Recursive identification of induction motor parameters

The use of linear parameter estimation techniques to determine the rotor resistance, self-inductance of the rotor winding, as well as the stator leakage inductance of a three-phase induction machine is investigated in this paper. In order to obtain results with maximum accuracy, some specific procedures to reduce the effect of the operating conditions on the quality of the estimates are investigated. For analytical identification, a model is developed from the steady-state equations of induction motor dynamics. The identification procedure, based on a simple algorithm derived from least squares techniques, uses only the information of stator currents and voltages and rotor angular speed as input–output data. The computer simulation as well as the experimental results are used to anchor the main conclusions issued from this study and to demonstrate the practical use of the identification method.     

基于MRAS的感应电动机无速度传感器控制方法

针对传统的感应电动机转速辨识算法只对电动机转速进行辨识,而没有考虑定子电阻的变化对电动机转速辨识所带来影响的问题,提出了一种改进的基于MARS的感应电动机无速度传感器控制方法。该方法采用电压模型的输出作为转子磁链、定子电阻的期望值,电流模型的输出作为转子磁链、定子电阻的推算值,根据MARS理论,以电压模型作为参考模型、电流模型作为自适应可调模型,进行电动机转速、定子电阻的辨识。仿真结果表明,该方法能够同时辨识电动机转速和定子电阻,有效消除了定子电阻发生变化对电动机转速辨识带来的影响,提高了感应电动机控制系统的低速辨识性能。     

感应电机二阶滑模次优算法定子磁链观测器设计

提出了基于二阶滑模次优算法的感应电机定子磁链观测方法,设计了定子磁链观测器,并应用到感应电机直接转矩控制中.本文设计的磁链观测器,通过准确的跟踪电流及其变化率,从而实现对转子磁链的准确估算,然后利用转子磁链与定子磁链的关系,估算出定子磁链.由于本文设计的定子磁链观测器是一个多输入多输出(MIMO)系统,稳定性分析非常复杂,为此将磁链估算误差的微分看作扰动处理,从而将MIMO的观测器模型分解成两个独立的单输入单输出(SISO)系统,简化了稳定性分析.将该观测器用于感应电机直接转矩控制中,达到了很好的控制效果.仿真和实验验证了该方法的有效性.     

PARAMETRIC IDENTIFICATION OF LINEAR NOISY INPUT-OUTPUT SYSTEMS

An efficient method is proposed for identification of linear noisy input-output systems. Central to this method is that the variances of the input and output noises, which determine the bias in the ordinary least-squares (LS) estimator, are estimated in the way of increasing the degrees of both the denominator and the numerator of the system transfer function by one, but with no need to evaluate the average LS errors. While achieving estimation unbiasedness, the proposed method exhibits algorithmic advantages over the LS-based algorithms recently developed. Performance comparisons with other existing estimation algorithms based upon computer simulations are given.     

FUZZIFYING THE “PRECISE”

Based on the fuzzy-integral model, methods and algorithms are developed for identifying the “input–output” operator of continuous and stationary discrete extremal fuzzy dynamic systems (EFDS). The EFDS “input–output” operator is restored by means of experimental data with possibilistic uncertainty, the source of which is extremal fuzzy time intervals. The regularization conditions for obtaining quasi-optimal estimates are substantiated by the proved theorems. The corresponding algorithms are provided. The results obtained are illustrated by examples in the case of a finite set of EFDS states.     

Improved parameter estimates for non-linear dynamical models using a bootstrap method

It is known that the least-squares (LS) class of algorithms produce unbiased estimates providing certain assumptions are met. There are many practical problems, however, where the required assumptions are violated. Typical examples include non-linear dynamical system identification problems, where the input and output observations are affected by measurement uncertainty and possibly correlated noise. This will result in biased LS estimates and the identified model will exhibit poor generalisation properties. Model estimation for this type of error-in-variables problem is investigated in this study, and a new identification scheme based on a bootstrap algorithm is proposed to improve the model estimates for non-linear dynamical system identification.     

Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model

This paper focuses on the parameter estimation problems of output error autoregressive systems and output error autoregressive moving average systems (i.e., the Box–Jenkins systems). Two recursive least squares parameter estimation algorithms are proposed by using the data filtering technique and the auxiliary model identification idea. The key is to use a linear filter to filter the input–output data. The proposed algorithms can identify the parameters of the system models and the noise models interactively and can generate more accurate parameter estimates than the auxiliary model based recursive least squares algorithms. Two examples are given to test the proposed algorithms.     

A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems

In this paper, we propose a novel identification algorithm for a class of dual-rate sampled-data systems whose input–output data are measured by two different sampling rates. A polynomial transformation technique is employed to derive a mathematical model for such dual-rate systems. The proposed modified stochastic gradient algorithm has faster convergence rate than stochastic gradient algorithms for parameter identification using the dual-rate input–output data. Convergence properties of the algorithm are analyzed. Finally, illustrative and comparison examples are provided to verify the effectiveness and performance improvement of the proposed method.     

矩阵变换器驱动异步电机的直接转矩控制

通过对异步电机转矩角动态控制过程的分析,预测出下一控制周期电机定子磁链的期望电压矢量,以此作为矩阵变换器的输出参考电压欠量,实现了采用矩阵变换器供电的异步电机直接转距控制.利用Matlab/Simulink构建了系统模型,给出了仿真结果.结果表明,该系统不仅具有矩阵变换器的理想输入输出特性,而且具有直接转矩控制优良的动静态性能.     

