Search Results (258)

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

The transient performance of sensorless control for interior permanent magnet synchronous motors (IPMSMs), based on back-electromotive force (back-EMF) estimation, is a critical factor in ensuring the high reliability of motor drive systems. Although rotor speed and position can be accurately estimated under steady-state conditions, estimation errors tend to increase during transient states such as acceleration, deceleration, and load torque variations. The enhancement of transient stability is closely related to the overshoot in the estimated position and speed errors. In this paper, the maximum overshoot of the estimated position and speed errors during transient operation is analyzed. Furthermore, compensation strategies are proposed to reduce the magnitude of these overshoots. The effectiveness of the proposed sensorless control method is validated through comparative analysis with existing approaches. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

To eliminate the dependence of the induction motor (IM) flux-oriented control system on position sensors, IM sensorless control based on a full-order state observer is studied in this paper. First, according to the IM rotor flux linkage models of current and voltage, the speed of the full-order state observer for IM and the solution for the feedback matrix are designed. Then, to simplify the expression of the feedback matrix and improve the stability of the observer for high-speed operation, a novel solving method of the feedback matrix by left-shifting the poles of the observer is proposed, and the terms containing rotor speed are simplified. On this basis, a speed estimation method based on the current error and Lyapunov theory is proposed. For low-speed operation, the feedback matrix parameter design method is proposed based on the stability conditions of d–q axis current error model. Finally, the feasibility and effectiveness of the proposed full-order state observer are verified by simulation and experiment. Since the improved full-order state observer can provide accurate speed feedback and rotor flux position for flux-oriented vector control systems, the IM drive system exhibits good steady-state and dynamic performance. Full article

This paper introduces a speed-sensorless dual-field-oriented control (DFOC) strategy for induction motors (IMs). DFOC combines the advantages or rotor- and stator-field orientation to significantly reduce the parameter sensitivity of the control regarding the generation of the converter control variable. A simplified structure is also proposed, using only two regulators for the flux and speed control, eliminating the two current regulators. Related to sensorless control, the classical adaptation mechanism within an MRAS (model reference adaptive system) observer is replaced with artificial intelligence (AI)-based approaches. Specifically, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are employed for rotor speed estimation. They offer significant advantages in managing complex and nonlinear systems, providing enhanced flexibility and adaptability compared to traditional MRAS methods. The effectiveness of the proposed sensorless control scheme is validated through both simulation and real-time implementation. The paper focuses on the ANN and RNN architectures, as deep learning models, in terms of the reliability and accuracy of rotor speed estimation under various operating conditions. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

This paper presents the application of a sliding mode observer for speed sensorless control of a six-phase induction machine. The use of nonlinear sliding mode techniques yields acceptable performance for both low- and high-speed motor operations over a wide speed range. The effectiveness and accuracy of the developed sensorless scheme are verified by experimental results, which demonstrate the system’s performance under various operating conditions. These results demonstrate the advantages of the proposal as a valid alternative to the conventional method, which uses a mechanical speed sensor for multiphase machines. Additionally, the sensorless approach can also serve as a redundant backup in the event of mechanical sensor failure, thereby increasing the reliability of the overall drive system. Full article

This paper presents a novel adaptive twisting sliding mode control strategy combined with a speed-sensorless cascade nonlinear observer for the high-performance control of linear induction motors (LIMs). The primary objective is to achieve accurate speed and rotor flux tracking without relying on mechanical sensors, thereby enhancing system reliability and reducing hardware complexity. For this purpose, a cascade nonlinear observer is designed and applied to the class of nonlinear affine systems representing LIM dynamics. Based on the interconnected form of the LIM mathematical model, the observer simultaneously reconstructs both the motor speed and rotor fluxes in real time. The stability of the proposed cascade observer is analyzed using Lyapunov theory, ensuring the convergence of the estimation errors under bounded disturbances. Complementing the observer, two adaptive gain twisting sliding mode controllers are developed: one for speed tracking and another for flux regulation. These controllers are robust against external disturbances and parameter uncertainties, even when the bounds of such disturbances are unknown. This feature significantly enhances the practical applicability of the control system in real-world industrial environments. To validate the performance and robustness of the proposed control scheme, a hardware-in-the-loop (HIL) experiment was conducted. Comparative studies with existing state-of-the-art sensorless control methods demonstrate that the proposed cascade nonlinear observer-based approach achieves faster convergence, higher estimation accuracy, and better disturbance rejection capabilities, while requiring less computational effort. Full article

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this established accurate discrete motor model, the influence of low CFR on the phase estimation error of back electromotive force (EMF) is analyzed. Building on this foundation, an accurate discrete Luenberger observer (ALO) is designed, and a corresponding phase compensation control method is proposed. A motor drive control system comprising hardware, software, and experimental test setups is constructed. The experimental results demonstrate that, compared to the Euler model, the discrete mathematical model established by this method significantly improves position observation accuracy under low CFR conditions. Furthermore, compared to the traditional Luenberger observer (TLO), the estimation error of the proposed observer under a low CFR is reduced by approximately 85%. This approach exhibits high application value in the sensorless control of high-speed and high-frequency motors. Full article

