Accuracy Of Rotor Position Estimation Research Articles

Sensorless Control of Surfaced-Mounted Permanent Magnet Synchronous Motor in a Wide-Speed Range

This paper delves into a comprehensive study of a wide-speed-range sensorless control approach for surface-mounted permanent magnet synchronous motors (SPMSMs). In the low-speed range, a novel high-frequency pulse voltage injection (HFPVI) method is introduced for rotor position estimation, which does not depend on motor saliency and is well-suited for SPMSMs. This method incorporates a second-order generalized integrator (SOGI) and a new modulation signal to enhance the accuracy of rotor position estimation. For medium-to-high speeds, an improved super-twisting sliding mode observer (STSMO) utilizing a continuous hyperbolic tangent function is proposed to mitigate chattering. Additionally, a new phase-locked loop (NPLL) is introduced to accurately obtain the rotor position. Furthermore, this paper designs an exponential weighted switching function to facilitate a smooth transition of the motor from the low-speed domain to the medium- and high-speed domains. The effectiveness and superiority of the proposed methods are validated through simulations and experiments conducted on an RTU-BOX platform. The rotor position estimation errors of the proposed new HFPVI method and the improved STSMO method under various operating conditions are both approximately 0.05 rad (2.8 elc·deg), and the SPMSM can switch smoothly from the low-speed range to the medium- and high-speed ranges.

A Rotor Position Detection Method for Permanent Magnet Synchronous Motors Based on Variable Gain Discrete Sliding Mode Observer

The purpose of this paper is to study the sensor-less rotor position estimation method for permanent magnet synchronous motors, and to achieve accurate estimation of rotor position in different conditions. Firstly, the traditional super-twisting observer algorithm is analyzed, and a new discrete variable gain sliding mode observer is designed to solve the buffeting problem in discrete systems, taking the reaction force as the disturbance signal. By estimating the back potential of the observer, the buffeting problem in the sliding mode algorithm can be effectively improved as shown by the simulation results. Then, to solve the problem of phase delay in rotor position estimation, an adaptive orthogonal phase-locked loop method is used to compensate the estimation error caused by the change in motor speed and increase the estimation accuracy of rotor position. The stability of the method can be proven by Lyapunov’s second method. Simulation experiments verify the accuracy of the proposed PMSM rotor position estimation method.

Sensorless Speed Control of PMSM in Low-Speed by High Frequency Pulsating Injection with Double Modulation Error Compensation

A novel improvement scheme for the High Frequency Pulsating Injection-Sinusoidal (HFPI-S) method that considers the error function is proposed and implemented for PMSM sensorless control. The limitations in extracting the error function and dealing with signal noise at low-speed could appear the estimation errors in rotor position within the HFPI-S system. In this study, a new model for the proposed method, including the addition of dual modulation as error compensation in the process of extracting rotor position and enhancing the observer for conventional rotor position signals was focused. The results of this study showed that the extraction of the error function that is expanded could obtain more accurate results. Therefore, the addition of dual modulation and the observer enhancement could be effectively applied to maintain the accuracy of rotor position estimation even under low-speed and loaded conditions.

Enhanced Position Estimation Based on Frequency Adaptive Generalized Comb Filter for Interior Permanent Magnet Synchronous Motor Drives

Accurate rotor position estimation is critical to ensure the high-performance operation of position sensorless interior permanent magnet synchronous motor (IPMSM) drives. However, the back electromotive force (BEMF) estimated by the model-based observers is usually nonideal, which may further degrade the position estimation accuracy. In this paper, a frequency adaptive generalized comb filter (FAGCF) with a quadrature phase-locked loop (PLL) is adopted to suppress the rotor position estimation errors by filtering out the dominant distortions in the BEMF, including the (6k±1)th harmonics caused by inverter nonlinearity and flux spatial harmonics, as well as the DC offset from the inaccurate current measurements. This proposed filter is constructed by multiple digital delay blocks and therefore has the advantage of easy implementation with less online parameters tuning effort. Meanwhile, a linearized model is derived for PLL parameter design, with which the stability of the FAGCF-PLL position estimation system can also be analyzed theoretically. The effectiveness of the proposed sensorless control strategy has been validated with the experimental results on a 1.5-kW IPMSM drive.

