异步电动机低速时无速度传感器控制的一种新方案

介绍了一种新的简单的用于高性能异步电动机控制的定子电阻估算方法.利用旋转坐标沿定子电流矢量方向定向,可使定子电阻直接从异步电动机的数学模型中获得.通过电机在低速范围的运行,用无速度传感器直接磁场定向控制系统来检测提出的方法.控制系统用了两个观测器:一个是用于估计转子磁链的卡尔曼滤波观测器,另一个是用于辨识速度的模型参考自适应观测器.仿真结果表明了该方法的有效性.     

基于扩展卡尔曼滤波的永磁同步电机无传感器矢量控制

为实现永磁同步电机(PMSM)的无传感器控利,引入扩展卡尔曼滤波对转子位置和转速进行估计,该算法通过测量电机端电压和定子电流在线估计转子位置和速度.为提高PMSM转速估计精度,以两相静止坐标系下考虑转动惯量的PMSM实际模型为递推估计对象,搭建转速、电流双闭环的无传感器矢量控制系统.仿真结果表明,系统状态估计精度较高、运行稳定、超调小、动静态性能良好.     

Observers for induction motor state and parameter estimation

The Kalman filter in its basic form is a state estimator and can be applied to the problem of estimating induction motor rotor currents in a vector control scheme. This filter is shown to combine information from the plant model with output measurements to produce an optimal estimate of the unmeasured states. Also described is the application of the extended Kalman filter algorithm to the online estimation of rotor resistance in an induction motor drive. Significant savings in computing requirements are obtained with a reduced-order model of the motor, in which measured, rather than computed, values of stator currents are used     

异步电机矢量控制系统速度辨识研究

本文针对无速度传感器异步电机矢量控制系统，提出了一个基于卡尔曼滤波器的异步电机速度辨识方法。利用电机机端的定子电压和电流，运用卡尔曼滤波辨识速度。为提高算法的快速法，采用了降低观测器方法，该方法具有较高的精度，仿真结果表明，转速在全速度范围内都有较好的辨识精度。     

Design and implementation of a closed-loop observer and adaptivecontroller for induction motor drives

This paper discusses the implementation and experimental results of a closed-loop rotor flux observer and model reference adaptive system (MRAS) of a direct field-oriented control (FOC) of an induction motor drive. The motor was supplied from a high-frequency (20 kHz) AC resonant link via a MOS-controlled-thyristor (MCT)-based bidirectional converter. Hardware and software implementations of the various motor control functions are presented. The closed-loop observer combines the current and voltage models via a speed-dependent gain (SDG). The current model was formulated to operate in the rotor reference frame and requires only an encoder angle and not the actual rotor speed for implementation. The closed-loop observer permits the use of a pure analog integrator to calculate an adequate stator flux. The use of an AC resonant link further complicated an all-digital calculation of the stator flux. The observer and adaptive controller were tested on a 400-Hz 2-hp induction motor for low and high speeds. The closed-loop observer showed sensitivity at low speeds to the rotor circuit time constant which attributed to the current model rotor flux estimation. At high speed, the closed-loop observer followed the voltage model rotor flux estimation attributes. The MRAS was able to improve the complete speed response by correcting the current model rotor flux observer for errors in estimation of its parameters     

采用扩展卡尔曼滤波磁链观测器的永磁同步电机直接转矩控制

提出利用卡尔曼滤波(extended Kalman filter,EKF)观测器对永磁同步电机(permanent magnet synchronous motor,PMSM)直接转矩控制(direct torque control,DTC)系统进行精确参数估计的方法。通过检测定子电压和电流,应用EKF观测器精确估计电机的定子磁链、电机转速和转子位置,间接估计转矩,近而实现PMSM的无速度传感器控制;同时改进常规的EKF估计状态方程,提高速度估计的精确性,并证明了基于EKF的控制系统的稳定性定理。仿真结果表明该方法减小了系统的非线性、参数变化以及干扰带来的影响,EKF能够准确估计状态变量,提高了直接转矩控制的性能。     

一种基于MRAS的异步电机速度辨识方法

本文提出了一种在静止坐标系下实现的模型参考自适应系统,以辨识异步电机的转速.该方法的参考模型和可调模型分别由电机定子侧和转子侧的方程构成.参考模型中不含定子电阻,并且避免了纯积分运算,因而在宽速度范围内具有较好的鲁棒性.可调模型包含电机转速的信息和一个电流模型转子磁链观测器.当电机转速的估计值收敛于转速的真实值时,观测的转子磁链也收敛于其真实值,可将其用于直接磁场定向控制中.另外,为避免对定子电流的纯微分,本文应用微分跟踪器以提取高质量的微分信号.数值仿真和实验结果验证了该方法的有效性.     

