多输入多输出变量带误差模型的最坏情况频域辨识

本文将单输入单输出(SISO)变量带误差(EIV)模型的频域最坏情况辨识方法推广应用于多输入多输出 (MIMO)情况. 类似于SISO情况, 多输入多输出变量带误差(MIMO EIV)模型的辨识模型集合由估计的系统名义模型及 其最坏情况误差界描述. 所估计的系统名义模型表征为正规右图符号, 其最坏情况误差界具有可能的更少保守性, 可利 用EIV 模型的先验信息和后验信息由v-gap度量量化得到. 因此, 这种模型集合非常适合于后期利用Vinnicombe提出 的H1回路成形法设计鲁棒控制器. 最后, 利用一数值仿真实例验证所提出辨识方法的有效性.     

多输入多输出变量带误差模型的最坏情况频域辨识（英文）

本文将单输入单输出(SISO)变母带误差(EIV)模型的频域最坏情况辨识方法推广应用于多输入多输出(MIMO)情况.类似于SISO情况,多输入多输出变量带误差(MIMO EIV)模型的辨识模型集合由估计的系统名义模型及其最坏情况误差界描述.所估计的系统名义模型表征为正规右图符号,其最坏情况误差界具有可能的更少保守性,可利用EIV模型的先验信息和后验信息由v-gap度量量化得到.因此,这种模型集合非常适合于后期利用Vinnicombe提出的H。。回路成形法设计鲁棒控制器.最后,利用一数值仿真实例验证所提出辨识方法的有效性.     

基于粒子群优化的Wiener模型辨识与实例研究

针对一类工业过程中可描述成Wiener模型的非线性系统,其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题．利用粒子群优化(PSO)算法在整个参数空间内并行搜索获得极小值优化问题的最优解(Wiener模型的最优估计),通过对粒子的迭代轨迹进行分析,改进了PSO算法中惯性权重和学习因子的选择．通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性,并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究,取得了满意的辨识效果．     

一种基于粒子群优化的非线性系统辨识方法

针对单入单出离散时间非线性动态系统提出一种辨识方法.该方法采用带误差修正的改进泛模型作为非线性系统的结构模型,模型中的时变特征参量及误差修正系数采用粒子群（PSO）算法优化,优化后的模型可以逼近非线性系统.该方法简单、易于实现.通过对Box-Jenkins煤气炉数据等非线性被控对象的仿真研究及对模型的分析,表明了所提出算法的有效性.     

基于脉冲响应的输出误差模型的辨识

基于系统脉冲响应参数, 利用相关分析方法, 提出了一种辨识输出误差模型参数的方法. 该方法是利用有限脉冲响应模型逼近输出误差模型, 通过依次递增脉冲响应参数的数目N来提高逼近精度. 理论分析表明, 只要N足够大, 模型的辨识精度可以满足实际要求. 提出的辨识方法可以在假设阶次N =1的条件下, 依次递增计算N较大时的脉冲响应参数和目标函数值, 从而根据脉冲响应确定系统的参数. 仿真试验说明提出的方法估计输出误差模型的参数是有效的.     

具有预负载非线性特性的双率系统递推最小二乘估计算法

针对具有预负载非线性特性的双率系统, 提出一种新的辨识方法. 借助切换函数简化系统模型, 通过损失数据模型估计系统损失的输出数据, 进而利用系统所有输入和输出数据, 提出相应双率系统递推最小二乘算法. 与多项式转换方法相比, 该方法能够直接辨识出系统参数. 仿真结果验证了所提出方法的有效性.

     

融合改进蚁狮算法和T-S模糊模型的噪声非线性系统辨识

针对传统的T-S模糊辨识方法难以准确辨识含噪声的非线性系统问题,将噪声信号和系统的其他输入变量一起作为模糊前件的输入,采用具有动态随机搜索和寻优半径连续收缩机制的改进蚁狮算法优化模糊前件的结构参数,使用加权最小二乘法实现模糊后件的参数辨识.数值仿真表明,所提出的辨识方法可以有效抑制噪声的影响,经过改进蚁狮算法优化后的T-S模糊模型辨识效果更好.最后,将所提出方法用于直拉硅单晶生长热模型的辨识,实验结果表明该方法优于传统的辨识方法.     

