Search Results (100)

Squirrel-cage induction generators often perform better without a mechanical speed sensor. Eliminating an encoder or resolver removes one of the most fragile and failure-prone components, while modern control algorithms can estimate speed with sufficient accuracy. Shaft-mounted sensors are vulnerable to heat, vibration, dust, moisture, and electrical noise; they require precise mounting and additional cabling and typically fail long before the machine itself. In many industrial and marine applications, unplanned shutdowns are more often caused by damaged sensors or cables than by the generator. Unlike sensorless speed-detection methods developed for motoring operation, the proposed approach targets the generator mode, where both phase currents and the DC-link voltage are measured. It uses two indicators: the magnitude and sign of the active current, and the instantaneous rise in DC-link voltage when the converter output frequency matches the machine’s shaft speed. Because active current remains negative over a wide frequency range during start-up, its sign change alone cannot uniquely identify the synchronization point. In generator operation, however, the DC-link capacitor voltage provides an additional criterion: the speed at which power reverses sign, indicated by a change in the sign of the DC-voltage derivative. As the inverter frequency approaches the machine rotational frequency from below, the DC voltage increases, reaches a maximum at maximum slip, and then decreases once the inverter frequency exceeds the machine speed. The article demonstrates how these signals can be used in practice to identify the rotational speed of a squirrel-cage generator. Full article

Permanent magnet synchronous motors (PMSMs) are widely preferred in modern applications due to their high efficiency, high torque-to-inertia ratio, high power factor, and rapid dynamic response. Achieving optimal PMSM performance requires precise control, which depends on accurate estimation of motor speed and rotor position. This information is traditionally obtained through sensors such as encoders; however, these devices increase system cost and introduce size and integration constraints, limiting their use in many PMSM-based applications. To overcome these limitations, sensorless control strategies have gained significant attention. Since PMSMs inherently exhibit nonlinear dynamic behavior, accurate modeling of these nonlinearities is essential for reliable sensorless operation. In this study, a Runge–Kutta Extended Kalman Filter (RKEKF) approach is developed and implemented to enhance estimation accuracy for both rotor position and speed. The developed method utilizes the applied stator voltages and measured phase currents to estimate the motor states. Experimental validation was conducted on the dSPACE DS1104 platform under various operating conditions, including forward and reverse rotation, acceleration, low- and high-speed operation, and loaded operation. Furthermore, the performance of the developed RKEKF under load was compared with the conventional Extended Kalman Filter (EKF), demonstrating its improved estimation capability. The real-time feasibility of the developed RKEKF was experimentally verified through execution-time measurements on the dSPACE DS1104 platform, where the conventional EKF and the RKEKF required 47 µs and 55 µs, respectively, confirming that the proposed approach remains suitable for real-time PMSM control while accommodating the additional computational effort associated with Runge–Kutta integration. Full article

This paper presents a high-performance sensorless control strategy for induction motors using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for rotor speed estimation, eliminating the need for mechanical sensors. The ANFIS approach leverages stator voltages and currents, reducing costs and complexity. The motor is controlled via Indirect Stator Field Orientation Control (ISFOC) with a three-level Neutral–Point–Clamped (NPC) inverter employing Space Vector Modulation (SVM). Symmetry in the motor’s magnetic structure and SVM’s switching patterns enhances control precision, stability, and efficiency while minimizing harmonic distortion. Simulation results validate the proposed ANFIS-based estimator’s superior performance compared to a MRAS-based Luenberger observer under various operating conditions, demonstrating accurate speed tracking and robustness against load disturbances. Full article

(1) Background: Most robotic MIS platforms lack native haptic feedback, leaving surgeons to infer tissue loads from vision alone—an especially risky limitation in esophageal procedures. (2) Methods: We develop a sensorless, image-only force-estimation pipeline that maps endoscopic video to tool–tissue forces using a lightweight EfficientNetV2B0 CNN. The model is trained on 9691 labeled frames from in vitro esophageal experiments and validated against an FT300 load cell. For intraoperative feasibility, the system is deployed as a plug-in on PARA-SILSROB, consuming the existing laparoscope feed and driving a commercial haptic device. The runtime processes every 10th frame of a 60 FPS stream (≈6 Hz updates) with ~15–20 ms per-prediction latency. (3) Results: On held-out tests, the model achieves MAE = 0.017 N and MSE = 0.0004 N², outperforming a recurrent CNN baseline while maintaining real-time performance on commodity hardware. Integrated evaluations confirm stable operation at the deployed update rate and low latency compatible with closed-loop haptics. (4) Conclusions: By avoiding distal force sensors and preserving sterile workflow, the approach is readily translatable and retrofit-friendly for current robotic platforms. The results support the practical feasibility of real-time, sensorless force feedback for robotic esophagectomy and related MIS tasks, with potential to reduce tissue trauma and enhance operative safety. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