基于自适应观测器的感应电机转速估计方法研究

介绍了一种能在线补偿定转子电阻的基于自适应观测器的感应电机转速估计方法.该转速估计方法只需检测定子电压电流作为输入,利用观测器观测出转子磁链和定子电流,并利用定子电流误差和转子磁链观测值辨识出转速、定子电阻和转子电阻,并反馈于观测器,确保电机转速估计对参数变化的鲁棒性.仿真及实验结果表明,此方法估计的速度能应用于无速度传感器矢量控制系统,并具有较好的辨识精度和鲁棒性.     

Adaptive non-linear speed control of induction motors

In this paper we present two adaptive non-linear speed control algorithms for induction motors. They employ input-output linearization techniques for the motor model in the stator fixed reference frame. The first control algorithm directly tracks speed and rotor flux. The second is designed using torque and rotor flux tracking and is extended for speed control. A key point is that this algorithm ensures exact current limitation in the known parameter case. The adaptation based on Lyapunov design can cope with rotor and stator resistance variations. Significant simulations using sampled controllers are presented emphasizing adaptation and current limitation effects.     

磁通观测改进的无速度传感器DTC系统

针对定子电阻摄动与积分漂移对直接转矩控制(DTC)性能的影响,在分析直接转矩控制特点的基础上,推导了以电机有功功率为基础的定子电阻辨识算法与以反电动势为基础的定子磁链辨识算法,并将坐标系定向于定子电流矢量上推导出转速估计算法.以此建立的直接转矩控制系统,定子电阻辨识算法不依赖电机其他易变参数,转速估计中未使用转子电阻或转子时间常数;通过4kW感应电机无速度传感器DTC系统仿真试验表明,积分漂移得到有效抑制,估计速度与实际速度良好吻合,系统具有较好调速性能.     

Nonlinear adaptive state-feedback speed control of a voltage-fed induction motor with varying parameters

An adaptive nonlinear-state-feedback speed control scheme of a voltage-fed induction motor has been developed in which the control of torque and flux is decoupled. The inputs to the control algorithm are the reference speed, the reference flux, the measured stator currents, the measured rotor speed, the estimated rotor flux, and estimates of the rotor resistance, stator resistance, and load torque, which may vary during operation. The controller outputs are the reference stator voltages in rotor-flux rotating reference frame. An accurate knowledge of the rotor flux and machine parameters is the key factor in obtaining a high-performance and high-efficiency induction-motor drive. The rotor flux is estimated using the induction-motor rotor-circuit model. Although the estimated rotor flux is insensitive to the stator-resistance variation, it does depend on the rotor resistance. A stable model reference adaptive system (MRAS) rotor-resistance estimator insensitive to stator-resistance variation has been designed. Stable stator-resistance and load-torque MRAS estimators have also been developed. These estimators have been developed to constitute a multi-input-multi-output (MIMO) decoupled-cascade structure control system. This simplifies the design problem of the estimators for a stable operation from a MIMO design problem to a single-input-single-output (SISO) design problem. The continuous adaptive update of the machine parameters and load torque ensures accurate flux estimation and high-performance operation. Simulation and experimental results are presented to verify the stability of the induction-motor drive in various operating modes.     

基于多项式磁饱和模型及EKF的感应电机磁链观测

感应电机的转子磁链通常用定子电流进行观测,易受测量噪声影响,且观测模型精度与电机参数有关。当电机内磁场幅值变化时,磁路非线性饱和效应会引起互感参数波动,导致观测磁链的幅值和方向偏离实际值,使控制出现偏差。对此,提出基于四阶多项式磁饱和模型以及扩展卡尔曼滤波(EKF)的全状态估计算法,以定子电流作为反馈,观测转子磁链的同时,辨识互感参数。仿真及实验结果表明所提算法能降低磁饱和非线性对控制系统的影响。     

基于扩展卡尔曼滤波器的永磁同步电机转速和磁链观测器

为了取消永磁同步电机控制中的机械传感器，获得直接转矩控制中需要的电机磁链信息，设计了一种基于扩展卡尔曼滤波器的永磁同步电机转速和磁链估算方法。选取定子固定坐标系下定子磁链、电机转速和转子位置为状态变量，电压和电流作为输入、输出量，建立估算定子磁链、电机转速和转子位置的EKF滤波器系统。采用空间矢量调制的直接转矩控制策略，有效减小了直接转矩控制方法的转矩脉动，并保持了功率器件恒定的开关频率。实验结果表明EKF准确地观测了电机转速和磁链，所构建的无速度传感器DTC控制系统具有良好的转速和转矩控制性能。     

基于遗传算法优化的定子磁链扩展卡尔曼估计方法

定子磁链估计的精度直接关系到异步电机直接转矩控制(DTC)的控制效果,本文提出了一种基于扩展卡尔曼滤波器(EKF)的定子磁链观测方法,该方法选取DTC反馈通道中多个主要参数作为状态变量进行联合滤波估计,为保证EKF算法滤波参数的准确性,采用遗传算法(GA)对EKF中的系统噪声矩阵和测量噪声矩阵进行了优化处理.通过使用该方法设计的磁链观测器与DTC构成闭环系统进行仿真实验表明,滤波参数优化后的EKF算法更加有效地提高了磁链估计精度,从而提高DTC系统的低速控制性能.     

