多个未知时延的ＭＩＳＯ系统的递推辨识

对于未知时延的多输入单输出（ＭＩＳＯ）系统,借助分离性原理,推导出迭代的可分离的非线性最小二乘（ＳＮＬＳ）辨识方法．为降低收敛于局部最小的可能性,利用全局优化理论,推导了全局可分离的非线性最小二乘（ＧＳＮＬＳ）辨识方法;为消除强观测噪声所引起的参数估计的偏差,将ＧＳＮＬＳ方法调整为一新颖的全局可分离的非线性多新息递推最小二乘（ＧＳＮＭＩＲＬＳ）辨识方法,仿真实验验证了算法的有效性．

     

基于递推最小二乘法的温箱系统辨识

温箱在生活中应用越来越广泛,对于温箱的精确控制已成为一项重要课题,所以对于温箱模型的分析和确定十分重要。以温箱为辨识对象,在温箱热稳定的基础上给温箱输入M序列辨识信号来获取辨识数据。采用递推最小二乘法对温箱进行辨识并按照残差定阶法对温箱系统进行定阶、定型。最后在MATLAB中仿真并验证。     

输入输出端存在噪声时系统的无偏辨识

在实际辨识中，观测到的系统输入数据往往被噪声所污染，这给无偏辨识系统带来困难，基于和文［６］中相同的原理，本文提出了一种递推的偏差补偿最小二乘法。它通过在系统输入端已知滤波器，将已知零点嵌入被辨识系统中，然后利用这些零点所提供的信息在线估计辨识偏差，并将偏差加以补偿，从而实现系统的无偏估计。     

基于递阶分解聚类的非线性系统递推模糊辨识

提出一种基于递阶分解聚类的递推模糊辨识方法．采用半模糊化方法对论域内的样本进行归类,根据各子集“线性化”程度评判模糊聚类的有效性,通过对性能最差的子集进行分解并辨识新增子模型的参数,逐步完成整个样本空间的模糊划分和模型辨识过程．在线辨识时采用递推最小二乘算法对模糊规则进行修正,同时可根据建模精度的要求删除性能最差的规则,并确立新模糊规则．仿真研究表明了该方法的有效性．     

基于电压递推与FFRELS的航空蓄电池模型参数动态辨识

摘要：针对电池等效电路模型参数具有时变性,难以准确辨识的问题,建立了二阶RC航空电池模型,提出了基于遗忘因子扩展递推最小二乘法(FFRELS)的参数辨识算法;同时针对电池开路电压测量时间长的问题,创建了蓄电池开路电压辨识递推模型,实现开路电压的动态估计,该方法能够准确辨识出模型的参数,有效提高蓄电池开路电压的辨识精度。最后通过仿真实验,验证了该方法能够提高电池模型参数的辨识精度。     

基于MATLAB的递推最小二乘法辨识与仿真

通过对最小二乘算法的分析,推导出了递推最小二乘法的运算公式,提出了基于MATLAB/Simulink的使用递推最小二乘法进行参数辨识的设计与仿真方法。并采用Simulink建立系统的仿真对象模型和运用MATLAB的S-函数编写最小二乘递推算法,结合实例给出相应的仿真结果和分析。仿真结果表明,该仿真方法克服了传统编程语言仿真时繁杂、难度高、周期长的缺点,是一种简单、有效的最小二乘法的编程仿真方法。     

多输入多输出系统参数的集员辨识

本文讨论了多输入多输出系统参数的集员辨识。在系统动态噪声功率有界的假定下导出了ＭＩＭＯ系统参数的成员集合是个椭球，证明在一定条件下椭球趋向于一点。文中的仿真例子验证了上述结果。     

焊接过程的非线性辨识

准小孔弧焊接过程是个典型的非线性系统。为了得到它的模型,本文采用最小二乘法对其进行辨识。仿真结果表明最小二乘法是非线性系统辨识的一种非常有效的方法。     

永磁同步电机的建模与参数辨识

永磁同步电机由于其优越的性能而广泛的应用于精确的伺服控制控制系统中,针对电机的参数会随着工作环境的变化而变化,导致控制的精度受到很大影响.提出采用实时地对电机参数进行辨识,成为提高整个系统性能的保证.在深入分析永磁同步电机的电磁特性后,推导出永磁同步电机在两相静止坐标下的电机数学模型;并在该数学模型下利用递推最小二乘法编写辨识算法,对电机的参数进行在线辨识.利用matlab软件平台构建永磁同步电机双闭环仿真模型进行仿真,仿真结果表明算法能精确地辨识电机的参数,具有较好的鲁棒性.从而证明上述方法的正确性,能够提高控制系统的精度.     

