Low-Noise Initial Position Detection Method for Sensorless Permanent Magnet Synchronous Motor Drives

Fast and reliable initial rotor position detection is essential for restarting sensorless permanent magnet synchronous motors (PMSMs) in free-running condition. In this article, a fast initial rotor position estimation method for low-speed free-running motor is proposed, which utilizes a combined sinusoidal current and square-wave voltage injection method. The sinusoidal current is imposed into the estimated d-axis to magnify the magnetic saturation effect. The amplitudes of the d-axis current caused by injected square-wave voltage are then accumulated. The large difference of the two integrated signals for positive and negative d-axis currents can be reliably used to identify the rotor polarity. Meanwhile, in low-speed free-running stage, the change of saturation degrees introduced by the injected sinusoidal signal does not affect the position estimation accuracy. Moreover, even if the sinusoidal current signal is injected in the incorrect d-axis, the resultant torque is small and unexpected rotation of the rotor is prevented. Its influence on the free-running motor is negligible, due to the combined injection with continuously online updated estimated rotor position by high-frequency square-wave voltage injection during the polarity identification process. Finally, the effectiveness of the proposed method is investigated on a 1.5 kW interior PMSM test platform.

Brushless DC (BLDC) motor drive creates major attention in industrial and domestic applications due to its low inertia, high torque and high power density. However, the major challenge in BLDC motor compared to any other drive is its initial rotor position detection. To detect the initial position of the rotor, traditionally HALL sensors are adopted which leads to high cost and unreliability. To eliminate this, the sensorless technique of speed control of BLDC motor is adopted using microcontroller. This paper mainly focuses on the start-up technique of BLDC motor where the sinusoidal commutation is used which eliminates torque ripple and provides precise smooth control. A technique called initial position detection is being focused which works based on inductive sense variation. The real-time experimentation is carried out for testing the reliability of the developed technique.

High performance operation of a sensorless Permanent magnet synchronous motor (PMSM) drive demands accurate tuning of controller and observer parameters. This paper presents an Extended Kalman filter (EKF) based sensorless PMSM drive, wherein the dynamic performance of the closed loop controlled drive is enhanced from the dual perspective of the controller and the observer. To this effect, a modification of the speed controller is proposed to reduce the overshoot in speed which is otherwise present while using a conventional PI controller. The performance of the EKF observer is improved by a simple adaptive tuning, which effects fast dynamic response in speed estimation while providing better noise rejection in the steady state operation of the drive. The proposed techniques are validated using simulation and experiment, and results are presented for a Voltage Source Inverter fed 1.5 kW sensorless PMSM drive.

ABSTRACTThis paper proposes a new method to detect the initial rotor position at standstill of permanent magnet synchronous motor (PMSM). To estimate rotor position and rotor speed from the back electromotive force (EMF) voltage, we apply sensorless speed control based on sliding-mode observer (SMO). The initial rotor position is detected by using a suitable high-frequency sequence of voltage pulses intermittently applied to the stator windings at standstill. With this approach, we managed to minimise the error on the estimated position to 3.75° electrical degrees without additional materials and uncomplicated calculations. The stability of the proposed SMO was verified using the Lyapunov method. Numerical simulations and experiments demonstrate that the novel SMO method can effectively estimate rotor position and speed with achievement of good static and dynamic performance. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the digital signal processor (DSP) TMS320F240.

In this paper, the inductance of a sensorless PMSM (Permanent Magnet Synchronous Motor) drive system equipped with a periodic load torque compensator based on a wavelet denoising and least-order observer with time-delay compensation is estimated in real-time. In a sensorless PMSM system with constant load torque, the magnetically saturated inductance value remains constant. This constant inductance error causes minor performance degradation, such as a constant rotor position estimation error and non-optimal torque current, but it does not introduce a speed estimation error. Conversely, in a sensorless PMSM motor system subjected to periodic load torque, the magnetically saturated inductance error fluctuates periodically. This fluctuation leads to periodic variations in both the estimated position error and the speed error, ultimately degrading the load torque compensation performance. This paper applies the maximum energy-to-Shannon entropy criterion for the optimal selection of the mother wavelet in the wavelet transform to remove the motor signal noise and achieve more accurate inductance estimation. Additionally, the coherence and correlation theory is proposed to address the time delay in the least-order observer and improve the time delay. A self-saturation compensation method is also proposed to minimize periodic speed fluctuations and improve control accuracy through inductance parameter estimation. Finally, experiments were conducted on a sensorless PMSM drive system to verify the inductance estimation performance and validate the effectiveness of vibration reduction.

Industrial sensorless permanent magnet synchronous motors (PSM) working at minimum speed of about 10 % of rated speed have been in use for some years. However, PSM drives with high static and dynamic quality down to standstill have just started to be in use in industry. This paper presents a description of the properties of PSM drives using the INFORM® method for sensorless zero-speed control even for torque-, speed and even position-controlled applications. It is shown that the method is able to ensure highly transient operation even at high torque (up to a few times of rated torque) in the low-speed range. Evaluating the statistical properties of the method [Figs. 5, 7] yields probabilities of failures during the lifetime of the drive depending on the “INFORM quality” of the PSM as well as on the dynamic load properties of the application. An industrial example shows the advantageous application of the method in a position-controlled series application.

