Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.     

基于能量辨识的非线性静电-结构系统的自由度缩减建模方法

给出了一种基于系统能量函数辨识的静电致动微薄板系统自由度缩减建模方法.从Von Karman应变-位移关系式出发,推导出以广义模态坐标为变量的系统动能、应变能以及电容函数的函数表达式.为了将应变能以及电容函数写成广义模态坐标的多变量多项式形式,利用一系列经静态非线性结构有限元计算的结果,拟合得到多变量多项式的未知系数.由Lagrangian方程获得原系统的自由度缩减模型.利用该模型对器件的静/动态特性进行仿真,其计算费用很低.与有限元结果比较,验证了建模方法的正确性.     

Instrumental variables approach to identification of a class of MIMO Wiener systems

A new approach to identification of multi-input multi-output (MIMO) Wiener systems using the instrumental variables method is presented. It is assumed that static nonlinear elements are invertible and their inverse characteristics can be expressed or approximated by polynomials of known orders. It is also assumed that the linear part of the Wiener system can be represented by a matrix polynomial form. Based on these assumptions, the Wiener system is transformed introducing a new parameterization and its parameters are estimated using a linear-in-parameters model. To solve the problem of non-consistency of least squares parameter estimates, an instrumental variables method is employed. A numerical example is included to show the effectiveness and the practical feasibility of the presented approach.     

Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems

This paper focuses on the identification problem of Hammerstein systems with dual-rate sampling. Using the key-term separation principle, we derive a regression identification model with different input updating and output sampling rates. To solve the identification problem of the dual-rate Hammerstein systems with the unmeasurable variables in the information vector, an auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables with their corresponding recursive estimates. Convergence properties of the algorithm are analyzed. Simulation results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.     

Recursive parameter identification of the dynamical models for bilinear state space systems

This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms.     

基于气动力降阶模型的跨音速气动弹性稳定性分析

基于离散型输入输出差分模型,运用非定常CFD方法训练信号,然后运用最小二乘方法进行参数辨识,得到降阶的非定常气动力模型,再将该离散差分模型转换为连续时间域内的状态方程。耦合气动状态方程和结构状态方程,得到耦合系统的气动弹性状态方程。求解不同动压下状态矩阵的特征值,根据根轨迹图分析系统的稳定性特性。分析结果与直接耦合CFD/CSD方法结果相吻合,可以计算跨音速非线性气动弹性问题。其计算效率比直接耦合CFD/CSD方法提高1~2个数量级。针对Isogai wing在跨音速出现的S型颤振边界进行了较为细致的分析,阐述了该现象是由于系统诱发颤振的分支随着速度(来流动压)的提高而发生转移所导致的。     

Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.     

Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

This paper develops a biased compensation recursive least squares-based threshold algorithm for a time-delay rational model. The time-delay rational model is transformed into an augmented model by using the redundant rule, and then, a recursive least squares algorithm is proposed to estimate the parameters of the augmented model. Since the output of the augmented model is correlated with the noise, a biased compensation method is derived to eliminate the bias of the parameter estimates. Furthermore, based on the structures of the augmented model parameter vector and the rational model parameter vector, the unknown time delay can be computed by using a threshold given in prior. A simulated example is used to illustrate the efficiency of the proposed algorithm.     

考虑关节摩擦的空间机器人动力学建模与参数辨识

研究了考虑关节摩擦影响的空间机器人系统的动力学建模与参数辨识问题．采用单向递推组集方法和虚功率原理建立了含有关节摩擦的多体系统动力学方程,推导了关节摩擦对系统动力学方程的贡献,采用基于腕力传感器信号和最小二乘法的辨识方法进行了系统惯性参数的辨识．数值仿真结果验证了数学模型的正确性与辨识方法的有效性．     

Galerkin-Petrov least squares mixed element method for stationary incompressible magnetohydrodynamics

The Galerkin-Petrov least squares method is combined with the mixed finite element method to deal with the stationary, incompressible magnetohydrodynamics system of equations with viscosity. A Galerkin-Petrov least squares mixed finite element format for the stationary incompressible magnetohydrodynamics equations is presented. And the existence and error estimates of its solution are derived. Through this method, the combination among the mixed finite element spaces does not demand the discrete Babuska-Brezzi stability conditions so that the mixed finite element spaces could be chosen arbitrartily and the error estimates with optimal order could be obtained.     

Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model

For the difficulty that the information vector in the identification model contains the unknown variables, we substitute these unknown variables with the outputs of the auxiliary model and then develop an auxiliary model based recursive least squares algorithm, an auxiliary model based least squares iterative (AM-LSI) algorithm, and derive an equivalent matrix decomposition based AM-LSI algorithm for input nonlinear controlled autoregressive systems based on the auxiliary model. The simulation results show that the proposed algorithms can estimate the parameters of a class of input nonlinear systems.     

A texture evolution model in cubic-orthotropic polycrystalline system

A new methodology based on a conservation principle in the orientation space is developed to simulate the texture evolution in a cubic-orthotropic polycrystalline system. A least squares error method was used to improve the accuracy of the simulation results obtained from the texture evolution function. The model is applied to uniaxial tension, compression and rolling for a large deformation of more than 50% using a single evolution parameter. The validity and application range of this new model are discussed by simulating and predicting texture evolution during different loading conditions. The new methodology provides a family of texture evolution paths and streamlines which empowers the materials designer to optimize the desired microstructure.     

Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.

     

Determination of the Parameters of Hydraulic Models of the Vascular Bed by the Input Impedance Method

On the basis of hydraulic models of the vascular bed, the possibility of quantitatively estimating the resistances, inertia coefficients and compliances of model elements, as well as the changes in these parameters, from the hydraulic input impedance is evaluated. It is shown that the input impedance method makes it possible to find stable estimates for the hydraulic model parameters using the least squares method. No significant differences between the parameters of hydraulic models of the vascular bed calculated by the input impedance method and measured independently were found. The possibility of using the input impedance method to estimate the arterial response of an organ is discussed.     

Analytical Solution for Constant-Rate Pumping Test in Fissured Porous Media with Double-Porosity Behaviour

An analytical solution for constant-rate pumping test in fissured porous media is developed using the dual porosity approach with pseudo-steady state exchange between fissures and matrix. It is shown how the solution relates to other pumping well functions. For analyses of pumping tests, type curves are presented that enable to estimate aquifer transmissivity, storage coefficients of the fissures and matrix and conductance for fluid exchange between the fissures and matrix. The analytical solution is also linked to a non-linear least squares optimisation algorithm to automatically fit simulated and observed drawdown and estimate hydraulic properties of the aquifer. The usefulness of the method is demonstrated by a practical application using data described in literature from a pumping test conducted in a confined limestone karst aquifer. Results obtained for the aquifer parameters estimates including 95% confidence intervals demonstrate the practical usefulness of the obtained solution.     

陀螺零偏二次模型参数的系统级辨识算法

以单轴旋转光学捷联惯性导航系统为原型,假设水平陀螺常值漂移的影响得以完全调制,方位陀螺漂移为随时间变化的二次模型,在水平阻尼工作模式下推导了系统位置误差与方位陀螺漂移之间严格的数学关系。分别设置了方位陀螺漂移仅有常值项、一次项、二次项和全系数误差的误差模型,利用递推最小二乘算法成功辨识出设定的二次模型中各个参数值。仿真结果表明,常值项首先被辨识出来,估计时间约为14 h,估计误差为6.54e-6(°)/h;一次项系数估计时间约为30 h,估计误差为2.73e-8(°)/h;二次项系数估计时间约为42 h,估计误差为1.51e-9(°)/h;全系数估计需要45 h,估计误差为7.28e-6(°)/h。辨识结果验证了该算法的正确性。实际系统中,可适当增加总的辨识估计时间,以达到更高精度的辨识结果。     

Parameter Identification and Control of Ground Temperature

A numerical thermal management system for the ground structure is presented. The system consists of two parts, i.e. the identification analysis of thermal conductivity and the thermal control analysis for the ground. The former is carried out by using the nonlinear least squares method and the latter is based on the optimal control theory. The formulations of these methods are presented and they are applied to an actual putting green in a golf course. Reasonable thermal conductivity of the ground is identified by parameter estimation and the ground temperature is theoretically controlled as illustrated by numerical examples.     

基于控制响应的时变系统模态参数辨识的改进子空间方法

提出了一种基于系统控制信号激发的响应数据来辨识时变系统模态参数的改进子空间方法。该方法以系统控制响应信号建立系统的状态空间输出方程并构造了一个广义Hankel矩阵,通过对该矩阵做奇异值分解(SVD),用广义能观阵的估计代替输出矩阵,然后利用奇异值矩阵的正交性,有效地降低了噪声敏感性和计算量,从而容易地辨识出等效状态下的系统矩阵,最后采用转换矩阵辨识出时变系统的模态参数。通过理论分析、仿真和实验,讨论了不同信噪比对辨识结果的影响,验证了该方法的有效性。     

Spectral simulation and shock absorber identification

In vehicle dynamics shock absorbers are used for the optimization of driving comfort and driving safety. Therefore, it is necessary to identify characteristics of shock absorbers under real conditions. This paper introduces the use of hardware-in-the-loop simulations for the identification of shock absorbers involving stochastic models of the road roughness. For this purpose a dynamic hydraulic test stand is used replacing the classical mechanical test stands which allow only sinusoidal excitation. For the Monte Carlo simulation with a real shock absorber in the loop, the random excitation of ground roughness is generated using a modified spectral representation method based on the famous contributions of Shinozuka. Motion and force of the shock absorber are measured and fed back to the Monte Carlo simulation of a car model in real time. The characteristic of the shock absorber is identified using the classical least squares method and a correlation-based method. A piecewise linear model for the characteristic relating the damping force and the velocity of the piston is applied for the shock absorber identification.     

应用POD和Padé拟合的线性动力学系统辨识

提出了一种线性动力学系统辨识方法。应用Padé多项式对系统的动刚度曲线进行拟合,通过最小二乘法确定Padé多项式中的系数矩阵,利用遗传算法确定出Padé拟合式中的参数。通过比较系统动刚度矩阵的理论公式和Padé拟合式,辨识得到系统的质量阵、刚度阵和阻尼阵。当系统阶数较高时,可结合POD降阶技术对系统进行辨识,该方法适用于全阶模型和降阶模型。数值仿真算例表明,本文方法具有较高的精度和较好的鲁棒性。