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1.
跟踪—微分器用于连续系统辨识   总被引:3,自引:1,他引:2  
张文革  韩京清 《控制与决策》1999,14(11):557-560
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。  相似文献   

2.
扩张状态观测器用于连续系统辨识   总被引:6,自引:0,他引:6  
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。  相似文献   

3.
本文提出一种辨识连续Hammerstein模型参数的方法,该方法利用不同幅值的M周期序列作为系统的输入,通过DFT变换,离析出线性子系统的连续模型,并由频域辨识法及时域辨识法直接估计出连续模型的参数和非线性特性的系数。数字仿真结果表明本算法是可行且有效的。  相似文献   

4.
一般双率随机系统状态空间模型及其辨识   总被引:16,自引:1,他引:16  
对于双率采样数据系统,本文使用提升技术,推导了双率系统的提升状态空间模型.对 于系统状态可测量的双率系统,利用最小二乘原理辨识提升系统模型的参数矩阵;对于状态不 可测的未知参数双率系统,利用递阶辨识原理,提出了双率系统递阶状态空间模型辨识方法,来 辨识系统的状态和参数.具体做法:基于获得的状态估计和提升系统的输入输出数据递归估计 系统参数,然后基于获得的参数估计,计算系统的状态.  相似文献   

5.
徐珂文  戴邵武 《计算机仿真》2006,23(9):62-64,99
大多数实际物理系统的模型本质上是连续的,连续时间模型为物理系统的行为提供了更好的解释。提出了一种采样数据中存在高频有色噪声的连续模型辨识方法,该方法通过引入积分运算,将连续时间系统的微分方程模型转换为积分方程模型,从而使噪声的影响可以忽略,然后直接利用传统的最小二乘法估计出系统的连续时间模型参数。该方法具有较强的抗有色噪声干扰的鲁棒性、计算方法简单、参数辨识精度高等优点。仿真实例验证了该方法的有效性和可行性。  相似文献   

6.
多变量系统状态空间模型的递阶辨识   总被引:11,自引:1,他引:11  
丁锋  萧德云 《控制与决策》2005,20(8):848-853
研究多变量系统状态空间模型的递阶辨识问题,推广了作者提出的标量系统状态和参数联合辨识算法.当状态可量测时,利用最小二乘原理直接辨识状态空间模型的参数矩阵;当状态不可测时,利用递阶辨识原理提出了状态空间模型递阶辨识方法,使用系统输入输出数据来估计系统的未知状态和参数.状态空间模型递阶辨识方法分为两步:首先假设系统状态是已知的(即参数估计算法中的未知系统状态用其估计代替),基于状态估计和系统输入输出数据递归计算系统参数估计;然后基于系统输入输出数据和获得的参数估计,递归计算系统的状态估计.  相似文献   

7.
控制系统的一种直接盲辨识方法   总被引:3,自引:1,他引:3  
提出一种在时域中直接盲辨识系统传递函数的方法。首先利用过采样方法,将SISO系统转换为具有相同零极点的SIMO系统进行处理;然后通过对新的SIMO模型先估计分子参数、后估计分母参数的方法,即可获得原SISO模型的参数估计。该方法也可用于辨识非最小相位系统。  相似文献   

8.
利用调制函数法辨识非线性连续系统的模糊模型参数.系统的动力学微分方程存在微分项,通过输入输出数据辨识模糊模型参数时不能忽略扰动的影响,因此辨识模糊模型参数比较困难.利用调制函数法可以消除微分项,通过无微分项的联立方程的求解容易进行模糊模型参数辨识.几个非线性连续系统的仿真实验验证了所设计的利用调制函数法的模糊模型参数辨识的正确性和有效性.  相似文献   

9.
非线性系统参数集员辨识的一种新方法   总被引:3,自引:0,他引:3  
在噪声未知但有界的情况下,研究了非线性系统参数的集员辨识问题,提出了先对非线性系统参数可行集的中心进行估计,再估计参数可行集大小的集员辨识两步法。  相似文献   

10.
针对传统辨识方法不适用于具有不稳定初始状态的连续时间系统的问题,提出一种全新的状态估计辨识法.首先,用状态空间模型中状态变量的初始值表征系统初始状态,并将状态变量的初始值看作待辨识参数的一部分.然后,用粒子群优化算法获得所有参数的最优估计.该方法在测试开始前不需要任何过程数据,对测试信号无任何要求,可直接用于闭环辨识.仿真实验证明该算法是有效的.  相似文献   

11.
Stability robustness analysis of a system under parametric perturbations is concerned with characterizing a region in the parameter space in which the system remains stable. In this paper, two methods are presented to estimate the stability robustness region of a linear, time-invariant, discrete-time system under multiparameter additive perturbations. An inherent difficulty, which originates from the nonlinear appearance of the perturbation parameters in the inequalities defining the robustness region, is resolved by transforming the problem to stability of a higher order continuous-time system. This allows for application of the available results on stability robustness of continuous-time systems to discrete-time systems. The results are also applied to stability analysis of discrete-time interconnected systems, where the interconnections are treated as perturbations on decoupled stable subsystems  相似文献   

12.
A new bias-compensating least squares (LS) method is presented for the parameter estimation of linear single-input single-output (SISO) continuous-time systems. A discrete-time model obtained by using the linear integral filter is augmented by introducing a pre-filter on the input and then the parameters of the augmented model are estimated by the conventional LS method. The distinct characteristic roots of the pre-filter are used to estimate the bias in the LS estimate. The pre-filter should be chosen so that its frequency bandwidth is wider than those of the system and the input signals. Since the new method requires minimal information on the noise characteristics, it is easily applicable to the case of coloured noise.  相似文献   

13.
The problem of sampled-data (SD) based adaptive linear quadratic (LQ) optimal control is considered for linear stochastic continuous-time systems with unknown parameters and disturbances. To overcome the difficulties caused by the unknown parameters and incompleteness of the state information, and to probe into the influence of sample size on system performance, a cost-biased parameter estimator and an adaptive control design method are presented. Under the assumption that the unknown parameter belongs to a known finite set, some sufficient conditions ensuring the convergence of the parameter estimate are obtained. It is shown that when the sample step size is small, the SD-based adaptive control is LQ optimal for the corresponding discretized system, and sub-optimal compared with that of the case where the parameter is known and the information is complete.  相似文献   

14.
The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.  相似文献   

15.
The parameter estimation of excitation systems using a proposed indirect stochastic method is presented. In this method, the discrete-time ARMA model corresponding to each block of the excitation system is first identified from sampled input/output data. Then, the proper reduced order of the continuous-time model is determined using the concept of dominant energy modes. Finally, the parameters of the continuous-time model are found by matching its frequency response to that of the identified ARMA model. The proposed method is used to estimate the parameters of an excitation system in a pumped storage power plant. The results show that the estimated continuous-time models are rather close to those supplied by the vender  相似文献   

16.
Linear filter methods have been used in the field of continuous-time identification over a considerable time period. Due to the effectiveness and simplicity of the approach, they have found widespread applications and drawn much interest from the system identification community. However, the estimation of time delay along with continuous-time model parameters has remained an unsolved problem although there are some simple step response based methods. In this paper, a new linear filter method is introduced for simultaneous parameter and delay estimation of continuous-time transfer function models. The proposed method estimates the time delay along with other model parameters in an iterative way through simple linear regression. In addition, the estimated delay is not necessarily an integer multiple of the sampling interval. The applicability of the developed procedure is demonstrated by simulations as well as a laboratory scale experimental example.  相似文献   

17.
The purpose of this paper is to study the filtering problems from the viewpoint of the information theory. For a linear system it is proved that the necessary and sufficient condition for maximizing the mutual information between a state and the estimate is to minimize the entropy of the estimation error. Then we derive the Kalman-Bucy filter for both the discrete-time and the continuous-time systems by an application of the information theory. Furthermore, the approach is extended to the nonlinear dynamical systems with noisy observations and then the information structures of the optimal filter for a continuous-time nonlinear system are made clear, which has been presented as the interesting open problems by Bucy.  相似文献   

18.
This paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-time system is a linear system, its output response is a highly nonlinear function with respect to the system parameters. In order to propose a direct estimation algorithm, a criterion function is constructed between the response output and the observation output by means of the discrete sampled data. Then a scheme by combining the Newton iteration and the least squares iteration is builded to minimise the criterion function and derive the parameter estimation algorithm. In light of the different features between the system parameters and the output function, two sub-algorithms are derived by using the parameter decomposition. In order to remove the associate terms between the two sub-algorithms, a Newton and least squares iterative algorithm is deduced to identify system parameters. Compared with the Newton iterative estimation algorithm without the parameter decomposition, the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced because the dimension of the Hessian matrix is lessened after the parameter decomposition. The experimental results show that the proposed algorithm has good performance.  相似文献   

19.
For a linear time-invariant system of order d⩾2 with a white noise disturbance, the input and the output are assumed to be sampled at regular time intervals. Using only these observations, some approximate values of the first d-1 derivatives are obtained by a numerical differentiation scheme, and the unknown system parameters are estimated by a discretization of the continuous-time least-squares formulas. These parameter estimates have an error which does not approach zero as the sampling interval approaches zero. This asymptotic error is shown to be associated with the inconsistency of the quadratic variation estimate of the white noise local variance based on the sampled observations. The use of an explicit correction term in the least-squares estimates or the use of some special numerical differentiation formulas eliminates the error in the estimates  相似文献   

20.
The paper presents a new dual-mode nonlinear model predictive control (NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control. The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework. The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible; and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions. Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system. Recursive feasibility and closed-loop stability of this NMPC are established. The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator (LQR) method.   相似文献   

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