高超声速飞行器惯导系统误差参数两次优化辨识方法

为满足高超声速飞行器高精度和高可靠性的导航要求,提出一种在发射惯性系下利用智能优化算法实现捷联惯性系统误差参数两次优化辨识的方法.建立惯性测量单元(IMU)误差补偿模型和完整的非线性捷联惯性系统导航模型,为数值优化计算提供准确的模型基础.基于SINS/GPS/CNS组合导航系统信息,建立陀螺仪误差优化模型和加速度计误差优化模型,采用两次优化策略分步估计捷联惯性系统误差参数:首先利用粒子群算法对陀螺仪误差参数进行优化辨识和补偿;然后利用粒子群算法对加速度计误差参数进行优化辨识.仿真结果表明,基于组合导航系统信息和非线性优化模型,两次优化辨识方法能够在线辨识出高精度的捷联惯性系统误差参数,陀螺仪和加速度计优化参数值的相对误差均在20%以内,从而有效提高了高超声速飞行器导航精度.     

双率系统辅助模型框架下的随机牛顿递推辨识

针对双率系统, 采用基于辅助模型的改进随机牛顿递推算法辨识输出误差模型. 若当前参数估计对应的估计系统不稳定, 则出现中间不可测时刻输出估计发散, 辨识过程停止. 增加非线性模型与常规辅助模型一起为下步递推提供信息估计, 确保递推进行. 为避免出现输入不充分或者广泛时Hessian 阵奇异或者接近奇异的情况,在Hessian 阵的递推中增加对称正定矩阵. 最后给出了所提出辨识算法的一致收敛性证明.     

基于随机配置网络的非线性系统智能建模方法

针对一类非线性离散动态系统,研究非线性系统的智能建模方法.首先,采用带遗忘因子的递推最小二乘法对低阶模型的未知参数进行辨识;然后,对高阶非线性部分采用随机配置网络进行估计;最后,利用两种辨识方法在外部误差准则下对系统进行交替辨识,进而提出一种改进的非线性系统交替辨识的智能建模方法.将随机配置网络与递推最小二乘算法相结合,可有效提高非线性系统的辨识精度,并且通过数值仿真实验进行对比分析以验证所提出算法的有效性.     

Parameter estimation of continuous-time bilinear systems based on numerical integration and separable non-linear least-squares

This paper considers the problem of estimating the parameters in continuous-time bilinear systems. The system identification approach is based on numerical integration and separable non-linear least-squares. The situation where the output signal is contaminated with noise is also discussed. The suggested estimation method uses a bias compensating approach and the model parameters together with parameters associated with the noise are estimated by solving an overdetermined system of equation in a least-squares sense.     

Predictor-based continuous-discrete nonlinear observer design subject to aperiodic sampled and delayed measurement

In this paper, a predictor-based continuous-discrete nonlinear observer is proposed for a class of nonaffine Lipschitz nonlinear systems with aperiodic sampled delayed output measurements and external disturbance. Firstly, this study introduces a class of delayed sampling hybrid nonlinear systems. By employing Lyapunov techniques and trajectory-based stability theory, sufficient conditions are established for ensuring input-to-state stability of the delayed sampling hybrid nonlinear system with respect to external disturbances. Then the predictor-based continuous-discrete nonlinear observer is designed which consists of a continuous-time observer, a compensating injector and a predictor. The continuous-time observer is utilized to obtain continuous and delay-free state estimation. The compensating injector is designed to compensate for output errors that occur between sampling instants. Furthermore, the predictor is employed to obtain delay-free output error information, which is then utilized by the continuous-time observer for feedback correction. The proposed observer's exponential input-to-state property against the disturbance is proved via the proposed hybrid system stability theory. The effectiveness of the proposed observers has been demonstrated through numerical simulations and performance comparisons with the zero-order holder-based observer and the output predictor-based observer designs in order to highlight the effectiveness and advantages of the proposed observer.     

An optimal IV technique for identifying continuous-time transfer function model of multiple input systems

An instrumental variable method for continuous-time model identification is proposed for multiple input single output systems where the characteristic polynomials of the transfer functions associated with each input are not constrained to be identical. An associated model order determination procedure is shown to be reasonably successful. Monte Carlo simulation analyses are used to demonstrate the properties and general robustness of the model order selection and parameter estimation schemes. The results obtained to model a winding process and an industrial binary distillation column illustrate the practical applicability of the proposed identification scheme.     

