Some Permanent Magnet Synchronous Motor (PMSM) Sensorless Control Methods based on Operation Speed Area

Permanent magnet synchronous motors (PMSMs) are increasingly used in high performance variable speed drives of many industrial applications. PMSM has many features, like high efficiency, compactness, high torque to inertia ratio, rapid dynamic response, simple modeling and control, and maintenance free operation. Presence of position sensors presents several disadvantages, such as reduced reliability, susceptibility to noise, additional cost and weight and increased complexity of the drive system. For these reasons, the development of alternative indirect methods for speed and position control becomes an important research topic. Advantages of sensorless control are reduced hardware complexity, low cost, reduced size, cable elimination, increased noise immunity, increased reliability and decreased maintenance. The key problem in sensorless vector control of ac drives is the accurate dynamic estimation of the stator flux vector over a wide speed range using only terminal variables (currents and voltages). The difficulty comprises state estimation at very low speeds where the fundamental excitation is low and the observer performance tends to be poor. Moreover, the noises of system and measurements are considered other main problems. This paper presents a comprehensive study of the different sliding mode observer methods of speed and position estimations for sensorless control of PMSM drives.

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.

This article presents a Permanent Magnet Synchronous Motor (PMSM) sensorless control system using backstepping control. The Extended State Observer (ESO) is also used to perform the sensorless control, and to estimate the load torque which is considered an external disturbance of the control system. Starting from the classic Field Oriented Control (FOC) type PMSM control structure, the classic PI speed and current controllers i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> are replaced by a backstepping control structure. The main blocks used for the implementation and numerical simulation in the Matlab/Simulink graphical programming environment are presented. The very good static and dynamic performance obtained through numerical simulations even at high speeds, step variations of the value of the load torque, uniformly distributed noise, and variations of the PSMS nominal parameters can be explained by the fact that the backstepping control is applied to the non-linear system of the PMSM and by the step-by-step method of achieving each iqref, ud and uq control by using a Lyapunov-type function. Future articles propose the approach of real-time implementation of the PMSM sensorless control by using the backstepping control.

Recently, sensorless controls, which eliminate position and speed sensor in a permanent magnet synchronous motor drive, have been much studied. Most sensorless control algorithms are based on the back-EMF and speed estimations which are obtained from the voltage equations. Therefore, the sensorless control performance is largely affected by the parameter errors of a motor. This paper investigates a novel adaptive control with the parameter error compensation for the speed sensorless control of a permanent magnet synchronous motor. The proposed parameter estimation is obtained from the d-axis current error between the real and estimated currents. The proposed algorithm is verified through the simulation and experimentation.

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.

The paper proposes an on-line method for the estimation of the stator resistance and the permanent magnet flux in sensorless permanent magnet synchronous motor drives. An adaptive observer augmented with a high-frequency signal injection technique is used for sensorless control. The observer contains excess information that is not used for the speed and position estimation. This information is used for the adaptation of the motor parameters. At low speeds, the stator resistance is estimated, whereas at medium and high speeds, the permanent magnet flux is estimated. Steady-state analysis and small-signal analysis are carried out to investigate the parameter estimation, and adaptation mechanisms are defined for the parameters. The convergence of the parameter estimates is shown by simulations and laboratory experiments. The stator resistance adaptation works down to zero speed in sensorless control.

Sensorless technologies for the field-oriented control of the permanent magnet synchronous motors (PMSMs) are being actively developed today. Recently, a robust, nonlinear and globally convergent position observer for the surface-mount permanent magnet synchronous motor (SPMSM) has been proposed in [4]. The key feature of the robust observer is estimation of the rotor position requiring only the knowledge of the stator currents, voltages, resistance and inductance. However, the resistance and inductance of the stator may change during operation as a result of overheating, mechanical wear and environmental impacts. Thus, the aim of this study is to develop adaptive algorithms for the stator windings' parameters, which can be used for this position observer improvement. In this paper the authors propose four adaptive observers for estimation of the resistance and inductance at low motor speeds. It is assumed, that only currents and voltages of the stator windings are known. The other state variables and parameters of PMSM are unknown. The main idea of the synthesis of identification algorithms is linear filtering, which allows us to eliminate the unknown terms obtained by the mathematical transformations. Requirements to the external load, which ensure successful identification of the PMSM stator parameters, are given for each observer. The proposed adaptive observers for the stator resistance and inductance can be applied in case, when at low speeds, the field-oriented control of PMSM is carried out by means of Hall sensor, while at higher speeds, the sensorless control algorithms are used. A distinctive feature of the proposed estimation algorithms is that the resistance and inductance identification can be implemented during PMSM operation.

