辨识Box-Jenkins模型参数的递推广义增广最小二乘法

本文提出一种递推广义增广最小二乘辨识算法(RGELS)。该算法推广了递推增广最小二乘法(RELS),可用于一般模型(BOX-Jenkins模型)的参数辨识。数字仿真例子表明了算法的有效性。     

最小二乘法参数辨识及其微机实现

最小二乘法是系统参数辨识中最基本、最成熟的方法,在微机上实现最小二乘法辨识参数,使参数辨识和建模得到了从理论到实际应用的飞跃。     

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。     

遗传算法在T-S模糊模型参数辨识中的应用

介绍了T-S模糊模型的建模过程,在现有T-S模糊模型参数辨识方法的基础上,提出了一种先应用最小二乘法对结论参数进行粗略辨识,以确定参数的大致范围之后,再应用遗传算法对前提参数和结论参数同时优化的参数辨识方法。通过MATLAB对本算法进行了仿真,并对非线性函数进行了逼近实验,所取得的结果令人满意。     

飞行器气动力参数辨识

基于飞行器法向过载方程,首先采用逐步回归分析方法对气动力系数的多元线性回归模型进行因子筛选,确定气动模型结构,然后用逐步回归分析法的结果作为初值,用实时递推最小二乘法估计气动力参数。     

遗传算法在模糊模型参数辨识中的应用

介绍了T-S模糊模型的建模过程,在现有T-S模糊模型参数辨识方法的基础上,提出了一种先应用最小二乘法对结论参数进行粗略辨识,以确定参数的大致范围之后,再应用遗传算法对前提参数和结论参数同时优化的参数辨识方法,通过MATLAB对本算法进行了仿真,并对非线性函数进行了逼近实验,所取得的结果令人满意。     

基于MATLAB的最小二乘法参数辨识与仿真

本文介绍了基于MATLAB/Simulink的使用最小二乘法进行参数辨识的设计与仿真方法.首先简述参数辨识的概念和最小二乘法的基本原理,然后介绍如何采用Simulink建立系统的仿真对象模型和运用MATLAB的M语言编写最小二乘递推算法,最后结合实例给出相应的仿真结果和分析.本文的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.     

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.     

基于T-S模糊模型的辨识算法

提出一种新的基于T－S模糊模型的辨识算法。该算法可分为2步,第1步是比较粗糙的辨识,按子空间的线性程度来划分输入空间,规则前件参数由于空间的中心和大小决定,规则后件线性参数由最小二乘法确定2步是模的精细调整,利用梯度下降法调节隶属函数和规则后件的线性参数,仿真实验说明了该算法的有效性。     

French-DeGroot社会网络模型的结构辨识与参数估计

近年来社会网络的研究受到越来越多的关注.本文研究基于French-DeGroot模型的社会网络参数和结构辨识问题,通过网络中个体所持的观点来判断个体间是否存在影响关系、进一步估计个体之间影响的大小.具体而言:假设网络存在固执个体(stubborn agents)和非固执个体(non-stubborn agents)两类,当固执个体的观点为零均值独立同分布随机变量序列时,利用最小二乘算法估计网络未知参数,证明了估计的强一致性并给出收敛速度;进一步,构造结构辨识算法判断个体间是否存在影响关系,证明了结构辨识算法的有限时间收敛性.最后给出仿真例子验证算法的有效性.     

基于LabVIEW的加速度计模型参数辨识模块设计

加速度计的模型参数辨识对研究加速度计动态特性具有重要作用.针对加速度计动态特性测试系统中加速度计模型参数辨识模块的设计,利用LabVIEW图形化编程的特点,以最小二乘原理为基础,通过对加速度计频域响应函数的拟合实现了加速度计模型参数的辨识,并构建了可视化软面板.实验测试表明:所设计模块能够实现加速度计模型参数辨识,有较高的参数辨识精度,并具有良好的人机交互,所辨识出的模型能够描述加速度计的动态特性.     