Identification of time delays in linear stochastic systems

This paper presents a new approach to the explicit identification of an input time delay in continuous-time linear systems. The system model is converted to a discrete-time version, assuming that a digital computer is to be used for time delay estimation and control. A recursive identification algorithm based on parallel Kalman filtering and Bayes' estimation is developed. The sampling rate is adapted during the time delay estimation process using the most recent estimate of the time delay. This method assures that the estimate of the time delay approaches the true value with each successive iteration. The proposed method also has the advantage of a fast convergence rate because prior knowledge of the delay, if available, can be effectively utilized.     

Identification with stochastic sampling time jitter

This work investigates how stochastic sampling jitter noise affects the result of system identification, and proposes a modification of known approaches to mitigate the effects of sampling jitter, when the jitter is unknown and not directly measurable. By just assuming conventional additive measurement noise, the analysis shows that the identified model will get a bias in the transfer function amplitude that increases for higher frequencies. A frequency domain approach with a continuous-time model allows an analysis framework for sampling jitter noise. The bias and covariance in the frequency domain model are derived. These are used in bias compensated (weighted) least squares algorithms, and by asymptotic arguments this leads to a maximum likelihood algorithm. Continuous-time output error models are used for numerical illustrations.     

Frequency domain identification of continuous-time output error models, Part II: Non-uniformly sampled data and B-spline output approximation

This paper treats several aspects relevant to the identification of continuous-time output error (OE) models based on non-uniformly sampled output data. The exact method for doing this is well known in the time domain, where the continuous-time system is discretized, simulated and the result is fitted in a mean square sense to measured data. The material presented here is based on a method proposed in a companion paper (Gillberg & Ljung, 2010) which deals with the same topic but for the case of uniformly sampled data. In this text it will be shown how that method suggests that the output should be reconstructed using a B-spline with uniformly distributed knots. This representation can then be used to directly identify the continuous-time system without proceeding via discretization. Only the relative degree of the model is used to choose the order of the spline.     

Recursive parameter estimation of transfer function matrix models via Simpson's integrating rules

Recursive formulae for repeated integration of a continuous-time function with uniformly sampled data using Simpson's 1/3 and 3/8 integrating rules are derived. Combined with the recursive algorithm of the least-squares solution, a method for recursive parameter estimation of transfer function matrix models in multiple-input-multiple-output systems is proposed. It is demonstrated that the use of the popular integrating rules for parameter estimation can be as effective as sophisticated methods that use orthogonal functions and the associated operational properties reported in the literature. The proposed algorithm is suitable for on-line applications and computer programming. Three numerical examples are included to illustrate the applicability of the proposed method.     

Projection-based identification algorithm for grey-box continuous-time models

In this paper, a new identification method for continuous-time models, which can handle various grey-box structures and has strong robustness, is presented. The proposed method is based on an incremental model update scheme and the projection onto the subspace which reflects the model structure. By utilising these schemes, robustness of other continuous-time system identification methods and versatility of generic optimisation algorithms can be integrated into the proposed method. The effectiveness of the proposed method is demonstrated through numerical examples related to a grey-box model in closed-loop system and systems with unknown time-delay.     

Continuous-time System Identification with Nuclear Norm Minimization and GPMF-based Subspace Method

To improve the accuracy and effectiveness of continuous-time (CT) system identification, this paper introduces a novel method that incorporates the nuclear norm minimization (NNM) with the generalized Poisson moment functional (GPMF) based subspace method. The GPMF algorithm provides a simple linear mapping for subspace identification without the timederivatives of the input and output measurements to avoid amplification of measurement noise, and the NNM is a heuristic convex relaxation of the rank minimization. The Hankel matrix with minimized nuclear norm is used to determine the model order and to avoid the over-parameterization in subspace identification method (SIM). Furthermore, the algorithm to solve the NNM problem in CT case is also deduced with alternating direction methods of multipliers (ADMM). Lastly, two numerical examples are presented to evaluate the performance of the proposed method and to show the advantages of the proposed method over the existing methods.