Search Results (54)

This paper presents a comprehensive sensorless control approach for interior permanent magnet (IPM) motors, integrating high-frequency injection (HFI) and model-based observer techniques to ensure accurate rotor position estimation across a wide speed range. Two HFI strategies—pulsating and rotating—are investigated experimentally and compared in combination with two observer structures: the conventional Sliding Mode Observer (SMO) and Adaptive-Gain SMO (AG-SMO). The AG-SMO dynamically adjusts its observer gain according to the estimated back-electromotive force (back-EMF) amplitude, significantly reducing chattering and improving estimation performance under varying load and noise conditions. A Frequency-Adaptive Complex Coefficient Filter (FACCF) and an Orthogonal Phase-Locked Loop (PLL) are incorporated to eliminate phase delay and enhance demodulation accuracy. Simulation and experimental results obtained using a 30 W, 20 V IPM motor demonstrate that the pulsating HFI + AG-SMO configuration achieves superior stability and noise immunity, while the rotating HFI + AG-SMO provides smoother and more accurate estimation. Overall, the proposed hybrid control framework achieves robust, high-precision, and sensorless operation for IPM motors over the wide speed range, offering a practical solution for applications such as inverter-driven compressor systems operating in noisy environments. Full article

This paper presents a sensorless control strategy for permanent magnet synchronous motor (PMSM) drives in industrial and automotive air compressor applications. The strategy utilizes an adaptive-gain sliding mode observer integrated with a refined back-EMF model to suppress chattering and improve convergence. The proposed approach achieves precise rotor position and speed estimation across a wide operational range without mechanical sensors. It directly addresses the critical needs of reliability, compactness, and resilience in automotive environments. Unlike conventional observers, its originality lies in the enhanced gain structure, enabling accurate and robust sensorless control validated through both simulation and hardware tests. Comprehensive simulation results demonstrate effective performance from 2000 to 8500 rpm, with steady-state speed tracking errors maintained below 0.4% at 2000 rpm and 0.035% at 8500 rpm under rated load. The control methodology exhibits excellent disturbance rejection capabilities, maintaining speed regulation within ±5 rpm under an 80% load disturbance at 8500 rpm while limiting q-axis current ripple to 2.5% of rated values. Experimental validation on a 2.2 kW PMSM-driven compressor test platform confirms stable operation at 4000 rpm with speed fluctuations constrained to 20 rpm (0.5% error) and precise current regulation, maintaining the d-axis current within ±0.07 A. The system demonstrates rapid dynamic response, achieving acceleration from 1320 rpm to 2365 rpm within one second during testing. The results confirm the method’s practical viability for enhancing reliability and reducing maintenance in industrial and automotive compressors systems. 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

This paper proposes an anti-disturbance model predictive fault-tolerance control strategy for open-circuit faults of five-phase flux intensifying fault-tolerant interior permanent magnet (FIFT-IPM) motors. This strategy is applicable to electric agricultural equipment that has an open winding failure. Due to the rich third-harmonic back electromotive force (EMF) content of five-phase FIFT-IPM motors, the existing model predictive current fault-tolerant control algorithms fail to effectively track fundamental and third-harmonic currents. This results in high harmonic distortion in the phase current. Hence, this paper innovatively proposes a multi-plane virtual vector model predictive fault-tolerant control strategy that can achieve rapid and effective control of both the fundamental and harmonic planes while ensuring good dynamic stability performance. Additionally, considering that electric agricultural equipment is usually in a multi-disturbance working environment, this paper introduces an adaptive gain sliding-mode disturbance observer. This observer estimates complex disturbances and feeds them back into the control system, which possesses good resistance to complex disturbances. Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental results. 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

Precise speed estimation of sensorless induction motor (SIM) drives remains a significant challenge, particularly at zero and very low speeds. This paper proposes a mathematically modeled and enhanced stator current-based Model Reference Adaptive System (MRAS) estimator integrated with correction terms using rotor flux dynamics to continually update the value of the estimated speed to the correct value. The MRAS observer uses the stator current in the adjustable IM model instead of the rotor flux or the back emf to eliminate the effect of pure integration of the rotor flux, the parameters’ deviation, and measurement errors of stator voltages and currents on speed observation. It depends on the observed stator current, the current estimate error, and rotor flux estimation correction terms. A neural network (NN) for the adaptive law of the MRAS observer is proposed to enhance the accuracy of the suggested approach. Simulation results examine the developed method. A laboratory prototype based on DSP-DS1103 was also built, and the experimental results are presented. The SIM drive is examined at zero and very low speeds in motoring and regenerating modes. It exhibits good dynamic performance and low-speed estimation error compared to the conventional MRAS. Full article