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

For the problem of the rotor position estimation and control accuracy of permanent magnet synchronous motor (PMSM), this paper proposes a PMSM sensorless based on radial basis function (RBF) neural network optimized Automatic disturbance rejection control (RBF-ADRC) and strong tracking filter (STF) improved square root generalized fifth-order cubature Kalman filter (SGHCKF-STF). The Automatic disturbance rejection control (ADRC) has strong robustness, but there are many parameters and difficult to adjust. Now we use RBF neural network to adjust the parameters in ADRC online so as to improve the robustness and anti-disturbance ability. In order to improve the estimation accuracy of rotor position and speed, the orthogonal triangle (QR) decomposition and STF are introduced on the basis of the generalized fifth-order cubature Kalman filter (GHCKF) to design the SGHCKF-STF algorithm that not only ensure the non-positive nature of the covariance matrix but also improve the ability to cope with sudden changes in state during the filtering process. Experimental results show that the combination of RBF-ADRC and SGHCKF-STF improve the sensorless control effect of the PMSM to some extent.

Sensorless control of SPMSM based on an adaptive sliding mode observer with optimized phase-locked loop structure

To improve the speed and position detection accuracy of surface-mount permanent magnet synchronous motor (SPMSM) vector control and reduce unnecessary chattering of the system, this paper proposes a sensorless control strategy of SPMSM based on an adaptive sliding mode observer (ASMO) with optimized phase-locked loop (OPLL) structure. First, in order to overcome the chattering of system caused by discontinuous switching characteristic of signum function in conventional sliding mode observer (CSMO), a continuous saturation function is selected as the switching function. The ASMO adopts the system state-related adaptive gain function to adjust the switching gain value of the system in real time, which overcomes the slow response speed or severe chattering of the system caused by the constant switching gain of CSMO. Second, to reduce the phase delay between the rotor position estimation value and the actual value caused by the adoption of low-pass filter (LPF) and the position estimation error caused by arctangent function method, an OPLL method is designed for accurate estimation of rotor position and speed. Finally, the effectiveness and feasibility of the proposed improved SMO algorithm is verified by simulation and experiments on an SPMSM with rated power of 2 kW.

Research on Permanent Magnet Synchronous Motor Sensorless Control System Based on Integral Backstepping Controller and Enhanced Linear Extended State Observer

The traditional sensorless control system of permanent magnet synchronous motor (PMSM) has the problems of low estimation accuracy and poor anti-interference ability. Moreover, the position estimation performance is subjected to position harmonic ripples caused by inverter nonlinearities and flux spatial harmonics. To optimize the dynamic performance of the PMSM sensorless control system, this paper proposes a sensorless control scheme that combines integral backstepping control with enhanced linear extended state observer (ELESO). The ELESO consists of two linear extended state observers (LESOs), which estimate the internal and external disturbances of the system, to improve the estimation accuracy of rotor position. Then, the integral backstepping controller processes the estimated rotor position and speed information to obtain d and q-axis voltages. The sensorless control scheme is implemented in the Matlab/Simulink and verified by experiments. The simulation and experiment show that the scheme can effectively suppress load interference and improve control accuracy.

Adaptive Quasi-Proportional-Resonant Observer-Based Rotor Position Estimation for Sensorless PMSM Drives

It is important to improve the accuracy of rotor position estimation for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To mitigate the position harmonic error caused by the nonideal factors in high-frequency signal injection based methods, an adaptive quasi-proportional-resonant rotor position observer (AQPRO) for sensorless PMSM drives is proposed in this article. The AQPRO can track the estimation error and generate the compensation value, which can realize online suppression for position error. By analyzing the stability and the influence of parameters of the proposed position observer on the suppression performance, a proportional-resonant gain self-tuning strategy is investigated to improve the suppression effect of the position harmonic error for different loads and speeds. The influence of self-tuning regulator parameters is modeled and analyzed in detail. The proposed method does not require offline detection and has a strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.

Adaptive Iterative Learning Control-Based Rotor Position Harmonic Error Suppression Method for Sensorless PMSM Drives

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

A Flying Restart Strategy for Position Sensorless PMSM Driven by Quasi-Z-Source Inverter

The accurate estimation of rotor position and speed before flying restart is of great significance to improve the operation reliability of permanent magnet synchronous motor systems. The traditional multizero vector short-circuit method can improve the estimation accuracy of speed and rotor position, but the increased number of short-circuits reduces the electromagnetic torque response speed after the power supply recovers. In order to accurately estimate the initial speed and rotor position before the flying restart and effectively improve the electromagnetic torque response speed, a shoot-through zero vector short-circuit method based on quasi-Z-source inverter (qZSI) is proposed. This method breaks the limitation of regulating DC link voltage under the normal operation of the motor in the conventional methods, and puts forward a new idea of advancing the regulation of the DC link voltage to the stage of abnormal operation before the motor restarts. By designing the insertion mode of the mixed vectors and analyzing the action time of each vector before the flying restart, the accurate estimation of position and speed is realized and, meanwhile, the boost of the qZSI’s DC link voltage is achieved, thus giving the sensorless flying restart method a faster torque response speed for the PMSM system driven by qZSIs.

