Implementation and experimental investigation of sensorless speed control with initial rotor position estimation for interior permanent magnet synchronous motor drive

In this paper, a new approach to sensorless speed control and initial rotor position estimation for interior permanent magnet synchronous motor (IPMSM) drive is presented. In rotating condition, speed and rotor position estimation of IPMSM drive are obtained through an extended Kalman filter (EKF) algorithm simply by measurement of the stator line voltages and currents. The main difficulty in developing an EKF for IPMSM is the complexity of the dynamic model expressed in the stationary coordinate system. This model is more complex than that of the surface PMSM, because of the asymmetry of the magnetic circuit. The starting procedure is a problem under sensorless drives, because no information is available before starting. The initial rotor position is estimated by a suitable sequence of voltage pulses intermittently applied to the stator windings at standstill and the measurement of the peak current values of the current leads to the rotor position. Magnetic saturation effect on the saliency is used to distinguish the north magnetic pole from the south. To illustrate our work, we present experimental results for an IPMSM obtained on a floating point digital signal processor (DSP) TMS320C31/40 MHz based control system.     

永磁同步电机矢量控制系统的EKF无传感器控制策略

介绍了一种基于扩展卡尔曼滤波的永磁同步电机无传感器转子位置与速度估算方法,并以此为基础实现了永磁同步电机的无传感器矢量控制系统。通过测量流过电机定子电流和电机端电压在线估计电机转子的位置和速度,实现永磁同步电机的无传感器控制策略。仿真和实验结果验证了该方案的可行性及有效性。     

Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive

This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The rotor speed is synthesized from the induction motor state equations. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive     

Rotor position estimation in a switched reluctance drive usingrecursive least squares

This paper describes a new algorithm for the estimation of rotor position in a switched reluctance motor. It is based on a recursive least-squares estimator deducing both position and speed. A particular advantage of the algorithm is its ability to extract information about rotor position at very low speeds (one electrical cycle per minute) from voltage and current waveforms sampled only at the converter switching frequency. Experimental results for a 12/8 motor demonstrate that estimation is possible over the full range of operating conditions, including the field-weakening region, with a typical accuracy of better than two mechanical degrees. The paper also illustrates the performance of the algorithm by showing it operating within a sensorless position controller     

Sensorless PM brushless DC motor drives

To control PM brushless DC motors, position and speed sensors are indispensable because the current should be controlled depending on the rotor position. However, these sensors are undesirable from standpoints of size, cost, maintenance, and reliability. There are different ways of approaching this problem, depending on the flux distribution. The paper presents the speed and position sensorless control of PM brushless DC motors with a sinusoidal flux distribution. Two approaches are presented and compared with each other; one is based on the voltage model of the motor and another is based on the current model. The starting procedure is also a very difficult problem under sensorless drives, because the sensorless drive algorithm uses voltage and current for estimation of rotor position, but no information is available before starting. A novel starting method is presented by using a salient-pole machine. Experimental results based on DSP-TMS320C25 controller are shown for comparisons, which demonstrate desired characteristics both in steady-state and starting conditions     

Synchronization at startup and stable rotation reversal of sensorless nonsalient PMSM drives

In this paper, a variant of the well-known "voltage model" is applied to rotor position estimation for sensorless control of nonsalient permanent-magnet synchronous motors (PMSMs). Particular focus is on a low-speed operation. It is shown that a guaranteed synchronization from any initial rotor position and stable reversal of rotation can be accomplished, in both cases under load. Stable rotation reversal is accomplished by making the estimator insensitive to the stator resistance. It is also shown that the closed-loop speed dynamics are similar to those of a sensored drive for speeds above approximately 0.1 per unit, provided that the model stator inductance is underestimated. Experimental results support the theory.     

Experimental Evaluation of Braided EKF for Sensorless Control of Induction Motors

Temperature- and frequency-dependent variations of the rotor (R'_r) and stator (R_s) resistances pose a challenge in the accurate estimation of flux and velocity in the sensorless control of induction motors (IMs) over a wide speed range. Solutions have been sought to the problem by signal injection and/or by the use of different algorithms for the different parameters and states of the same motor. In this paper, a novel Extended-Kalman-Filter (EKF)-based estimation technique is developed for the solution of the problem based on the consecutive operation of two EKF algorithms at every time step. The proposed ldquobraidedrdquo EKF technique is experimentally tested under challenging parameter and load variations in a wide speed range, including low speed. The results demonstrate a significantly increased accuracy in the estimation of R_s and R'_r, as well as load torque, flux, and velocity in transient and steady state, when compared with single EKFs or other approaches taken to estimate these parameters and states in the sensorless control of IMs. The improved results also motivate the utilization of the new estimation approach in combination with a variety of control methods which depend on accurate knowledge of a high number of parameters and states.     

Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives

In this paper, a new Kalman filtering technique, unscented Kalman filter (UKF), is utilized both experimentally and theoretically as a state estimation tool in field-oriented control (FOC) of sensorless ac drives. Using the advantages of this recent derivative-free nonlinear estimation tool, rotor speed and$dq$-axis fluxes of an induction motor are estimated only with the sensed stator currents and voltages information. In order to compare the estimation performances of the extended Kalman filter (EKF) and UKF explicitly, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. In the simulation results, it is shown that UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, has more satisfactory rotor speed and flux estimates, which are the most critical states for FOC. These simulation results are supported with experimental results.     

无位置传感器无刷直流电机启动的新方法研究

针对无位置传感器无刷直流电机的启动问题,提出了对三段式闭环启动中的预定位、外加速过程的改进方法。在转子定位阶段采用基于空间电压矢量调制的短时脉冲来获得转子初始位置,精度可达15°。加速阶段采用施加空间电压矢量,通过检测母线电流变化率确定换相时刻,当速度达到额定转速的80%后,通过反电动势法进行换相。通过仿真分析可知,该方法能够有效的实现电机启动,并提高了启动的可靠性。     

基于模型参考自适应系统的感应电机控制

采用模型参考自适应法设计了无速度传感器矢量观测器。现以电压模型为参考模型,电流模型为可调模型,推算出速度信息,计算输出控制信号,实现了对感应电机的精确控制;通过Matlab／Simulink对其进行仿真、验证,结果表明,该系统对定子磁链观测精度高,速度估计准确,改善了电机的控制特性。     

基于自适应状态观测器的异步电机无速度传感器矢量控制系统

异步电机无速度传感器矢量控制系统是目前研究的热点,本文采用一种闭环磁链观测器,即自适应状态观测器对转子磁链进行观测,与传统开环电压、电流模型相比,观测效果更好。在转子磁链观测的基础上,采用PI型自适应律,对转速进行了辨识。最后,通过Matlab仿真验证了本文给出的异步电机无速度传感器矢量控制系统的可行性,仿真结果表明该系统具有较好的动、静态性能,并具有一定的抗干扰能力。     

Online rotor resistance estimation using the transient state underthe speed sensorless control of induction motor

In the speed sensorless control of the induction motor, the machine parameters (especially rotor resistance R₂) have a strong influence on the speed estimation. It is known that the simultaneous estimation of the rotor speed and R₂ is impossible in the slip frequency type vector control, because the rotor flux is constant. But the rotor flux is not always constant in the speed transient state. In this paper, the R₂ estimation in the transient state without signal injection to the stator current is proposed. This algorithm uses the least mean square algorithm and the adaptive algorithm, and it is possible to estimate R₂ exactly. This algorithm is verified by the digital simulations and experiments     

Sensorless speed control of nonsalient permanent-magnet synchronous motor using rotor-position-tracking PI controller

This paper presents a new velocity estimation strategy of a nonsalient permanent-magnet synchronous motor (PMSM) drive without a high-frequency signal injection or special pulsewidth-modulation (PWM) pattern. This approach is based on the d-axis current regulator output voltage of the drive system that has the information of rotor position error. Rotor velocity can be estimated through a rotor-position-tracking proportional-integral (PI) controller that controls the position error to zero. For zero and low-speed operation, the PI controller gains of rotor position tracking controller have a variable structure according to the estimated rotor velocity. In order to boost the bandwidth of the PI controller around zero speed, a loop recovery technique is applied to the control system. The proposed method only requires the flux linkage of the permanent magnet and is insensitive to parameter estimation error and variation. The designers can easily determine the possible operating range with a desired bandwidth and perform vector control even at low speeds. The experimental results show the satisfactory operation of the proposed sensorless algorithm under rated load conditions.     