This paper proposes a sensorless field-oriented control (FOC) strategy for permanent magnet synchronous motors (PMSMs), focusing on rotor flux position estimation based on back-electromotive force (back-EMF) signals. The limitations of conventional phase-locked loop (PLL) techniques for rotor flux position estimation along the motor shaft are analyzed, and an enhanced PLL structure is developed to address these deficiencies.In electric vehicle traction applications, precise flux position estimation alone is insufficient; accurate generation of d–q-axis current commands is equally critical. To address this need, a zero-pole-free PI regulator is designed within the PLL module, enabling more accurate flux estimation. Additionally, a gradient-based self-tuning algorithm is employed to identify system parameters, particularly the stator inductance, enabling the controller to optimize current command generation.Comprehensive system-level simulations have been conducted to validate the effectiveness of the proposed sensorless control scheme. Comparative studies demonstrate that the proposed method significantly improves feasibility and robustness for practical PMSM drive applications. Full article

In this study, a sensorless vector control method was proposed for a doubly fed induction machine (DFIM), where the stator is directly connected to the grid. The DFIM is a three-phase symmetric system without saliency, and when the stator side is directly connected to the grid, the magnitude and frequency of the stator flux are almost fixed and determined by the grid voltage. Due to its three-phase symmetric configuration, this structure can be modeled in a manner similar to that of a symmetric permanent-magnet synchronous motor (PMSM). It enables the application of back-EMF-based sensorless control methods commonly used for symmetric PMSMs. In PMSMs, sensorless estimators typically estimate the back-EMF using only stator voltage and current measurements. By extending this modeling concept to DFIMs, a similar estimator can be designed that utilizes only rotor-side voltage and current for sensorless back-EMF estimation. This paper proposes a back-EMF estimator using only rotor-side voltages and currents, which were implemented on a stator flux reference frame. The proposed algorithm also estimates the stator-side variables, including the magnitudes of stator voltage, current, and stator power factor. These variables can be used to detect grid faults. The feasibility of the proposed method was validated via experiments using a 2.4 kW DFIM. It was confirmed that the sensorless operation functioned properly even during speed acceleration/deceleration and step load conditions. Additionally, the system maintained stable operation and achieved an accurate estimation of stator voltage and current, even under a 30% voltage sag in the stator grid voltage. Full article

This paper presents an enhanced sensorless control strategy for permanent magnet synchronous motors (PMSMs) by improving back-electromotive force (back-EMF) estimation and control robustness. An improved back-EMF extended state observer (ESO) is proposed, incorporating back-EMF differentiation to compensate for DC position error without requiring an increased observer bandwidth. Furthermore, an ESO-based quadrature phase-locked loop (QPLL) is developed to improve position tracking accuracy and enhance the robustness of the speed loop sliding mode controller (SMC) against unknown disturbances. To address parameter uncertainties in the back-EMF observer and current controller, a recursive least squares (RLSs) algorithm with an adaptive forgetting factor is introduced, providing a balance between adaptation speed and noise suppression. Simulation results validate the proposed approach, demonstrating improved estimation accuracy, disturbance rejection, and overall robustness in sensorless PMSM control. Full article

Since the introduction of robot-assisted laparoscopic surgery, efforts have been made to incorporate force sensing technologies to monitor critical components and to provide force feedback. The advanced laparoscopic robotic system (AdLap RS) is a robotic platform that aims to make robot technology more sustainable through the use of the fully reusable shaft-actuated tip-articulating (SATA) instruments. The SATA instrument driver features electronics and sensors exposed to the sterile environment, which complicate the sterilisation process. The aim of this study was to develop and validate smart sensing in stepper motors using the back electromotive force in a newly developed Smart SATA Driver (SSD), eliminating the need for sensors in the sterile environment. Methods: The stepper drivers were equipped with TMC2209 ICs featuring StallGuard technology to measure back EMF. The tip was actuated up until a set StallGuard threshold value was reached, at which the resulting tip force was measured. This cycle was repeated ten times for a range of threshold levels. A regression analysis with a power series model was used to determine the quality of the fit. Results: The SSD is capable of exerting tip forces between 2.4 and 8.2 N. The back EMF force test demonstrated a strong correlation between obtained StallGuard values and measured tip forces. The regression analysis showed an R-squared of 0.95 and a root Mean squared error of 0.4 N. Discussion: The back EMF force test shows promise for force feedback, but its accuracy limits real-time use due to back EMF fluctuations. Future improvements in motor stability and refining the back EMF model are needed to enable real-time feedback. Conclusion: The strong correlation during the back EMF force test shows its potential as a low-budget method for detecting motor stalls and estimating tool–tissue forces without the need for sensors in laparoscopic instruments. Full article