UKF-Based Parameter Estimation and Identification for Permanent Magnet Synchronous Motor

The accuracy of rotor position estimation determines the performance of the sensorless control system of a permanent magnet synchronous motor. In order to realize the accurate control of rotor position and speed, it is necessary to identify the motor parameters. Modeling and simulation of the state estimation are investigated for a permanent magnet synchronous motor with parameter identification based on the unscented Kalman filter (UKF) in this article. Based on the mathematical model of the motor, the unscented Kalman filter is used to identify the rotor flux and quadrature axis inductance simultaneously, and the identified parameters are used to update the motor model in the sensorless vector control algorithm. The simulation results show that the unscented Kalman filter can converge to the real value in a short time with small errors. It can follow the changes of motor parameters well and achieve high-precision speed and position estimation.

Research On Sensorless Control Of Permanent Magnet Synchronous Motor

Aiming at the problem that there is no position sensor to observe the rotor position and speed of the built-in permanent magnet synchronous motor under the traditional method of high frequency rotating voltage signal injection, the estimation accuracy of rotor position and speed is low because of the use of a variety of filters in the signal demodulation process. The generalized second-order integrator is used to replace the traditional filters to extract the high frequency response current. In order to reduce the filtering delay problem existing in the traditional signal demodulation method, a new signal demodulation method is proposed, and the rotor position estimation is compensated with appropriate phase compensation. At the same time, appropriate phase compensation is carried out for the estimation of rotor position By constructing IPMSM sensorless vector control simulation module in Simulink simulation, compared with the traditional high-frequency injection method, the improved algorithm can estimate the rotor magnetic pole position more accurately and faster in the process of sensorless control of the motor in the low speed range, and has better stability introduction.

Improved Position Estimation Method for SRM Considering Current Measurement Errors

Saturation and current measurement errors will decrease the accuracy of rotor position estimation for switched reluctance motor (SRM). To avoid the drawbacks, a rotor position estimation method based on the unsaturated inductance reconstruction is proposed considering current measurement errors. Firstly, the incremental, apparent and unsaturated inductances are analyzed. The unsaturated inductance is calculated with the similar triangle solution in normal condition during the linear inductance rising region. To avoid the pulse injection, the linear regression algorithm is used to calculate the virtual unsaturated inductance in the linear inductance falling region of adjacent phase. Then the complete rotor position in each electric period is obtained by combining the estimated position of three phases. Secondly, the influence of current measurement errors on rotor position estimation is analyzed. The offset error is directly avoided by detecting the zero-value region of phase currents. The scaling error is compensated by balancing the amplitudes of three phase unsaturated inductances. The proposed method does not need additional hardware and can avoid the influence of saturation and current measurement errors simultaneously. Experimental results validate the effectiveness of the proposed method.

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

In this paper, a sensorless control strategy of permanent magnet synchronous motor (PMSM) based on fuzzy sliding mode observer (FSMO) is proposed. On the premise of satisfying the Lyapunov stability condition, a sliding mode observer (SMO) is constructed. The sigmoid function is used instead of the sign function as the switching function. The parameters of sigmoid function are adjusted in real time by establishing fuzzy rules to change the convergence characteristics of sigmoid function, so as to improve the observation performance. The back EMF signal extracted by the SMO can be made smoother by using the back EMF adaptive law, which reduces the chattering of the system and the observation error. In order to solve the problem that the traditional phase locked loop (PLL) can not be used when the positive and negative speed of the motor is switched, a tangent function PLL is proposed in this paper. Through the use of tangent function, the value and symbol of back EMF are avoided from entering the system, so as to realize the accurate estimation of rotor speed and rotor position under the condition of positive and negative speed switching. The designed Fuzzy Sliding Mode Observer is simulated in Matlab/Simulink, and the experimental verification is carried out on the 2kW surface mounted PMSM vector control platform with TMS320F28335 as the main control chip. Simulation and experimental results show that this method can effectively track the changes of rotor speed and position when the motor is switched between positive and negative speed, and has the characteristics of fast convergence, small chattering and good robustness.