基于龙贝格观测器的PMSM无位置传感器控制系统设计

针对基于低分辨率霍尔位置传感器的永磁同步电机系统在中高速时出现的估算精度低与响应速度慢等问题,在建立永磁同步电机数学模型的基础上,将龙贝格观测器与锁相环结构相结合,提出一种永磁同步电机无位置传感器控制算法。利用MATLAB/Simulink工具搭建控制系统仿真模型验证该控制系统的可行性,并通过搭建基于PAC5232的实物平台对比验证龙贝格观测器相对于霍尔位置传感器的优越性。实验结果表明,该无位置传感器控制系统有效地提高了系统的响应速度和估算精度,使其能够更好地跟踪转子速度以及转子位置信息。     

Sensorless DTC-SVM for Induction Motor Driven by a Matrix Converter Using a Parameter Estimation Strategy

This paper presents a new direct torque controlled space vector modulated method to improve the sensorless performance of matrix converter drives using a parameter estimation scheme. The flux and torque error are geometrically combined in a new flux leakage vector to make a stator command voltage vector in a deadbeat manner. A new sensorless method of estimating the rotor speed, flux, stator resistance, and rotor resistance is derived and verified with experimental results. Common terms in the error dynamics are utilized to find a simpler error model involving some auxiliary variables. Using this error model, the state estimation problem is converted into a parameter estimation problem assuming the rotor speed is constant. The proposed adaptive schemes are determined so that the whole system is stable in the sense of Lyapunov. The effectiveness of the proposed algorithm is verified by experiments.     

PMSM无传感器初始位置检测及低速运行研究

目前,永磁同步电机(PMSM)无位置传感器运行研究受到广泛关注。文章采用一种基于高频方波信号注入的方法实现PMSM无位置传感器启动以及低速运行。首先详细分析了高频方波信号注入检测原理,然后对注入的高频方波信号以及电流采样模式进行了改进。在估计的两相旋转坐标系轴向注入频率等于逆变器开关频率的高频方波电压信号,通过巧妙的安排定子电流采样模式,根据检测到的定子电流并结合注入的方波电压信号即可获得转子位置信息;采用Luenberger观测器对转子位置信息进行观测,以获得较为平稳准确的电机转速和转子角度估计值;利用电机的磁路饱和特性,实现基于高频方波信号注入法的PMSM无位置传感器转子初始位置检测。所提出的改进方法不依赖于准确的电机参数,不需要使用任何滤波器,信号处理过程简单易实现。仿真结果验证了该方法的正确性。     

Instability phenomena and remedies in sensorless indirect fieldoriented control

Sensorless indirect field oriented induction motor drives using information in the fundamental excitation for speed estimation are considered. It is shown that instability phenomena which are not present for sensored operation occur, both for nominal and low speeds. The problems at nominal speeds are remedied by adding a term to the standard slip relation, such that approximate “voltage model” characteristics are obtained. The low-speed problems are remedied by careful choice of the model machine parameters and the torque-producing stator current component. It is also shown how a speed estimation algorithm based on a voltage error can be designed. Its asymptotic properties are similar to those of a current error-based least squares estimator such as the extended Kalman filter. As no state observer is required, the sensorless control system is computationally very efficient, requiring only about 15 multiplications and two divisions per sample for a digital implementation     

High dynamic speed sensorless AC drive with on-line model parametertuning for steady-state accuracy

Controlled speed sensorless AC motor drives have reached a stage of development permitting good dynamic performance above 3% of rated speed. However, the accuracy of the rotor speed estimation under load remains sensitive to parameter errors of the internal machine model. This paper presents an approach that ensures high steady-state speed accuracy in addition to high dynamic performance. To eliminate the speed estimation error, the machine parameters are adapted online, based on the evaluation of rotor slot harmonic effects. A stator flux-oriented control scheme is implemented in a digital signal processor system to demonstrate the robustness of the speed estimation to parameter variations. Experimental results demonstrate that the control system advantageously combines high dynamic performance with accuracy of speed estimation     

Initial rotor position estimation method of sensorless PMsynchronous motor with no sensitivity to armature resistance

This paper proposes a new initial rotor position estimation method of a sensorless permanent magnet (PM) synchronous motor at starting condition, which has no sensitivity to the armature resistance. The method is based on saliency of the rotor and employs the alternating magnetic field which is excited by a current controller. The phase differences between the magnetizing current references and the voltage references make it possible to estimate the rotor direction accurately without motor parameters, except the ratio of direct axis and quadrature axis inductance. Also, the magnetic pole is evidently identified by detecting the voltage reference oscillation phenomena caused by magnetic saturation, which can be performed without motor parameters. The experimental result has proven that the estimation error was within -4.5 to +2.5 mechanical degrees, even though the armature resistance varied to 125% of the nominal value     

基于Matlab／Simuljnk的无速度传感器直接转矩控制的仿真

本文介绍了异步电动机直接转矩控制的基本原理，提出了基于自适应全阶磁链观测器的速度估算方法，实现了无速度传感器的速度辨识。并应用Matlab／Simulink软件对该系统进行了建模和仿真，仿真结果表明，该系统对电机定子磁链的观测精度高，转速估算准确，尤其在低速下能保持很高的性能。