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 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.

In view of the unsteady transition between the high-speed and low-speed sensorless control strategies in the full speed range of the Electric Vehicle (EV) permanent magnet synchronous motor (PMSM), the sectional current reduction rate (SCRR) method and the direct switching method (DSM) are proposed to achieve the smooth transitions in the starting and braking process, respectively. To start with, the I/F control method is adopted to realize the startup successfully. And then in order to offer a smooth transition from I/F control method to Sliding Mode Observer (SMO) control mode, the given rotor current is reduced by SCRR, at the same time the synchronous rotating reference frame is close to the real rotor reference frame gradually. When the frames coincide with each other as far as possible, the two control methods complete the switching. In the braking state, the motor would be controlled by DSM, which switches the control method from the SMO to I/F method, to ensure the ride comfort of EV. Finally, the simulation not only verifies the feasibility of SCRR and DSM, but also compares the SCRR method with DSM to the traditional one-section current reduction rate method (OSCRR) without DSM. The results show that comparing with the traditional OSCRR, SCRR can accelerate the starting switching process of the motor and ensure a smooth and no-impact procedure. In addition, chatter in the braking state is eliminated effectively by DSM and the system has good anti-load-disturbance, dynamic and static response capability in various applications.

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.

Conventional techniques for sensorless control of permanent magnet synchronous motor drive (PMSM), which requires information on rotor position, are reviewed, and recent developments in this area are introduced in this paper along with their inherent advantages and drawbacks. The paper presents an improved method for sensorless speed control of PMSM drive with emphasis placed on signal injection method. This signal injection method examines the control performance of sensorless PMSM drive by injecting signal externally and thereby sensing the rotor position. The main objective of this drive system is to have speed control at standstill and low speed regions. Several tests are carried out to demonstrate the ability of proposed models at different operating conditions with the help of simulation results in Matlab/Simulink environment. Simulation results confirm that the proposed sensorless control approach of PMSM can achieve high performance at standstill and low speeds but not at very high speeds. An experimental setup is implemented using a 1HP surface mounted (SM) PMSM and DsPICDEM^TM MCHV-2 development board, to check the validity of simulation results.

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.

The high-frequency (HF) signal injection methods have been widely used for the low-speed sensorless control of permanent magnet motors under the vector control framework. However, for the fault-tolerant permanent magnet synchronous motor (FTPMSM) system under fault conditions, the fault-tolerant control is usually based on independent control of each phase current, rather than the vector control, which prevents the existing sensorless control from being directly applied. To cope with this issue, this article proposes a novel sensorless control by HF signal injection in any nonfault two-phase windings for the FTPMSM system with no coordinate transformation, which can ensure the low- and zero-speed sensorless control performance both in fault-free and fault-tolerant operation conditions. This sensorless control scheme is based on an independent control framework of each phase current, which consists of the real-time inductance observer, the nonorthogonal phase-locked loop (NPLL), the notch filter, and the polarity discrimination of the magnet pole. The real-time inductance observer is proposed to estimate the differential mode inductances of two nonfault phases. The NPLL is proposed to achieve the rotor position and speed estimation based on the obtained two-phase inductances. To enhance the sensorless control performance, a new notch filter is proposed to filter out the injected HF response component in phase current. The proposed sensorless FTPMSM system has preeminent low-speed control performance and robustness in the nonfault and fault-tolerant operation process, which is also verified via experiments on a six-phase FTPMSM platform.

This paper presents a novel PMSM sensorless control scheme based on a linear Kalman filter (LKF) observer. The LKF algorithm is able to estimate rotor position and current components in the stator reference frame by measuring stator currents, and using a mathematical model based on the voltage equations of motor. The proposed observer is linear but time-varying because the rotor speed is treated as a model parameter to be updated on-line. Estimation of rotor speed is performed in two steps: firstly a rough speed estimation is obtained by processing the estimates of rotor position; then a simple speed observer is used to improve the accuracy of speed estimates. The observer output, i.e. the final speed estimate, is fed back to both the speed control loop and LKF algorithm. Sensitivity to strong stator resistance variations makes this sensorless algorithm unsuitable for operation at very low speed; good performances are obtained in the speed range from 10% of rated speed to full speed.