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To address the challenges of diminished back-EMF, high noise interference, and reduced accuracy in traditional low-speed sensorless control, this study proposes a rotor position estimation method based on structural vibration characteristics. The coupling mechanism between air-gap electromagnetic force density and stator structural vibration modes is analyzed. This analysis reveals that rotor spatial information is embedded within specific modal phases, establishing the physical basis for utilizing vibration phase as a position carrier. Accordingly, a workflow encompassing signal acquisition, modal selection, and phase calculation is developed and integrated into a sensorless control system. Simulation results demonstrate that the proposed method achieves stable estimation even under strong noise. The estimation error shows clear performance advantages over conventional back-EMF-based methods in the low-speed region, validating its effectiveness and robustness at low speeds. This research provides a new approach that introduces non-electrical structural information as a complementary channel to overcome the inherent limitations of electrical-signal-based position estimation at low speeds.

This paper reviews the application of Hall position sensors in control and position estimation of permanent magnet synchronous motors (PMSMs). Accurate rotor position and motor speed data are essential for the high-efficiency control of PMSMs in modern industry. Rotor position and motor speed can be measured by mechanical position sensors, which are costly and less reliable, or by rotor position observers, which are sensitive to system models and changes in motor parameters. This paper examines the benefits, limitations, challenges, and uses of low-resolution Hall position sensors in PMSM drives, presenting them as a cost-effective solution for achieving a balance between performance and expense. In addition, the paper discusses recent solutions to issues related to misplaced Hall position sensors and fault-tolerant control algorithms, and gives an outlook to future developments in this area.

This article presents an estimation method of the BLDC rotor position with asymmetrically arranged Hall sensors. Position estimation is necessary to control the motor by methods other than block commutation. A sinusoidal control method was selected for the research, which significantly reduces torque ripples and acoustic noise and is quite simple to implement. Inaccurate performance of the elements determining the position of the BLDC motor rotor causes a large error in the position estimation and has a negative impact on the operation of the drive controlled in this way. Using the developed control algorithms, it is possible to correctly determine the mechanical position of the rotor even for multi-pole motors. The proposed method is relatively easy to implement and does not require modification of control systems, being limited to changes only in the software of such devices. The tests of the actual system clearly show the usefulness of such a control method and its effectiveness.

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.

To address the issue of decreased rotor position estimation accuracy in permanent magnet synchronous motors (PMSMs) caused by linearization rounding errors in the extended Kalman filter (EKF), this paper proposes a rotor position estimation method for PMSMs based on higher-order extended Kalman filtering. This method relies on the state-space equations of a PMSM in a stationary coordinate system and establishes a higher-order Taylor series expansion based on the least squares approach. It constructs a prediction and update model for the state variables using the higher-order Taylor series expansion and designs an algorithm for estimating the rotor position of PMSMs based on higher-order extended Kalman filtering. The simulation results indicate that, compared to the EKF, the proposed method reduces the root-mean-square error by 10%.

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.

Due to the characteristics of inductance parameter mismatch and back electromotive force harmonics caused by novel leakage flux branches and other non-ideal factors for the variable-leakage-flux permanent magnet (VLF-PM) motor, its control system suffers from a deteriorated performance of the rotor position estimation. To overcome the problems mentioned above, an adaptive tracking estimator of the rotor position is proposed in this paper for the VLF-PM motor control system. First, the proposed method simplifies the VLF-PM motor mathematical model and reduces the effect of inductance parameter variations according to the active flux concept. Then, robust and gradient descent algorithms are utilized to maintain the robustness of inductance parameter variations and eliminate the specific order harmonics owing to the novel leakage flux branches. Meanwhile, the accuracy and stability are enhanced. Furthermore, the position compensation based on the current adaptive tracking strategy is proposed to compensate the rotor position error caused by other non-ideal factors. Finally, the feasibility of the proposed estimated system is verified.

This article proposes a control scheme to compensate for rotor position estimation error in sensorless control that arises from sudden load torque changes during the low-speed operation of washing machines. The load torque characteristics, generated by the movement of laundry and water in the washing machine, vary as the speed changes. In particular, at a low speed of approximately 50 rpm, which corresponds to the washing stroke, the centrifugal force acting on the laundry weakens. This causes the laundry to move irregularly, resulting in rapid torque fluctuations. The proposed scheme formulates a rotor speed compensation term to reflect the load torque variations. This compensation term is incorporated into the conventional back-electromotive-force (back-EMF) based sensorless control to reduce rotor position estimation error. The proposed scheme enhances control stability in transient states during low-speed operation while maintaining a low computational burden. The effectiveness of the proposed scheme is verified through simulations and experiments using a surface-mounted permanent magnet synchronous motor (SPMSM) designed for washing machines.

High-frequency signal injection (HFI) is widely used for sensorless motor control but has mostly been studied in machines with high saliency ratios. In outer-rotor permanent magnet-assisted synchronous reluctance motors (OR-PMaSynRMs), the external rotor extends the magnetic flux path and increases the symmetry of the reluctance barriers. This reduces the inductance difference between the dq-axes, thereby lowering the saliency and weakening the effectiveness of conventional HFI methods in position estimation. Hence, advanced sensorless control strategies are required for such motors. This study presents an adaptive HFI control strategy that combines frequency, amplitude and filter adaptation with a proposed voltage limiting mechanism. The algorithm has been experimentally implemented for the first time on an OR-PMaSynRM. The results confirm the applicability of the HFI technique to low-saliency motors and its ability to provide reliable and robust sensorless control.

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of 1.77°2, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs.