首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到10条相似文献,搜索用时 125 毫秒
1.
We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.  相似文献   

2.
In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.  相似文献   

3.
Constrained identification of state-space models representing structural dynamic systems is addressed. Based on physical insight, transfer function constraints are formulated in terms of the state-space parametrization. A simple example shows that a method tailored for this application, which utilizes the non-uniqueness of a state-space model, outperforms the classic sequential quadratic programming method in terms of robustness and convergence properties. The method is also successfully applied to real experimental data of a plane frame structure.  相似文献   

4.
A modeling method is proposed for a dynamic fast steering mirror (FSM) system with dual inputs and dual outputs. A physical model of the FSM system is derived based on first principles, describing the dynamics and coupling between the inputs and outputs of the FSM system. The physical model is then represented in a state-space form. Unknown parameters in the state-space model are identified by the subspace identification algorithm, based on the measured input-output data of the FSM system. The accuracy of the state-space model is evaluated by comparing the model estimates with measurements. The variance-accounted-for value of the state-space model is better than 97%, not only for the modeling data but also for the validation data set, indicating high accuracy of the model. Comparison is also made between the proposed dynamic model and the conventional static model, where improvement in model accuracy is clearly observed. The model identified by the proposed method can be used for optimal controller design for closed-loop FSM systems. The modeling method is also applicable to FSM systems with similar structures.  相似文献   

5.
In this paper, the identification of a class of nonlinear systems which admits input-output maps described by a finite degree Volterra series is considered. In actual fact, it appears that this class can model many important nonlinear multivariable processes not only in engineering, but also in biology, socio-economics, and ecology.To solve this identification problem, we propose a method based on local gradient search in a local parameterization of the state-space realization of finite degree Volterra series with infinite horizon. Using the local parameterization not only reduces the amount of the gradient calculations to the minimal value, but also overcomes the nonuniqueness problem of the optimal solution.Moreover, we propose a sequential projection method to provide an initial estimation of the parameters of finite degree Volterra series realization. This initial estimation is used to initialize the gradient search method.  相似文献   

6.
A systematic procedure is developed for state-space modeling and solving the dynamic behavior of any linearn order constant coefficient distributed-parameter system with two or more independent variables. The state-space model is a set of first-order linear difference equations and is also referred to as a discrete multidimensional state-space model. Transformation of a continuous distributed-parameter system into a discrete state-space model is based on the multidimensional Laplace-bilinear mapping technique. A procedure is outlined for converting the initial and boundary conditions of the system into a set of discrete conditions appropriate for the statespace model. Convergence of the state-space model's solution to the exact solution depends on the sampling rates of the independent variables and the ratio of increments. A few examples when state-space modeling of a distributed-parameter system is useful are: to estimate optimal feedback or optimal feedforward gains in active control applications; model reference optimal-distributed tracking systems; optimal tracking of desired trajectories; realtime system identification.  相似文献   

7.
We analyze the problem of modeling an observed impulse response by means of a finite-dimensional, linear, time-invariant system. Our approach differs from classical realization theory in the following respects. The modeling problem is split in two steps, namely, identification for determining a model for the observations, and realization for determining parameters which describe the model. Systems are considered as sets of time series, not as input-output maps. In particular, the partitioning of variables into inputs and outputs need not be known, and it is not required that there exist a causal relationship between inputs and outputs. Further, we make no assumptions concerning initial conditions, which in particular may be nonzero. Determination of initial conditions is part of the modeling problem. A final significant distinction from classical realization theory is that the systems need not be controllable.We characterize the class of systems which can be identified from impulse response measurements. Necessary and sufficient conditions are formulated in terms of state-space realizations. It turns out that noncontrollable systems are also identifiable. For causal systems, the condition is that the state transition matrix, restricted to the noncontrollable states, has sufficiently small cyclic index. For noncausal systems, the condition is expressed in terms of the rank of the (singular) state evolution equation.  相似文献   

8.
为解决状态空间系统的预报误差与系统参数之间的非线性、非凸性给参数估计带来的困难,提出了状态空间系统的梯度优化辨识方法。分析了基于局部线性化的梯度辨识原理,给出了基于QR分解、奇异值分解(SVD)确定参数搜索方向的实现方案,得到了估计系统参数的迭代辨识算法。探讨了算法的收敛性、给出了算法收敛速度的解析表达式,最后进行了数值仿真,实验结果说明了所提出方法的有效性。  相似文献   

9.
We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.  相似文献   

10.
Hammerstein-Wiener system estimator initialization   总被引:1,自引:0,他引:1  
In nonlinear system identification, the system is often represented as a series of blocks linked together. Such block-oriented models are built with static nonlinear subsystems and linear dynamic systems. This paper deals with the identification of the Hammerstein-Wiener model, which is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output. The proposed identification scheme is iterative and will be demonstrated on measurements. It will be proven that on noiseless data and in absence of modeling errors, the optimization procedure converges to the true system locally.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号