Search Results (361)

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications. Full article

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. Full article

This paper proposes an advanced sensorless control strategy for Permanent Magnet Synchronous Motors (PMSMs) aimed at enhancing dynamic performance, robustness, and reliability while eliminating the need for mechanical sensors. The core contribution lies in a novel hybrid speed regulation framework that combines a terminal sliding mode control scheme with a high-order super-twisting algorithm (HOT-STA-SMC), ensuring finite-time convergence, effective chattering suppression, and strong disturbance rejection under varying operating conditions. For the inner current loop, an Exponential Reaching Law Sliding Mode Controller (ERL-SMC) is implemented to guarantee fast current response and precise current tracking, even in the presence of parameter uncertainties. Furthermore, the conventional Model Reference Adaptive System (MRAS) observer is embedded within the proposed control architecture, resulting in more accurate speed estimation and enhanced stability during load fluctuations. The complete control system is rigorously modeled and tested in MATLAB R2024b/Simulink, capturing the full interaction between machine dynamics, control loops, and observer mechanisms. The simulation results verify that the proposed design achieves superior torque smoothness, minimal current ripples, and fast transient response compared to conventional sensorless methods. By integrating high-order sliding modes with advanced adaptive observation, this work offers a robust and cost-effective solution for high-performance PMSM drives, suitable for demanding applications such as electric vehicles, renewable energy conversion, and industrial motion control. Full article

To address the chattering phenomenon and sensitivity to load disturbances in conventional sliding mode observers (SMO) for sensorless permanent magnet synchronous motor (PMSM) control, this paper proposes a robust sensorless control strategy integrating a fuzzy adaptive SMO with an improved sliding mode speed controller. In the observer design, a continuous hyperbolic tangent function, $tanh \left(ax\right)$, replaces the traditional sign function, while a fuzzy logic controller adaptively tunes the convergence factor a to enhance estimation accuracy and suppress high-frequency chattering. Simultaneously, an adaptive quadrature phase-locked loop (AQPLL) is incorporated to achieve adaptive matching across various operating conditions by updating parameters online, which effectively reduces phase delay and improves the dynamic performance of rotor position and speed estimation. Furthermore, a non-singular terminal sliding mode control (NTSMC) strategy is employed in the outer speed loop with a proposed segmented terminal reaching law. This law ensures rapid response in large-error regions and mitigates steady-state oscillations in small-error regions, thereby strengthening system robustness against load disturbances. The stability of the proposed system is rigorously verified via Lyapunov stability analysis. Simulation and experimental results demonstrate that the proposed approach significantly reduces speed and position estimation errors under varying speeds and sudden load changes compared to the conventional SMO-PI method, while effectively suppressing system chattering to confirm its engineering feasibility. Full article

While walking robots possess significantpotential for various real-world applications, the reliance on high-performance sensors and complex control architectures for precise gait control remains a significant barrier to commercialization and lightweight design. To overcome these engineering limitations and lay the groundwork for a sensing paradigm adaptable to complex terrains, this study proposes an AI-based sensorless feedback control framework that incorporates the biological principles of proprioception. To this end, a walking robot leveraging the morphological intelligence of the Klann linkage was developed. We constructed a time-series dataset by defining motor current signals as ‘interoceptive sensing’ information—analogous to biological muscle feedback—and synchronizing them with absolute angular data. This dataset was used to train an Attention-LSTM (A-LSTM) model, which predicts future motor states in real-time by decoding nonlinear physical information embedded within internal current data, independent of external environmental sensors. By integrating the proposed model into a PI controller, a stable biomimetic walking loop was successfully implemented without the need for additional position sensors. Full article

To address the problem that the maximum error of back-EMF observers increases with an increase in motor speed, based on an extended state observer, this paper designs an angle-compensation strategy based on a proportional–integral controller and an extended state observer, according to the principle that a proportional–integral controller uses an integral link to eliminate steady-state error. After obtaining the back EMF, the proportional–integral phase-locked loop is often used to extract the angle and speed from the observed back EMF. However, this method will produce steady-state errors when the motor is accelerated, and the integration link is prone to overshoot, so it exhibits some defects. Therefore, this paper uses an extended state observer instead of a proportional–integral regulator to build an improved phase-locked loop based on an extended state observer. Full article

This paper deals with the problem of global sensorless speed control of permanent magnet synchronous motors in which the speed and load torque are not available. The key problem in sensorless speed control is the design of the observer that is used to estimate the speed and load torque. The major difficulty in the design of the observer results from the presence of the quadratic term of the speed and direct axis current in the quadrature axis current equation. We first view the quadratic term as a disturbance term. Then we design an adaptive feedback law in the direct axis current equation to reject the influence of the disturbance term on the observer. On the basis of the observation, we propose an observer-based adaptive global control method for the speed control of the motor. The method can ensure that all signals in the closed loop system are bounded and that the speed tracking error converges to zero. Simulations of a motor are performed to show the utility of the control method. Full article

