实验结果表明,根据在线辨识的特征模型与实际被控过程的失配很小,并提高了系统的控制性能.     

递推子空间自适应预测控制综合方法

为解决预测控制综合方法中的模型不确定问题,不同于以往利用多胞模型描述的方法,提出了一种新的基于递推子空间的自适应预测控制综合方法.通过在每一步中加入当前输入输出数据重新构建Hankel矩阵,对广义能观矩阵进行更新,从而获得对应的状态空间模型;然后将新获得的模型应用于预测综合的优化求解过程,得到当前时刻的控制律.为提高算法的收敛速度,在辨识的过程中引入了基于模型匹配误差的时变遗忘因子.最后,在慢时变与线性时不变两种情况下进行仿真,验证了所提出算法的有效性.     

有色噪声条件下的子空间辨识改进方法及应用

针对工业生产过程中噪声往往为有色噪声的情况,提出一种改进的子空间辨识方法。传统的子空间辨识方法在系统存在有色噪声时辨识效果不佳,改进方法则采用变换系统模型形式来克服有色噪声对系统的影响,在辨识时直接利用变换系统模型后的数据得到系统较为准确的状态空间模型,实践证明,状态空间模型更适用于工业过程。连续搅拌反应釜（CSTR）系统是一类典型的工业生产系统,将子空间辨识方法应用于CSTR过程的仿真实验,通过比较改进前和改进后的系统预测误差,验证了所提方法的有效性。     

基于模糊T-S模型的批过程建模与最优控制

基于模糊T-S模型,提出一种具有自学习能力的模糊方法用于批过程建模和最优控制.通过引入与均方误差相关的动态误差传递因子,使用改进的梯度下降法,本方法能够辨识模糊T-S预测模型.对于批过程的受限非线性最优控制,基于所辨识的预测模型,运用庞特里亚金最小值原理和平行分布补偿算法,本方法能够把一个复杂非线性系统最优控制设计问题转化为一些基于复杂T-S预测模型的局部线性系统的最优问题,从而给出一种有效和简单的模糊最优控制策略.所提方法用于一个半连续式反应器的建模和最优控制,仿真结果表明新方法是有效和准确的.     

基于辅助模型正交匹配追踪的多输入系统迭代辨识算法

针对含有未知时滞的多输入输出误差系统的时滞与参数辨识问题,提出一种基于辅助模型的正交匹配追踪迭代算法.首先,由于各输入通道的时滞未知,通过设定输入回归长度,对系统模型进行过参数化,得到一个高维的辨识模型,且辨识模型中参数向量为稀疏向量;然后,基于辅助模型思想和正交匹配追踪算法,在每次迭代过程中,对参数向量和辅助模型的输出进行交互估计,即利用正交匹配追踪算法获得参数向量的估计,再利用参数估计值计算辅助模型的输出,并用辅助模型的输出值代替信息向量中的不可测信息项以更新参数估计;最后,根据参数向量的稀疏特征,获得系统的时滞估计.所提出算法可以利用少量的采样数据信息同时获得系统参数和时滞的估计值.仿真结果表明了所提出算法的有效性.     

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.     

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.     

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.

     

基于主元分析的子空间辨识算法

子空间辨识算法作为一种优良的多变量系统辨识算法,最近在国内发展很快.但是现在国内介绍的大多数子空间辨识算法在变量有误差(errors-in-variable)时和闭环辨识时辨识结果却是有偏的,这是因为大多数子空间辨识算法都假设输入变量是没有噪声及辨识算法中存在的一个投影过程.文中介绍了一种新的子空间辨识算法,这种算法利用主元分析(PCA)来获取系统矩阵,避免了其他算法中的投影过程,因此该算法在闭环辨识和变量有误差(errors-in-variable)的情况下,辨识结果也是无偏的.最后给出一个仿真例子说明这种辨识算法的辨识效果良好.     

Frequency domain subspace identification of a tape servo system

As capacity demands for magnetic tape storage systems grow, servo actuator design for tracking data on high density tape media presents new modeling and control design challenges. In this paper a frequency weighted subspace identification algorithm is presented for control relevant model estimation of a tape servo actuator. Common to other subspace identification methods, the proposed algorithm is based on linear algebra techniques providing means for model order selection and model computation. The proposed subspace identification also allows for frequency dependent weightings to emphasize frequency data around the cross-over frequency to find models relevant for control design. The algorithm is applied to data obtained from a tape storage device, demonstrating model order selection and the estimation of servo actuator dynamics with control relevant model fit criteria.     