In response to the chattering issue inherent in sliding mode observers during rotor position estimation and to enhance the stability and robustness of sensorless control systems for surface-mounted permanent magnet synchronous motors (SPMSM), this study proposes a rotor position estimation algorithm for SPMSM based on an improved super-twisting sliding mode observer (ISTSMO) and a second-order generalized integrator (SOGI) structure. Firstly, the super-twisting algorithm is introduced to design the observer, which effectively attenuates the sliding mode chattering by using continuous control signals. Secondly, SOGI is introduced in the filtering stage, which not only effectively addresses the time delay issues caused by traditional low-pass filters but also enables the observer to extract rotor position information by monitoring only the back electromotive force (back-EMF) signal of the $\alpha$-phase, thereby simplifying the observer structure. Finally, the proposed scheme is experimentally compared with the traditional sliding mode observer on the YXMBD-TE1000 platform. The experimental results showed that during motor acceleration and deceleration tests, the average speed estimation error was reduced from 141 r/min to 40 r/min, and the maximum position estimation error was reduced from 0.74 rad to 0.29 rad. In load disturbance experiments, the speed variation decreased from 781 r/min to 451 r/min, and the steady-state speed fluctuation was significantly reduced. These results confirm that the proposed observer exhibits superior stability and robustness. Full article

Position sensorless control has been widely used in permanent magnet synchronous motor (PMSM) drives in low-cost applications or in the fault-tolerance control of position sensors. Conventional sensorless control methods often adopt a back electromagnetic force (EMF)-based position observer, which results in bandwidth reduction in signal processing and lower estimation accuracy. This paper introduces a numerical solution based on transmission line modeling (TLM) to obtain the back EMF. The TLM method is used for the numerical calculation of electromagnetics due to the clear algorithm structure, robust convergence and stability, and easy implementation in dynamic circuit analyses. This paper first analyzes the 2D TLM method techniques. Then, a new application of TLM theory in position sensorless control of PMSMs is put forward. The proposed TLM-based sensorless control scheme can estimate the back EMF without decreasing the bandwidth, thereby enhancing the dynamic performance of the sensorless control. All numerical results are implemented using the proposed approach, which validates the effectiveness of the proposed method. Full article

This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations of existing control methods at low speeds and under dynamic conditions. The study replaces the conventional arc-tangent technique with a QPLL-based approach, eliminating low-pass filters to enhance system adaptability and reduce delays. The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. Furthermore, the performance of a modified single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) in solar-powered LEVs is verified, minimizing current ripple and ensuring smooth motor operation. The system also incorporates a regenerative braking mechanism, extending the vehicle’s range by efficiently recovering kinetic energy through the battery with 30.60% efficiency. The improved performance of the proposed method and system over conventional approaches contributes to the advancement of efficient and sustainable solar-powered BLDC motor-based EV technologies. Full article

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems. Full article

The Sliding Mode Observer (SMO) is widely used for the sensorless control of Permanent-Magnet Synchronous Motors (PMSMs) due to its simple structure and strong parameter robustness. However, traditional SMOs have a limited speed range and suffer from chattering issues, which affect the accuracy of rotor position estimation. To address these problems, this paper proposes an Adaptive Super-Twisting SMO (AST-SMO) method. First, a fast super-twisting function is designed to resolve the step problem that occurs at the zero-crossing of the traditional sign function. Next, an adaptive-tracking high-order Sliding Mode Observer is constructed to extend the speed range of the SMO. The stability of the system is proven using the Lyapunov theorem. Finally, a sensorless control system for PMSMs is implemented and validated in MATLAB/SIMULINK. The results indicate that, compared to the traditional SMO, the AST-SMO reduces the back EMF THD from 20.03% to 14.2%. Additionally, the rotor estimation error across all speed ranges is less than 0.01. Therefore, AST-SMO offers a higher tracking accuracy, a wider speed range, and effectively suppresses sliding mode chattering and harmonic interference. Full article