In this paper, a sensorless field-oriented vector control (FOC) strategy combining an improved adaptive sliding mode observer (IASMO) and a finite-position-set phase-locked loop (FPS-PLL) is proposed for a surface permanent magnet synchronous motor (SPMSM) operating in the medium- and high-speed range. Firstly, a sliding mode observer (SMO) that can realize the observation of back electromotive force (back-EMF) is proposed, and an adaptive reaching law that can reduce the sliding mode coefficient is designed to help the SMO observe the back-EMF for the purpose of reducing chattering as well as verifying the stability of the system. Then, the FPS-PLL is used instead of a phase-locked loop (PLL) to extract the rotor position information from the observed back-EMF, thus avoiding the time-consuming process of tuning the PI parameters. The proposed FPS-PLL reduces the number of iterations from 64 to 20 while maintaining effective estimation performance. Finally, the effectiveness of the proposed scheme in suppressing chattering and maintaining comparable estimation accuracy while reducing computational burden is demonstrated by experiments. Full article

Nowadays, sensorless control of permanent magnet synchronous linear motors (PMLSM) is widely utilized in industrial applications due to its inherent cost and spatial advantages. However, existing sensorless control methods for PMLMs face insufficient observation accuracy of states and disturbances and poor variable-speed trajectory tracking. To address these issues, this paper proposes a sensorless control method combining multi-observer coordinated perception and robust adaptive control. Firstly, a sliding mode observer based on an improved saturation switching function is designed, which suppresses current noise with a low-pass filter to achieve unbiased estimation of back electromotive force (EMF). Secondly, an extended state observer with back-EMF as input is constructed to synchronously observe disturbances such as the mover speed, position, and thrust ripple of linear machine. Then, a robust adaptive controller is designed to compensate for system uncertainty via an adaptive law, forming closed-loop control with SVPWM. Compared with the traditional methods, the proposed multi-observer coordinated perception scheme can significantly enhance the observation accuracy of the mover speed, position, and lumped disturbances, and the robust adaptive controller can effectively improve the variable-speed trajectory, tracking performance under system uncertainties. Finally, the simulation results have confirmed the effectiveness of the proposed method in accurately observing and tracking speed and position, providing a feasible solution for high-precision sensorless control of PMLSM. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency and power density. To further enhance reliability and reduce cost and volume, sensorless control techniques have been extensively investigated over the past few decades. This article provides a review of major sensorless control methods, categorizing them into low-speed and high-speed methods. Virtual frequency methods, saliency-based methods, and model-based methods, along with their developments, are analyzed and compared. In addition, application-oriented analysis, implementation insights, as well as the challenges and future development trends are discussed at the end of the article. Full article

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB R2024b/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications. Full article

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies. Full article

This study introduces a sensorless control approach for permanent magnet synchronous motors (PMSMs) that employs an Improved Non-Singular Fast Terminal Sliding Mode Controller (IMNFTSMC) and an Improved Non-Singular Fast Terminal Sliding Mode Observer (IMNFTSMO). The IMNFTSMC employs a novel hybrid reaching law and a continuous piecewise square root switching function to achieve faster convergence and effective chattering reduction over the conventional Sliding Mode Controller (SMC). This design successfully replaces two critical components: the discontinuous constant velocity term (a key component of the traditional SMC reaching law that is a primary source of control chattering in PMSM torque regulation) and the high-gain exponential term (which tends to induce overshoot during transient speed adjustments and degrade steady-state control precision). In the IMNFTSMO, a hybrid approach combining linear and non-singular terminal sliding modes eliminates phase lag associated with low-pass filtering in traditional sliding mode observers, improving rotor position and speed estimation accuracy. Stability of both IMNFTSMC and IMNFTSMO is rigorously proven using Lyapunov stability theory.Validation through extensive simulations and hardware experiments, including challenging zero-speed start, speed stepping, and load disturbance tests, confirms the proposed strategy provides improved dynamic response, effective anti-disturbance capability, and high accuracy for rotor position and speed estimation compared to established benchmark methods, demonstrating its feasibility for mid-to-low speed sensorless PMSM drives. Full article

In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading to elevated failure rates; moreover, sensor installation introduces additional interfaces and wiring, thereby reducing system reliability. To address these issues, this paper proposes a sensorless control method based on an adaptive full-order sliding-mode observer (SMO). The proposed method employs the SMO output as the observer feedback correction term rather than the estimated back EMF, thereby avoiding substantial high-frequency noise. Furthermore, an S-shaped nonlinear function is designed to replace the conventional switching function, mitigating high-frequency chattering when the system operates in sliding mode; an adaptive sliding-mode gain function is designed, the sliding-mode gain and the boundary-layer thickness are adaptively tuned as a function of motor speed, which effectively enhances the back EMF estimation accuracy over a wide operating-speed range. The effectiveness of the proposed method is validated on a 2.3-kW PMSM experimental platform. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article