This paper presents adaptive sliding mode observers (A-SMOs) performing speed estimation for sensorless induction motor drives utilized in both industrial and electrical vehicle (EV) applications due to their computational simplicity. The fact that the constant switching gain (${\lambda }_0$) is used in conventional SMOs (C-SMOs) leads to the chattering problem, especially in low-speed regions. To tackle this issue, this paper proposes two different ${\lambda }_0$ adaptation mechanisms based on fuzzy and curve fitting methods. To estimate stator stationary axis components of stator currents and rotor fluxes together with the rotor speed, the proposed A-SMOs only utilize the measured stator currents and voltages of the IM. Here, the difference only between the estimated and measured stator currents is determined as the sliding surface in the proposed A-SMOs. To demonstrate the effectiveness of the proposed fuzzy-based A-SMO (FA-SMO) and curve fitting-based A-SMO (CFA-SMO), they are compared with C-SMO in real-time experiments for different scenarios including wide speed range operations of IM with/without load torque changes. Moreover, the stator and rotor resistances as well as the magnetizing inductance variations are also examined in real-time experiments of the proposed methods and the conventional one. The estimation results demonstrate how positively the ${\lambda }_0$ adaptations in FA-SMO and CFA-SMO affect the performance of C-SMO. Finally, two A-SMOs with improved performance are introduced and verified through real-time experiments. 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

Aiming at the commutation error in position sensorless control of brushless DC motors (BLDCMs) driven by four-switch three-phase (FSTP) inverters—caused by ignoring capacitor voltage fluctuations—this paper proposes a novel position sensorless control method based on voltage offset compensation. By independently performing PWM modulation on the switches of the non-capacitor-connected phases (Phase a and Phase b), the method suppresses three-phase current distortion. Meanwhile, it calculates the terminal voltages using switch signals and constructs a G(θ) function independent of the motor speed. Based on the voltage compensation amount derived in this paper, the influence of capacitor voltage fluctuations on this function is compensated. According to the relationship between the extreme value jump edges of the G(θ) function (after voltage compensation) and the commutation points, the accurate commutation signals required for motor operation are determined. The proposed strategy eliminates the need for filters, which not only avoids phase delay but also is suitable for motor rotor position estimation over a wider speed range. Experimental results show that compared with the uncompensated method, the average commutation error is reduced from approximately 18° to less than 3° electrical angle. Under different operating conditions, the proposed method can always obtain uniform commutation signals and exhibits strong robustness. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

This paper introduces a novel sensorless speed control strategy for squirrel-cage induction motors, which ensures robust operation in the presence of external disturbances by applying the state feedback technique. Based on the induction motor model, the speed controller is synthesized by defining a sliding variable that is driven to zero through the supertwisting control law, ensuring the stabilization of the tracking error. The time derivative of the error variable is estimated using a robust differentiator based on the sliding-mode twisting algorithm, thereby eliminating the need to estimate the load torque. A robust observer is employed to estimate the rotor speed and flux linkages simultaneously. The convergence of the estimated rotor flux linkages is enforced through a discontinuous first-order sliding-mode input, while the convergence of the rotor speed estimate is attained via a quasi-continuous super-twisting sliding-mode input. In the proposed model, the inductance parameters are determined from the magnetizing inductance and the leakage inductances of the stator and rotor. A procedure is also presented for adjusting the stator resistance and leakage inductances, taking into account the squirrel-cage rotor type and the skin effect in alternating current conduction. The performance of the sensorless speed control system under variations in load torque and reference speed is validated through experimental testing. The rotor speed estimation provided by the robust observer is accurate. The reference speed tracking control, evaluated using a 1600–1700 rpm pulse train phase-shifted by 4 s with respect to a 0–0.5 N·m pulse train, demonstrates high precision. Full article

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

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

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 study addresses the problem of DC-link current pulsations in four-wire AC/DC converters with energy storage operating under unbalanced load conditions. A sensorless compensation algorithm based on AC-side voltage and current measurements is proposed, eliminating the need for additional sensors. The algorithm incorporates a Second Order Generalized Integrator (SOGI) filter for accurate detection and compensation of the pulsating component. Experimental validation under severe asymmetry confirmed the method’s effectiveness. In case 1, the AC component of the DC-link current was reduced from 7 A to 1.4 A and, in case 2, from 3 A to 0.5 A. Corresponding FFT analysis showed a reduction in relative amplitude from 240% to 21.5% and from 264% to 22%, respectively. In an asymmetrical charging scenario (case 3), the AC component was reduced from 2.5 A to nearly 0 A, corresponding to a decrease from 42% to 4.9% in the FFT spectrum. These results demonstrate that the proposed method enables stable converter operation even under deep phase current imbalances, significantly improving energy storage reliability and utility grid performance. Full article