Gradient based and least-squares based iterative identification methods for OE and OEMA systems

Gradient based and least-squares based iterative identification algorithms are developed for output error (OE) and output error moving average (OEMA) systems. Compared with recursive approaches, the proposed iterative algorithms use all the measured input–output data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. The basic idea of the iterative methods is to adopt the interactive estimation theory: the parameter estimates relying on unknown variables are computed by using the estimates of these unknown variables which are obtained from the preceding parameter estimates. The simulation results confirm theoretical findings.     

六相感应电动机定子间互感电压研究

对六相感应电动机通入梯形波相电流时的定子间瞬时自感、互感系数及互感电压进行了理论计算和有限元分析。计算及分析结果表明:定子侧通入梯形波的六相感应电动机可以模拟直流电动机控制模式;转矩电流对定子每相的感应电压影响非常小,验证了建模与仿真时忽略定子转矩电流和转子感应电流产生的磁效应是合理的。     

一种基于多分辨分析的脉冲响应辨识方法

提出了一种基于正交基展开方法的脉冲响应辨识方法--正交尺度变换法.这种 方法以正交尺度函数展开的形式表示脉冲响应函数,对信号进行正交尺度变换,并使用最小 二乘类参数辨识方法估计参数.仿真结果验证了理论的正确性和方法的可行性.     

The Structural Robustness of the Induction Motor Stator Currents Subsystem

Stator‐currents control is essential for several high‐performance induction motor control schemes such as field oriented control. There are numerous reports dealing with sophisticated control schemes for this subsystem. However, classical linear controllers remain widely used due to their experimental success and simplicity. Considering that the induction motor stator currents subsystem is normally represented by a fifth order non‐linear multivariable model, it is remarkable that simple fixed linear controllers, such as typical proportional integral schemes, are able to provide adequate robustness and performance in practice. In fact, it is normally assumed that this subsystem is “easy” to control, and the difficulties are mostly technical. Moreover, it is common practice to consider a stable first order linear single input single output (SISO) system as a design model. On the other hand, it is widely known that stable and minimum phase uncertain SISO systems are also “easy” to control. In this article it is formally demonstrated that the stator currents subsystem of the induction motor is the multivariable equivalent of such SISO systems. That is, it is formally demonstrated that this process is “easy” to control. This result may assist with better induction motor control and may serve as an example of the evaluation of similar multivariable systems. Real time experimental results are included.     

Dynamic feedback linearization of the induction motor

Dynamic state feedback is used to achieve feedback linearization of the induction motor. It is shown that the sixth-order nonlinear dynamical model of the induction motor consisting of rotor speed, two stator currents, two rotor fluxes and an integrator in the feedback controller can be made equivalent to two third-order decoupled linear systems by nonlinear state feedback through the stator input voltages. It is further shown that a nonsingular dynamic feedback linearizing transformation exists as long as the (electromagnetic) torque put out by the motor is nonzero     

Fixed-order FIR approximation of linear systems from quantized input and output data

The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input–output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example.     

加速度计几种模型辨识方法的比较研究

加速度计离心试验中,为了更精确的得到加速度计的模型系数,比较研究了3种辨识方法:最小二乘方法(加权最小二乘)、总体最小二乘方法和EV模型方法.通过仿真得出在输出噪声和输入噪声为白噪声或者近似白噪声且离心机精度优于1×10-5的情况下,最小二乘与其他2种辨识方法辨识精度相当.最后通过试验对比了最小二乘方法与加权最小二乘方...     

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37?kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.     

Linearisation via input–output injection of time delay systems

This paper deals with the problem of linearisation of systems with constant commensurable delays by input–output injection using algebraic control tools based on the theory of non-commutative rings. Solutions for the problem of linearisation free of delays, and with delays of an observable nonlinear time-delay systems are presented based on the analysis of the input–output equation. These results are achieved by means of constructive algorithms that use the nth derivative of the output expressed in terms of the state-space variables instead of the explicit computation of the input–output representation of the system. Necessary and sufficient conditions are established in both cases by means of an invertible change of coordinates.     

Robust identification of MISO neuro‐fractional‐order Hammerstein systems

This paper introduces a multiple‐input–single‐output (MISO) neuro‐fractional‐order Hammerstein (NFH) model with a Lyapunov‐based identification method, which is robust in the presence of outliers. The proposed model is composed of a multiple‐input–multiple‐output radial basis function neural network in series with a MISO linear fractional‐order system. The state‐space matrices of the NFH are identified in the time domain via the Lyapunov stability theory using input‐output data acquired from the system. In this regard, the need for the system state variables is eliminated by introducing the auxiliary input‐output filtered signals into the identification laws. Moreover, since practical measurement data may contain outliers, which degrade performance of the identification methods (eg, least‐square–based methods), a Gaussian Lyapunov function is proposed, which is rather insensitive to outliers compared with commonly used quadratic Lyapunov function. In addition, stability and convergence analysis of the presented method is provided. Comparative example verifies superior performance of the proposed method as compared with the algorithm based on the quadratic Lyapunov function and a recently developed input‐output regression‐based robust identification algorithm.     

基于感应电机状态方程的定子电阻辨识方法的研究

基于感应电机两相同步旋转坐标系状态方程,提出了一种新的定子电阻辨识方案。在分别利用励磁电流微分方程和转矩电流微分方程推算转速的基础上,利用二者的等量关系实现了定子电阻在线辨识。该方案的优点是,定子电阻估算仅需要电机终端可测的定子电压、电流,无须借助转子或定子磁链。仿真实验将其应用于无速度闭环矢量调速系统,实时更新转速估算模块中的定子电阻,论证了该方案对电机参数变化鲁棒性强及对改善转速估算精度的有效性。