In this paper, normalised state vectors are used to demonstrate the state equations of Extended Kalman Filter (EKF) based sensorless Permanent Magnet Synchronous Motor (PMSM) drive. Based on the normalised EKF equations, Simple Genetic Algorithm (SGA) is employed to optimize the noise covariance and weight matrices of EKF parameters, which thereby reduces the parameter adjusting time and ensures stability of filters in estimations of position and speed. The simulations for SGA training are carried out by MATLAB/Simulink. The experimental sensorless drive system employing Field Oriented Control (FOC) method and SGA is implemented on STM32F103. The simulating and experimental results indicate the effectiveness of the proposed method.

This paper compares some sensorless Permanent Magnet Synchronous Motor (PMSM) controls for driving an electric vehicle in terms of operating speed. Sensorless control is a type of control method in which sensors, such as speed and position sensors, are not used to measure controlled variables. The controlled variable value is estimated from the stator current measurement. Sensorless control performance is not as good as a sensor-based system. This paper aims are to recommend a control method for the PMSM sensorless controls that would be used to drive an electric vehicle. The methods that we will discuss are divided into four categories based on the operation speed area. They are a startup, low speed, high speed, and low and high-speed areas. The low and high-speed area will be divided into with and without switching. If PMSM more work at high speed, the most speed area that is used, we prefer to choose the method that works at high speed, that is, the modification or combination of two or more conventional methods.

A new sensorless PMSM (Permanent Magnet Synchronous Motor) drive with reduced current sensors and field weakening algorithm is presented in this paper. After an overview among the different kinds of sensorless drives that appear in the literature, phase current and position/speed estimators used in this application are explained. Simulation and experimental results are reported.

This paper presents a speed and position sensorless permanent magnet synchronous motor (PMSM) drive using simple and robust model reference adaptive control (MRAC) for light electric vehicle (LEV) application. The sensorless speed and position estimator consists the flux and torque based MRAC, which improves the performance during dynamic as well as steady state condition for wide range of speed operation. Mostly, PMSM drive uses field oriented control for the closed loop speed control operation. Generally, PI controller is used speed and current controller. However, the PI controller gain requirement often changes during various driving conditions of the vehicle. Therefore, this work is focused to eliminate fixed gain PI controller by utilizing online self-tuned PI speed controller and PMSM model based predictive current controller. The self-tuning approach of PI controller for the speed control loop uses the principle of adaptive interaction, which performs tuning by minimizing error objective function. The predictive current controller eliminates the need of modulator and incorporates the inverter nonlinearities. In addition to this, the sensorless approach reduces the complexity and cost of the drive. The combined system of improved controllers and sensorless estimator improves the drive performance for wide range of speed operation.

For the position sensorless permanent magnet synchronous motor (PMSM) drive system, the online identification of temperature is of great significance for safe operation. Generally, the motor temperature can be obtained directly from the stator resistance, the identification of which is, however, affected by the position estimation error of sensorless control. In order to deal with this problem, a stator temperature identification method for the interior PMSM is proposed with the position error adaptive compensation. The influence of position error on the stator resistance and temperature identification is analyzed and then compensated based on the minimum current vector online tracking (MCVOT) scheme for improving the identification precision. For further improving the accuracy and the convergence rate of MCVOT, a current vector angle tracking strategy based on the adaptive variable tracking step value is adopted in the rotating reference frame. The validity of the proposed method is verified on two experimental platforms.

Objective. This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. Approach. Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used. Both analyses were applied to computer simulations of surface EMG baseline with the presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. Main results. Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. Significance. The findings implied the presence of a hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.

An indirect rotor speed and position sensing technique based on computation for a permanent magnet synchronous motor (PMSM) drive is presented in this paper. New expressions in terms of stator variables for rotor speed and position angle are derived and computed using real data from a laboratory prototype PM motor. The results show that the rotor speed and angle are in close agreement with those obtained from an electro-mechanical sensor. A two-loop PMSM drive system is then simulated using the derived expressions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper studies a speed sensorless permanent magnet synchronous motor (PMSM) drive system based on the adaptive observer method, analyzes the problem of instability in the low-speed electric mode according to the observer linearization method. To solve this problem, the observer augmented with a signal injection technique is researched. The simulation results show that the method is effective to enhance the system parameter robustness and speed observation precision, improve low speed performance of the speed sensorless vector control system.

In the permanent magnet synchronous motor (PMSM) sensorless drive method, motor inductance is a decisive parameter for rotor position estimation. Due to core magnetic saturation, the motor current easily invokes inductance variation and degrades rotor position estimation accuracy. For a constant load torque, saturated inductance and inductance error in the sensorless drive method are constant. Inductance error results in constant rotor position estimation error and minor degradations, such as less optimal torque current, but no speed estimation error. For a periodic load torque, the inductance parameter error periodically fluctuates and, as a result, the position estimation error and speed error also periodically fluctuate. Periodic speed error makes speed regulation and load torque compensation especially difficult. This paper presents an inductance parameter estimator based on polynomial neural network (PNN) machine learning for PMSM sensorless drive with a period load torque compensator. By applying an inductance estimator, we also proposed a magnetic saturation compensation method to minimize periodic speed fluctuation. Simulation and experiments were conducted to validate the proposed method by confirming improved position and speed estimation accuracy and reduced system vibration against periodic load torque.