基于扩展卡尔曼滤波器的感应电机转子断条故障检测

根据扩展卡尔曼滤波器的原理,推导出用感应电机端电压、线电流信号估计电机转速的方法,还对扩展卡尔曼滤波器的参数进行了优化,提高了辨识的精度和收敛的速度.对电机转速进行FFT频谱分析,并以此为基础进行电机转子断条故障的在线检测.该方法不仅较以往电流检测方法更有利于克服边频带效应带来的影响,同时也避免了精密的转速测量装置的使用,因此具有广泛的实用价值.实测结果表明该方法是正确有效的.     

双UKF算法及其在感应电机矢量控制中的应用

在无轨迹卡尔曼滤波器(UKF)算法的基础上,建立应用于感应电机矢量控制系统的双UKF算法,实现电机状态和参数的同时观测.电机模型选择以定、转子磁链为状态变量的降阶方程,从而有效避免了数值计算的不稳定性.利用Simulink建立感应电机矢量控制系统,通过仿真比较了双UKF与扩展卡尔曼滤波器(EKF)两种算法的性能.实验结果表明,双UKF算法能有效提高状态估计和参数辨识的精度和收敛速度.     

MRAS-based sensorless control of a vector controlled five-phase induction motor drive

Multi-phase ac motor drives are nowadays considered for various applications, due to numerous advantages that they offer when compared to their three-phase counterparts. In principle, control methods for multi-phase machines are the same as for three-phase machines. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Vector controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux waves in the stator or in the rotor. Open-loop estimators and closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an open-loop and model reference adaptive system (MRAS)-based sensorless control of vector controlled five-phase induction machine with current control in the stationary reference frame. The MRAS-based sensorless operation of a three-phase induction machine is well established and the same principle is extended in this paper for an IRFOC five-phase induction machine. Performance, obtainable with hysteresis current control, is illustrated for a number of operating conditions on the basis of simulation results. Full decoupling of rotor flux control and torque control is realised. Dynamics, achievable with a five-phase vector controlled induction machine, are shown to be essentially identical to those obtainable with a three-phase induction machine. Experimental verification is also provided.     

Robust direct torque and flux control of adjustable speed sensorless induction machine drive based on space vector modulation using a PI predictive controller

A Direct torque and flux control design for a sensorless induction motor, following a Lyapunov-based stator flux observer is presented. In this control scheme, the torque error signal and the amplitude of the stator flux reference vector are delivered to a PI predictive controller. The predictive controller also uses information on the amplitude and position of the actual stator flux vector and measured stator currents to determine the voltage command vector for space vector modulation inverter. In addition, a conventional PI speed controller is used to generate the torque reference signal. Using the fifth order model of the three-phase induction machines in a stationary two axes reference frame, a nonlinear stator flux observer is developed in order to estimate the rotor speed, rotor and stator resistances simultaneously. The stability of this observer is proved by Lyapunov theory. It is shown that if the persistancy of excitation condition is satisfied, the estimated quantities converge to their real values. The effectiveness of the proposed control scheme is verified by computer simulation and experimental results.     

Speed sensorless torque control of an IPMSM drive with online stator resistance estimation using reduced order EKF

Speed sensorless control of an interior permanent magnet synchronous motor (IPMSM) based on direct torque control (DTC) is proposed in this paper. The rotor speed and position of the IPMSM are estimated based on an active flux concept, where, the active flux vector position is identical to the rotor position. The proposed algorithm does not require neither high frequency injection signal nor complicated schemes even at vary low speed operation. Torque/ flux sliding mode controller (SMC) combined with space vector modulation is proposed to improve the performance of the classical DTC. Stator resistance value is required for a stator flux and electromagnetic torque estimation. Its variation due to temperature or frequency degrades the scheme performance, especially, at low speed operation. To overcome this problem, a reduced order extended Kalman filter (EKF) is proposed to update online the stator resistance. The advantages of the direct torque control, sliding mode controller, and speed sensorless control are incorporated in the proposed scheme. Simulation works are carried out to show the ability of the proposed scheme at different operating conditions. The results demonstrate the activity of the scheme at wide range speed operation with load disturbance and parameters variation.     

一种改进的感应电机离散化模型

研究了一种基于不同坐标系下的离散化模型。根据电机静止坐标系下的定子方程与转子坐标系下的转子方程,以定子磁链和转子磁链为状态变量,设计离散化矩阵实现磁链信息的估计。混合离散模型的稳定性可由离散域稳定性定理保证,离散模型的极点轨迹存在于稳定区域内,实现了全速度区域内的模型稳定。最后通过系统仿真,验证了离散模型的正确性。