动态称重系统的建模及其参数估计

阐述了为了兼顾动态称重系统的快速性和精度,将动态称重作为一个基于最小二乘法的参数估计和预测问题来处理,即从建立数学模型和信号处理算法方面加以解决。然后,将动态称重系统等效为二阶系统,分析得出了系统为时变非线性系统,推导出了系统的动态数学模型,并且,根据系统模型,将问题转化为参数辨识问题。辨识算法上,采用了基于Householder变换的自适应最小二乘法,其具有抗方程病态性好、稳定性好、估计精度高、计算量小、跟踪性好等优点。试验结果证明:所提出方法是可行的,达到了试验提出的技术要求,测量相对误差小于0.25%FS,系统在全量程范围内的准确度不低于0.25%。在提高称重速度的同时,也保证了系统的测量准确性,对于此类系统的实用化开发具有很好的参考价值。     

On parameter convergence in least squares identification and adaptive control

Least squares estimation is appealing in performance and robustness improvements of adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to achieve parameter convergence in least squares estimation. This paper proposes a least squares identification and adaptive control strategy to achieve parameter convergence without the PE condition. A modified modeling error that utilizes online historical data together with instant data is constructed as additional feedback to update parameter estimates, and an integral transformation is introduced to avoid the time derivation of plant states in the modified modeling error. On the basis of these results, a regressor filtering–free least squares estimation law is proposed to guarantee exponential parameter convergence by an interval excitation condition, which is much weaker than the PE condition. And then, an identification‐based indirect adaptive control law is proposed to establish exponential stability of the closed‐loop system under the interval excitation condition. Illustrative results considering both identification and control problems have verified the effectiveness and superiority of the proposed approach.     

Performance analysis of the auxiliary model-based least-squares identification algorithm for one-step state-delay systems

Based on the input–output representation of one-step state-delay systems, we use the auxiliary model-based recursive least-squares algorithm to estimate the parameters of the systems and study the convergence of the proposed algorithm by using the stochastic process theory. A simulation example is provided.     

UD分解与偏差补偿结合用于变量带误差模型辨识

本文提出一种基于UD(upper-diagonal)分解与偏差补偿结合的辨识方法,用于变量带误差(errors-in-variables,EIV)模型辨识.考虑单输入单输出(single input and single output,SISO)线性动态系统,当输入和输出含有零均值、方差未知的高斯测量白噪声时,该类系统的模型参数估计是一种典型的EIV模型辨识问题.为了获得这种EIV模型参数的无偏估计,本文先推导出最小二乘模型参数估计偏差量与输入输出噪声方差以及最小二乘损失函数与输入输出噪声方差的关系,然后采用UD分解方法递推获得模型参数估计值,再利用输入输出噪声方差估计值补偿模型参数估计偏差,以此获得模型参数的无偏估计.本文还讨论了算法实现过程中遇到的一些问题及修补方法,并通过仿真例验证了所提辨识方法的有效性.     

Design and analysis of a pneumatic high-impact force drive mechanism for in-pipe inspection robots

A pneumatic actuator is suitable and safe for in-pipe inspection robots in inflammable circumstances because of its ability to withstand explosions. However, ordinary pneumatic actuators limit driving speed because of their slow response. In this paper, we propose a novel pneumatic drive mechanism that can produce a high-impact force to move the in-pipe robot forward with sufficient speed by a catastrophic phenomenon using rapid release between a magnet and springs. We also introduce an anisotropic friction mechanism that uses a self-locking phenomenon to transmit the impact force to the pipe walls efficiently. A pin retraction mechanism that releases the self-locking condition is applied to retrieve the robot from the pipes. Optimizations of the proposed design were conducted based on a motion simulation model and verified in experiments. The experimental results obtained for maximum driving speeds in a straight pipe with different material types were approximately 90 and 50 mm/s for horizontal and vertical pipes, respectively. Stable strokes were also observed at different driving frequencies from 0.5 to 2.0 Hz.     

Direct and indirect least squares methods in continuous-time parameter estimation

The discrete-time least squares approach is extended to the estimation of parameters in continuous nonlinear models. The resulting direct integral least squares (DILS) method is both simple and numerically efficient and it usually improves the mean-squared error of the estimates compared with the conventional indirect least squares (ILS) method. The biasedness of the DILS estimates may become serious if the sample points are widely spaced in time and/or the signal-to-noise ratio is low and so a continuous-time symmetric bootstrap (SB) estimator which removes this problem is described. The DILS, SB and ILS methods form a three-stage procedure combining the robustness and numerical efficiency of direct methods with the asymptotic unbiasedness of ILS procedures.     

Least-squares-based iterative identification algorithm for Hammerstein nonlinear systems with non-uniform sampling

This paper focuses on identification problems for Hammerstein systems with non-uniform sampling. By using the over-parameterization technique, we derive a linear regressive identification model with different input updating rates. To solve the identification problem of Hammerstein output error systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. The performances of the proposed algorithm are analysed and compared by using a numerical example.