一种基于锁相环原理的参考模型自适应感应电机转速估计方法

针对超低速及零定子频率运行条件下感应电动机转速的不可观测性导致基于电机模型的传统速度估计方案无法实现速度估计,引入了高频信号注入法来获得转子磁链矢量位置角并得到转子磁链的参考模型,并以转子磁链的电流模型作为调节模型,在此基础上,提出了基于锁相环原理的参考模型自适应速度估计方案．仿真结果进一步验证了该方案的有效性．     

Grid-connected vector-controlled slip-ring induction machine drive with out speed sensor

A speed estimation method is presented in this paper for a grid-connected doubly-fed slip-ring induction machine drive. The proposed method is formulated with reactive power based model reference adaptive system (MRAS). The method does not require the estimation of stator/rotor flux. So, the integrator related problems at synchronous speed are overcome. Also, the estimation method is independent of stator and rotor resistance variation. Extensive simulation results are presented to validate the technique.     

改进型MRAS无速度传感器的无轴承异步电机矢量控制

无轴承异步电机(BIM)的转子磁链电压模型中含有纯积分环节,其积分初值和累计误差会影响磁链观测精度,进而使转速估计产生严重失真.为了实现BIM无速度传感器运行,本文借鉴模型参考自适应法(MRAS)基本结构,将改进二阶广义积分器与锁频环结合以代替原有纯积分器,提出了一种新的基于MRAS的BIM无速度传感器控制方法,构建了BIM转子磁链定向无速度传感器矢量控制系统.并且,基于MATLAB/Simulink的仿真验证和基于dSPACE的实验结果表明:与传统电压模型观测方法相比,所提出的转子磁链电压模型有效避免了纯积分环节带来的直流偏移和积分初值影响,有着更好的观测效果.同时,基于无轴承异步电机转子磁链定向无速度传感器矢量控制系统,电机能稳定悬浮运行,估算转速和实测转速具有很好的一致性.     

A new scheme for sensorless induction motor control drives operating in low speed region

A novel simple stator resistance estimation technique for high-performance induction motor drives is proposed. It makes use of a synchronously revolving reference frame aligned with the stator current vector, so that the resistance can be straightforwardly derived from the mathematical model of the induction motor. A sensorless direct field orientation scheme is employed to validate the proposed solution, with the drive operating in the critical area of low speeds. A combination of two observers is used: a Kalman filter observer to estimate the rotor flux, and a MRAS observer for speed estimation. The stator resistance estimator alleviates the usual performance degradation of MRAS-based drives at low speeds, caused by the thermal drift of stator resistance. Computer simulations, including realistic disturbances, show high effectiveness of the described approach.     

改进的MRAS无速度传感器VC控制系统仿真研究

针对交流传动系统中异步电机的精确控制和速度辨识等问题,在Simulink软件环境中,对基于模型参考自适应系统(MRAS)无速度传感器的异步电机的矢量控制(VC)系统进行了研究。系统采用按转子磁场定向的VC对异步电机进行控制,通过MRAS辨识算法估算电机转速,由Popov超稳定定理对磁链偏差进行收敛。由于速度辨识算法中电压模型的纯积分环节会引起误差积累和漂移问题,故采用改进积分型转子磁链估算模型来解决这一问题。仿真结果表明,速度辨识方法能够准确推算出电机转速,控制系统动态响应快、稳态静差小、抗负载扰动能力强,具有良好的动静态控制性能。     

Backstepping control with speed estimation of PMSM based on MRAS

This paper presents a backstepping control method with speed estimation of permanent magnet synchronous motor (PMSM) based on model reference adaptive system (MRAS). First, a comprehensive dynamical model of PMSM in d–q axis and its space state equations are established. Next, using Lyapunov stability theorem, based on the backstepping control theory, the PMSM rotor speed and current backstepping controllers are designed. Furthermore, using Popov stability theory, based on MRAS, the PMSM rotor speed observer is designed. Finally, Matlab/Simulink simulation results show that the backstepping control and speed observer are effective and feasible.     

