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。     

基于新息估计的递推闭环子空间辨识

本文针对闭环控制系统提出了一种基于新息估计的递推闭环子空间辨识方法.首先,通过新息估计减小闭环控制系统中过去噪声对未来输入的影响,进而获得子空间矩阵的参数估计;然后,运用最小二乘的递推方法进一步降低噪声的影响,从而实现下三角Toeplitz矩阵的参数估计;最后,采用Kung辨识算法对由估计参数构造的Hankel矩阵提取系统矩阵.通过仿真实验验证了所提方法的有效性和优越性.     

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。     

基于间接辨识方法的闭环系统辨识

针对单输入-单输出离散闭环系统问题,采用开环系统中的递推最小二乘法来进行参数估计。利用间接辨识方法,根据闭环系统的输入输出信号,用开环辨识的方法推导得到闭环系统的传递函数,再由已知的控制器传递函数推导出对象的辨识模型。最后,通过MATLAB进行仿真,验证了间接辨识的方法对闭环系统进行参数估计。     

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。     

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．     

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。     

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.     

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.     

PID controller design for fractional-order systems with time delays

Classical proper PID controllers are designed for linear time invariant plants whose transfer functions are rational functions of s^α, where 0<α<1, and s is the Laplace transform variable. Effect of input-output time delay on the range of allowable controller parameters is investigated. The allowable PID controller parameters are determined from a small gain type of argument used earlier for finite dimensional plants.     

Stability analysis of fuzzy large-scale systems with time delays in interconnections

The stability problem of fuzzy large-scale systems with time delays in interconnections is considered in this paper. The fuzzy large-scale system consists of J interconnected subsystems, which are represented by Takagi-Sugeno (T-S) fuzzy models. The stability conditions are derived using the Lyapunov-Krasovskii functional approach. This condition is independent of time delay and does not need the solution of a Lyapunov equation or Riccati equation. A stabilization approach for the delayed fuzzy large-scale systems through fuzzy state feedback-based controller is also presented in this paper. Finally, an example is given to demonstrate the result.     

Input-to-state stability and integral input-to-state stability of nonlinear impulsive systems with delays

This paper is concerned with analyzing input-to-state stability (ISS) and integral-ISS (iISS) for nonlinear impulsive systems with delays. Razumikhin-type theorems are established which guarantee ISS/iISS for delayed impulsive systems with external input affecting both the continuous dynamics and the discrete dynamics. It is shown that when the delayed continuous dynamics are ISS/iISS but the discrete dynamics governing the impulses are not, the ISS/iISS property of the impulsive system can be retained if the length of the impulsive interval is large enough. Conversely, when the delayed continuous dynamics are not ISS/iISS but the discrete dynamics governing the impulses are, the impulsive system can achieve ISS/iISS if the sum of the length of the impulsive interval and the time delay is small enough. In particular, when one of the delayed continuous dynamics and the discrete dynamics are ISS/iISS and the others are stable for the zero input, the impulsive system can keep ISS/iISS no matter how often the impulses occur. Our proposed results are evaluated using two illustrative examples to show their effectiveness.