Identification of the Tennessee Eastman challenge process with subspace methods

The Tennessee Eastman challenge process is a realistic simulation of a chemical process that has been widely used in process control studies. In this case study, several identification methods are examined and used to develop MIMO models that contain seven inputs and ten outputs. ARX and finite impulse response models are identified using reduced-rank regression techniques (PLS and CCR) and state-space models identified with prediction error methods and subspace algorithms. For a variety of reasons, the only successful models are the state-space models produced by two popular subspace algorithms, N4SID and canonical variate analysis (CVA). The CVA model is the most accurate. Important issues for identifying the Tennessee Eastman challenge process and comparisons between the subspace algorithms are also discussed.     

Approximative model reconstruction of cascade systems

This letter considers how to approximately reconstruct a cascade system from a given unstructured system estimate. Many system identification methods, including subspace methods, provide reliable but generally unstructured black-box models. The problem we consider is how to find cascade systems that are close to such black-box models. For this, we use model matching techniques and optimal weighted Hankel-norm approximation to obtain accurate low-order cascade systems. We show that it is possible to bound the reconstruction error in terms of an error tolerance parameter and weighted Hankel singular values. The suggested methods are illustrated on both a numerical example and a real double tank system with experimental data.     

N4SID and MOESP algorithms to highlight the ill-conditioning into subspace identification

In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.     

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．     

System identification and distributed control for multi-rate sampled systems

System outputs with different sampling times may challenge traditional subspace identification methods to generate accurate process models and consequently provide model-based control systems that may not be very effective. The multi-rate identification problem is addressed by dividing the multi-rate sampled system into different subsystems, and a multi-rate distributed model predictive control technique is proposed to control such systems. The performance of the proposed method is evaluated and illustrated by modeling and controlling the Tennessee Eastman challenge problem.     

包含执行器动力学的子空间预测动态控制分配方法

针对具有冗余执行机构的过驱动系统的最优控制分配问题, 基于数据驱动的子空间辨识方法和预测控制理论, 提出了一种考虑执行器动力学特性的动态控制分配新方法. 在考虑范数有界不确定性的在线子空间辨识的基础上, 对执行器动力学特性进行不确定性建模, 再结合预测控制理论进行动态控制分配. 从而将执行机构的动力学建模、控制量最优分配和执行机构控制律的设计包含在一个子系统框架内, 对执行机构的模型不确定性具有更好的鲁棒性. 最后给出仿真实例验证了算法的有效性.     

On-orbit system identification experiments on Engineering Test Satellite-VI

On-orbit system identification experiments were performed using the Engineering Test Satellite-VI (ETS-VI), launched in 1994. The purpose of the experiments is to check the validity of a mathematical model for ETS-VI which was previously obtained by ground experiments. Two kinds of system identification schemes are utilized. One is a traditional scheme based on polynomial black-box models such as the prediction error method, and the other is a subspace method based on state-space models. The purpose of this paper is to compare these system identification schemes using experimental data. It will be shown that the subspace method proves to be promising for the system identification of large space structures typified by ETS-VI.     

Comparison of input signals in subspace identification of multivariable ill-conditioned systems

Ill-conditioned processes often produce data of low quality for model identification in general, and for subspace identification in particular, because data vectors of different outputs are typically close to collinearity, being aligned in the “strong” direction. One of the solutions suggested in the literature is the use of appropriate input signals, usually called “rotated” inputs, which must excite sufficiently the process in the “weak” direction. In this paper open-loop (uncorrelated and rotated) random signals are compared against inputs generated in closed-loop operation, with the aim of finding the most appropriate ones to be used in multivariable subspace identification of ill-conditioned processes. Two multivariable ill-conditioned processes are investigated and as a result it is found that closed-loop identification gives superior models, both in the sense of lower error in the frequency response and in terms of higher performance when used to build a model predictive control system.     

Criteria for informative experiments with subspace identification

Informative experiments are identification experiments which contain sufficient information for an identification algorithm to discriminate between different models in an intended model set. In this paper, a particular set of identification algorithms, namely subspace based identification, is considered. Criteria for experiments to be informative with these methods in the deterministic setup and the combined deterministic-stochastic setup are presented. It is pointed out that if these criteria are not satisfied, interesting phenomena, in which perfect cancellations of the deterministic components and the stochastic components occur in a subspace projection, may occur. It is further shown that such cancellations can indeed be avoided under mild conditions.