永磁同步电机系统的无速度传感器研究

为了提高永磁同步电机系统的抗干扰能力,提出一种无速度传感器方法,用于速度辨识.将滑模(SM)变结构控制与模型参考自适应系统(MRAS)方法相结合,选取电机本体作为参考模型,利用逆变器输出的电压和电流,构建基于磁链方程的可调模型,利用两模型误差运用SM变结构方法辨识速度.在Matlab仿真平台对无速度传感器方法进行了分析,研究结果表明:所提出的无速度传感器方法具有较好的动静态性能,可以实现对速度的准确辨识.     

无速度传感器矢量控制系统的一种转速辨识新方法

在无速度传感器交流调速系统中,根据异步电动机的数学模型,利用易于检测的定子电压和电流,可对异步电动机的转速进行辨识。文章基于模型参考自适应系统(MRAS)分析了几种转速辨识的方法,针对这几种方法在参考模型中包含了一个积分环节、并受到定子电阻热敏变化和定子瞬态电感影响的缺点,提出了一种新型的转速辨识方法。该方法采用的参考模型避免了纯积分运算,并且不含定子电阻和定子瞬态电感,因而在宽速度范围内具有较好的鲁棒性。计算机仿真结果证明了该方法的有效性。     

基于MRAS异步电机转子电阻辨识

在异步电机的矢量控制系统中,转子电阻的变化严重影响着磁链的估算,制约着整个控制系统的运行性能.实时地对转子电阻进行辨识,成为提高整个系统性能的保证.基于模型参考自适应理论,提出一种新的转子电阻辨识算法,利用李雅普洛夫方法证明系统的稳定性.仿真结果表明:该估算算法能准确地辨识转子电阻,并且具有一定的鲁棒性.     

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。     

采用MRAS速度观测器的异步电机无电压传感器DTC研究

为提高系统的抗干扰能力,降低运行成本,提出无电压传感器控制策略,通过逆变器的导通状态和直流侧电压估算三相定子电压,采用模型参考自适应系统（ MRAS）方法设计速度观测器,基于异步电机数学模型得到转子磁链的两种模型,根据合适的自适应机构得到精确的实际转速,实时调节模型参数,该控制方法简单,保持较高的控制精度。最后通过实验验证了控制策略的可行性。     

Simulation studies on model reference adaptive controller based speed estimation technique for the vector controlled permanent magnet synchronous motor drive

This paper presents a speed estimation technique for the permanent magnet synchronous motor drive. A Model Reference Adaptive System (MRAS) has been formed using the instantaneous and steady-state reactive powers to estimate the speed. It has been shown that such unique MRAS offers several desirable features. The proposed technique is completely independent of stator resistance and is less parameter sensitive, as the estimation-algorithm is only dependent on q-axis stator inductance. Also, the method requires less computational effort as the simplified expressions are used in the MRAS. The stability of the proposed system is achieved through Popov’s Hyperstability criteria. Extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds including zero speed and a very satisfactory performance has been achieved.     

基于定转子电阻误差补偿的感应电动机自适应逆解耦控制研究

对于具有多变量、非线性、强耦合、慢时变等特征的异步电动机调速系统, 实现定子磁链与电磁转矩的高精度动态解耦是提高系统性能的关键. 首先通过非线性状态反馈建立感应电动机的积分逆模型, 并在此基础上提出了一个基于定、转子电阻误差补偿的感应电动机自适应逆解耦控制方法, 将补偿后的积分逆模型串联到对象的输入端建立广义被控对象. 复杂的感应电动机调速系统被解耦成电磁转矩与定子磁链的两个独立回路, 利用线性系统理论分别对独立回路进行综合设计, 实现定子磁链和电磁转矩对各自给定值的渐近跟踪. 利用Matlab进行了仿真实验, 实验结果验证了建议方案的有效性和可行性.     