MIMO系统参数集员辨识的优化算法

在噪声瞬时有界的情况下，提出了多输入多输出系统参数集员辨识的两种优化算法，这两种优化算法具有识别冗余数据的能力，而且与最小二乘方法相比具有更好的性能，文中的仿真例子验证了上述结果。     

Identification of continuous-time systems with multiple unknown time delays by global nonlinear least-squares and instrumental variable methods

This paper considers the identification problem of multiple input single output (MISO) continuous-time systems with unknown time delays of the inputs, from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived to significantly reduce the possibility of convergence to a local minimum, by using the stochastic global-optimization techniques. Furthermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to remove the biases of the estimates in the presence of high measurement noise. Simulation results show that the proposed algorithms work quite well.     

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.     

Recursive identification of switched ARX systems

We consider the problem of recursively identifying the parameters of a deterministic discrete-time Switched Auto-Regressive eXogenous (SARX) model, under the assumption that the number of models, the model orders and the mode sequence are unknown. The key to our approach is to view the identification of multiple ARX models as the identification of a single, though more complex, lifted dynamical model built by applying a polynomial embedding to the input/output data. We show that the dynamics of this lifted model do not depend on the value of the discrete state or the switching mechanism, and are linear on the so-called hybrid model parameters. Therefore, one can identify the parameters of the lifted model using a standard recursive identifier applied to the embedded input/output data. The estimated hybrid model parameters are then used to build a polynomial whose derivatives at a regressor give an estimate of the parameters of the ARX model generating that regressor. The estimated ARX model parameters are shown to converge exponentially to their true values under a suitable persistence of excitation condition on a projection of the embedded input/output data. Such a condition is a natural generalization of the well known result for ARX models. Although our algorithm is designed for perfect input/output data, our experiments also evaluate its performance as a function of the level of noise for different choices of the number of models and model orders. We also present an application to temporal video segmentation.     

多输入多输出非线性多时滞系统的直接自适应模糊跟踪控制

针对多输入多输出非线性多时滞系统,提出了一种直接自适应模糊跟踪控制方案．该方案有机综合了自适应控制和H∞ 控制,构建了一种自适应时滞模糊逻辑系统用来逼近有多重时滞的未知函数;设计了H∞ 补偿器来抵消模糊逼近误差和外部扰动．根据跟踪误差给出了参数调节规律,构造了包含时滞的李亚普诺夫函数,从而证明了误差闭环系统满足期望的H∞ 跟踪性能．仿真结果表明了该方案的可行性．     

Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise

This paper considers the recursive identification problems for a class of multivariate autoregressive equation-error systems with autoregressive noise. By decomposing the system into several regressive identification subsystems, a maximum likelihood recursive generalised least squares identification algorithm is proposed to identify the parameter vectors in each subsystem. In addition, a multivariate recursive generalised least squares algorithm is derived as a comparison. The numerical simulation results indicate that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the parameters of the multivariate autoregressive equation-error autoregressive systems and get more accurate parameter estimates than the multivariate recursive generalised least squares algorithm.     

Recursive identification of Hammerstein systems with application to electrically stimulated muscle

Modeling of electrically stimulated muscle is considered in this paper where a Hammerstein structure is selected to represent the isometric response. Motivated by the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structures is investigated. A recursive algorithm is then developed to address limitations in the approaches currently available. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Hence the procedure is termed the alternately recursive least square (ARLS) algorithm. When compared with the leading approach in this application area, ARLS exhibits superior performance in both numerical simulations and experimental tests with electrically stimulated muscle.     

New results on discrete-time delay systems identification

A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector.The gradient algorithm is used to deal with the identification problem.The effectiveness of this method is illustrated through simulation.     

Recursive parameter estimation of exhaust gas oxygen sensors with input-dependent time delay and linear parameters

Exhaust gas oxygen sensors are widely used for emission control in internal combustion engine systems. Due to their working principle and their positioning, these sensors are subject to input-dependent time delays and input-dependent linear parameters. Consequently, the corresponding time delays and linear parameters can vary fast, i.e. at the same rate as the respective input signals. This paper presents an extension of an existing gradient-based least-squares algorithm and its application to recursively estimate the input-dependent time delays and linear parameters of wide-range oxygen sensors in diesel engines. The extended algorithm is applied in a detailed simulation and experimental study involving real wide-range oxygen sensors that are affected by drift, aging, clogging and manipulation. The input-dependent time delay and linear parameter estimates obtained with the proposed recursive algorithm accurately reproduce the estimates obtained with a numerical offline optimization procedure.     

Permanence and global stability for a two-species cooperative system with time delays in a two-patch environment

A two-species nonautonomous delayed system is considered. The system models two-population dispersal between two patches in a heterogeneous environment. It is shown that the system is permanent under some appropriate conditions, and sufficient conditions are established for global stability of the system.