Speed Sensor-Less Control System of Surface-Mounted Permanent Magnet Synchronous Motor Based on Adaptive Feedback Gain Supertwisting Sliding Mode Observer

Aiming at the problems of severe chattering and difficulty in low-speed operation of the surface-mounted permanent magnet synchronous motor (SPMSM) sensor-less speed control system based on the traditional sliding mode observer (SMO), this paper proposes a sensor-less control strategy of supertwisting sliding mode observer based on adaptive feedback gain (AFG-STA-SMO). This strategy combines the supertwisting algorithm (STA) with the equivalent feedback principle and designs an adaptive law to compensate for the rotor position error by adjusting the feedback gain coefficient online. Secondly, considering the ripple component in the back electromotive force (back-EMF), the Kalman filter gets a smoother back-EMF signal, further improving the rotor position estimation accuracy. The stability of the system is proved by using the Lyapunov function. Finally, the feasibility and effectiveness of the proposed control strategy are verified by MATLAB/Simulink simulation.

A Hybrid Filtering Stage-Based Rotor Position Estimation Method of PMSM with Adaptive Parameter.

The performance of sensorless control in a permanent magnet synchronous machine (PMSM) highly depends on the accuracy of rotor position estimation. Owing to its strong robustness, phase-locked loop (PLL) is widely used in rotor position estimation. However, due to the influence of harmonics existing in back electromotive force (EMF), estimation error occurs by using PLL. In this paper, a hybrid filtering stage-based PLL is proposed to improve the rotor position estimation. Adaptive notch filters and moving average filters are integrated together to eliminate harmonic EMF. To make the method effective under varying speed conditions, adaptive parameters design guidelines are provided, considering dynamic performance under a wide operating range. The proposed method can accurately detect rotor position even under harmonic EMF disturbances. It can also adjust the frequency adaptively based on the rotating speed of the rotor, which means the estimation performance is not deteriorated under rotating speed changing conditions. The simulation results verify the effectiveness of the proposed method.

Sensorless PMSM Drive Inductance Estimation Based on a Data-Driven Approach

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.

Current Vector Angle Adaptive Adjustment Based Rotor Position Offset Error Suppression Method for Sensorless PMSM Drives

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position offset error (RPOE) caused by the cross-saturation effect and other factors in high-frequency signal injection based methods, a current vector angle adaptive adjustment based online compensation method is proposed. This method can accurately detect and compensate the RPOE online only using two current vectors and has low sensitivity to the motor parameters, which improves the robustness of the system. Additionally, the two current vector angles can be adaptively adjusted according to the load conditions to improve the signal-to-noise ratio and reduce the current amplitude fluctuation during the RPOE detection process. Since neither offline measurement nor finite-element analysis is required, this method has superior adaptability and universality for applications. The effectiveness and feasibility are verified by experiments on a 2.2-kW interior PMSM drive platform.

Research about the Sensorless Vector Control of Permanent Magnet Synchronous Motor Based on Two-stage Filter Sliding Mode Observer

In order to overcome the disadvantages of installing mechanical sensors and the problems of chattering and low observation accuracy in traditional sliding mode observer sensorless control, a two-stage filter Sliding Mode Observer (SMO) is proposed in this paper. By collecting the current and voltage of the Permanent Magnet Synchronous Motor (PMSM), the SMO algorithm is realized by using the state equation of the motor in the synchronous stationary coordinate system; the position of the rotor is estimated by the arc tangent function, the observation accuracy of the rotor position is improved by increasing phase compensation; the variable cut-off frequency filter is introduced to make the Low Pass Filter (LPF) cut-off frequency can be self-adjusted with the change of rotational speed, which improves the estimation accuracy of rotor position at different rotational speeds. Kalman filter is introduced to form a two-stage filter with variable cut-off frequency LPF, which greatly weakens the chattering of the motor and reduces the observation error. Finally, the simulation is carried out under MATLAB/Simulink. The simulation results show that the PMSM vector control system with two-stage filter sliding mode observer has high estimation accuracy, good dynamic and steady-state performance, strong anti-jamming ability and robustness.

Improved sensorless control scheme for PMSM based on high-frequency square-wave voltage injection considering non-linear change of inductance in D-Q axis

Aiming at the position sensorless control system of permanent magnet synchronous motor (PMSM), an improved high-frequency square wave (HFSW) signal injection scheme considering nonlinear inductance variation in d-q axis is proposed and implemented in this paper. Compared with the existing model, the modified scheme can take the dynamic variation of d-q axis inductance under different operation conditions into consideration by offsetting the resulting high-frequency incremental (HFI) current harmonic components, thus effectively suppressing the estimation error of the rotor position. The experimental results verify that the improved scheme proposed in this paper can not only improve the estimation accuracy of rotor position impaired by the non-linear variations of d-q axis inductance but also maintain the good steady-state and dynamic performance of traditional schemes.