基于DSP的模型参考自适应无速度传感器矢量控制

本文讨论了一种具有较好观测精度和鲁棒性的使用PI自适应的模型参考自适应的转子磁通、速度观测器的实现．构造一个直接转子磁场定向无速度传感器感应电机矢量控制系统，并给出了仿真和基于DSPF240的     

A New Full‐Order Sliding Mode Observer Based Rotor Speed and Stator Resistance Estimation for Sensorless Vector Controlled Pmsm Drives

Sensorless control of a permanent magnetsynchronous motor (PMSM) at low speed remains a challenging task. In this paper, a sensorless vector control of PMSM using a new structure of a sliding mode observer (SMO) is proposed. To remove the mechanical sensors, a full‐order (FO‐SMO) is built to estimate the rotor position and speed of PMSM drives. The FO‐SMO, which replaces a sign function by a sigmoid function, can reduce the chattering phenomenon. In order to overcome time delay, we cancel the low pass filter. This sensorless speed control shows great sensitivity to stator resistance and system noise. To improve the robustness of sensorless vector control, a full‐order SMO technique has been used for stator resistance estimation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. The validity of the proposed FO‐SMO with a 1.1 kw low‐speed PMSM sensorless vector control is demonstrated by experiments. In this paper, experimental results for FO‐SMO, back‐EMF SMO and MRAS techniques were obtained with fixed point DSP‐based (TMS320F240).     

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.     

基于神经网络可调模型的直接转矩控制系统速度辩识

基于无速度传感器辨识精度和动态性能的提高,在传统的模型参考自适应(MRAS)速度辨识模型的基础上,参考模型采用转子磁链电压模型。应用神经网络理论,对其可调模型进行了改进。并在无速度传感器直接转矩控制系统中对该速度辨识模型进行了研究,仿真结果验证了该速度辨识模型具有满意的辨识精度和动态性能。     

基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

基于新型Type-1 PLL的永磁同步电机转子位置估计方法

为了提高表贴式永磁同步电机的调速性能,提出一种基于新型锁相环(PLL)的转子位置估计方法.首先,针对表贴式永磁同步电机定子反电动势进行谐波分析,定义谐波成分在旋转坐标系下的表现形式;然后,为提高转子位置估计性能,针对特定谐波成分设计由自适应陷波器(ANF)和低通滤波器(LPF)串级组成的新型滤波器;最后,对新型PLL与同步旋转坐标系锁相环(SRF-PLL)进行对比仿真实验,实验结果表明,所提出的新型PLL不仅能保证较高的估计精度,而且具有较快的转子位置跟踪速度和较强的滤波性能.     

Sensorless direct torque control of an induction motor by a TLS-based MRAS observer with adaptive integration

This article presents a new speed and flux estimation algorithm for high-performance direct torque control (DTC) induction motor drives based on model reference adaptive systems (MRAS) observers using linear artificial neural networks (ANNs). Two completely new improvements of MRAS speed and flux observers are presented here: the first is a solution to the open-loop integration problem in the reference model, based on the voltage model of the induction machine, by means of a new adaptive neural integrator, the second is the employment of a new adaptation law in the ANN adaptive model, based on the total least-squares (TLS) technique. In particular, the adaptive neural integrator is based on two adaptive noise filters which completely cancel any DC drift present in the voltage or current signals to be integrated. This neural integrator does not need any a priori training of its two only neurons, adapting itself on-line. With regard to the ANN-based adaptive model, since the most suitable least-square technique to be used for training is the TLS technique, here the neuron is trained on-line by means of a TLS EXIN algorithm which is the only neural network able to solve a TLS problem recursively. Also the TLS EXIN algorithm does not require any a priori training, since it adapts itself recursively on-line. Moreover, to improve the dynamical performances of the speed loop of the drive, the adaptive model has been used as predictor, i.e. without any feed-back between its outputs and its inputs. The sensorless algorithm has been verified experimentally both on the classic DTC technique and on the DTC-SVM (space vector modulation), by adopting a proper test set-up. The speed observer has been tested in the most challenging operating conditions. The experimental results show that the dynamical performances of the sensorless drive are comparable or even better than those obtained with the corresponding DTC drives with encoders as for the medium to high-speed ranges. As for low-speed ranges, the presented sensorless DTC algorithm outcomes the performance presented in the literature for MRAS systems, thus permitting to have an accurate estimation equal or better than that obtainable with more complex observers. Finally, experimental results show that the MRAS speed observer is robust to load torque perturbations and permits zero-speed operation at no-load conditions.