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position observer for estimating the rotor position. Generally, this process relies on band-pass filters (BPFs) and low-pass filters (LPFs) to modulate signals in the quadrature current to obtain rotor position error information. However, limitations in filter accuracy and dynamic response lead to prolonged convergence times and timing inconsistencies in the estimation process, which affects real-time motor control performance. To address these issues, this study proposes an exponential moving average (EMA)-based scheme for rotor position error extraction, offering a rapid response under dynamic conditions such as direction reversals, step speed changes, and varying loads. EMA is used to pass the original rotor position information carried by the quadrature current signal, which contains HF components, with a specified smoothing factor. Then, after the synchronous demodulation process, EMA is employed to extract rotor position error information for the position observer to estimate the rotor position. Due to its computational simplicity and fast response in handling dynamic conditions, the proposed method can serve as an alternative to BPF and LPF, which are commonly used for rotor position information extraction, while also reducing computational burden and improving performance. Finally, to demonstrate its feasibility and effectiveness in improving rotor position estimation accuracy, the proposed system is experimentally validated by comparing it with a conventional system. Full article

To enhance the robustness of sensorless control in permanent magnet synchronous motors (PMSMs) under parameter mismatches, this paper proposes a novel sliding mode observer (SMO) that automatically adjusts the error factor. The purpose is to enable the precise observation of rotor position in PMSMs while simultaneously suppressing chattering and simplifying the design process. First, an SMO based on an adjustable error factor is designed, which reduces chattering and eliminates the need for a low-pass filter (LPF). The impact of the error factor within the SMO is then analyzed, including its effects on the estimation of current, speed, and position, and a method for determining the error factor based on these estimated values is introduced. This method uses a neural network algorithm to balance chattering suppression with high control accuracy. Finally, a neural network-based self-adjusting SMO model is proposed to automatically adjust the error factor based on motor operating conditions. Simulation and experimental results demonstrate the feasibility and effectiveness of this approach. Full article

The application fields of high-speed submersible permanent magnet synchronous motors (PMSM) are constantly expanding. Especially in high-risk and complex environments such as oil exploration, offshore oil exploitation, and deep well operation, the reliability, stability, and efficiency of motor drive systems are more and more prominent. The submersible motor is greatly affected by load disturbance, pressure change, and external oil flow, and the traditional method may not perform well in complex disturbance problems. Therefore, a three-order adaptive nonlinear extended state observer is proposed to collect the input and output information of the system in real time, and estimate the motor speed, position, and total disturbance. A linear feedback control law is designed to eliminate the disturbance. The superiority of the proposed algorithm under complex operating conditions is verified by the Simulink model and experiments, which provide a theoretical basis for the control of submersible motors. Full article

The sensorless control of doubly fed induction machine (DFIM) rotor magnetic flux based on a model reference adaptive system (MRAS) is proposed to improve the reliability of a large-scale variable-speed pumped storage (VSPS) system and reduce operation and maintenance costs. The existing sensorless control of doubly fed induction machines (DFIMs) is mostly focused on generator operation, making it difficult to apply to the VSPS system. The proposed strategy realizes the reversible operations of the VSPS through the design of an adaptive law under variable operating conditions, eliminating mechanical sensors, and possessing the characteristics of simple implementation and accurate identification. The mathematical model of the DFIM in a VSPS system is constructed, and an MRAS vector control strategy based on stator voltage orientation is established. The rotor angle and speed under reversible operating conditions are effectively identified by dynamically adjusting the angle error between the rotor flux reference model and the adaptive model to approach zero. Subsequently, comparative analysis with the closed-loop direct detection method verifies the advantages of the proposed strategy. The proposed control method can accurately identify rotor position and speed in the pumping and power generation conditions of the VSPS system and it demonstrates robust adaptability. Full article

High-frequency injection (HFI) is widely adopted for the sensorless control of permanent magnet synchronous motors (PMSMs) at low speeds. However, conventional HFI strategies relying on fixed-frequency carrier modulation and square-wave injection concentrate current harmonic energy within narrow spectral bands, thereby inducing pronounced high-frequency motor vibrations and noise. To mitigate this issue, this paper proposes a noise suppression strategy based on synchronized periodic frequency modulation (PFM) of both the carrier and high-frequency square-wave signals. By innovatively synchronizing the periodic modulation of the triangular carrier in space vector pulse width modulation (SVPWM) with the injected high-frequency square wave, harmonic energy dispersion and noise reduction are achieved, substantially lowering peak acoustic emissions. First, the harmonic characteristics of the voltage-source inverter output under symmetric triangular carrier SVPWM are analyzed within a sawtooth-wave PFM framework. Concurrently, a harmonic current model is developed for the high-frequency square-wave injection method, enabling the precise derivation of harmonic components. A frequency-synchronized modulation strategy between the carrier and injection signals is proposed, with a rigorous analysis of its harmonic suppression mechanism. The rotor position is then estimated via high-frequency signal extraction and a normalized phase-locked loop (PLL). Comparative simulations and experiments confirm significant noise peak attenuation compared to conventional methods, while position estimation accuracy remains unaffected. This work provides both theoretical and practical advancements for noise-sensitive sensorless motor control applications. Full article