Search Results (552)

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

This manuscript presents a robust formation and collision-free containment control system designed for a quadrotor fleet operating under turbulent wind conditions. Emphasizing collision avoidance, we introduce a two-layer strategy in which a virtual leader defines a trajectory, and leaders and followers maintain their positions while avoiding collisions among them. A graph convention is used to illustrate the roles of leaders and followers, as well as their interactions. Inter-agent collision avoidance is proposed by expanding the desired distance relative to all neighboring agents, thereby guaranteeing the convergence stage. Moreover, the approach employs a class of adaptive sliding mode strategies to ensure finite-time convergence, as well as non-overestimation of the control gain in the presence of uncertainties and perturbations. A stability analysis demonstrates the practical finite-time stability of the system using the Lyapunov methodology. Results from the simulation underscore the effectiveness of our proposal in adhering to the desired time-varying trajectories and ensuring sensor-less inter-agent collision avoidance for the followers, even in the presence of turbulent wind conditions. Full article

This research paper presents the design and implementation of a wall-climbing robot for safety-critical inspection systems. The robot incorporates wheels embedded with neodymium magnets and a rocker-bogie mechanism to navigate vertical and inverted surfaces. The key novelty of this work lies in the use of a simplified, sensorless rocker-bogie mechanism that enables smooth inner and outer transitions without depending on complex control systems. This study addresses the following research questions: (1) How can a wall-climbing robot achieve stable transitions using a rocker-bogie mechanism? (2) What is the maximum payload capacity of the robot without compromising mobility and stability? (3) How will the robot behave during obstacle climbing? Weighing 2.08 Kg, the robot can easily carry a payload of 1.56 Kg, and can climb obstacles of up to 20 mm. The robot system is controlled wirelessly via a Bluetooth module. During experimental testing, the robot performed different types of transitions with stability and reliable control. Future developments could include hybrid adhesion systems for unstructured situations and AI-assisted navigation. Full article

This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids. 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

This paper introduces an open-source software framework for implementing Field-Oriented Control (FOC) on a Brushless DC Motor (BLDC) across its entire speed range. The control strategy employs a Direct FOC method with a single Hall sensor combined with Space Vector Pulse Width Modulation (SVPWM) and complementary sensorless techniques. The BLDC motor and supporting circuits are modeled and validated through both simulation and hardware implementation. A modular software architecture enables deployment via distinct system components, promoting hardware abstraction and reducing platform-specific dependencies. The entire setup is conceptualized and executed in MATLAB/Simulink R2024b and the framework supports remote experimentation through a web-based interface, requiring only a single MATLAB license. This scalable solution is designed for academic researchers and industry practitioners alike, offering an accessible low-cost platform for motor control development, validation, and early-stage prototyping. Full article

Consistent volumetric flow control is essential in extrusion-based additive manufacturing, particularly when printing viscoelastic materials with complex rheological properties. This study proposes a control framework incorporating simplified rheological dynamics via a Kelvin–Voigt model that integrates nonlinear dynamic modeling, an unknown input observer (UIO), and a closed-loop PID controller to regulate material flow in a motorized electro-pneumatic extrusion system. A comprehensive state-space model is developed, capturing both mechanical and rheological dynamics. The UIO estimates unmeasurable internal states—specifically, syringe plunger velocity—which are critical for real-time flow regulation. Simulation results validate the observer’s accuracy, while experimental trials with a curing silicone resin confirm that the system can achieve steady extrusion and maintain stable linewidth once transient disturbances settle. The proposed system leverages a dual-mode actuation mechanism—combining pneumatic buffering and motor-based adjustment—to achieve responsive and robust control. This architecture offers a compact, sensorless solution well-suited for high-precision applications in bioprinting, electronics, and soft robotics, and provides a foundation for intelligent flow regulation under dynamic material behaviors. Full article

A reluctance actuator integrated into the double-sided dog clutch of a gearbox can significantly simplify the gear shifting system. However, its disadvantage is that an axial position sensor is required to shift the neutral gear. The sensor is placed in the aggressive environment of a gearbox and reduces the reliability of the entire system. Sensorless methods proposed in the literature deal with electrical machines or actuators with one degree of freedom (linear motion or rotation). In the dog clutch, the shift sleeve rotates and moves along its rotation axis simultaneously, moreover, the coil inductances are highly dependent not only on the axial position but also on the relative angular position between the shift sleeve teeth and the slots of its counterpart. This work proposes an original algorithm of sensorless control, which main novelty is the applicability for systems with two degrees of freedom, such as the considered actuator. The voltage induced in one of the coils and the prediction of the shift sleeve motion, which is based on the electromechanical model of the clutch, are used to control the currents. Not only an axial position sensor but also angular encoders are not required to apply the proposed method. The algorithm was tested both in simulations and experiments under different conditions. The results show that the proposed method allows to shift the neutral gear sensorless at different rotation speeds and different loads on the sleeve, regardless of what gearwheel is initially engaged. Full article

In sensorless control systems of permanent magnet synchronous motors (PMSMs), the traditional linear extended state observer (LESO) is preferred due to its simplicity and ease of implementation. With the development of PMSM sensorless control systems, the requirements for position estimation performance have increased, and thus, traditional LESOs can no longer meet those needs. To address this issue, this article proposes an estimation method based on an integrally compensated-enhanced linear extended state observer (IC-ELESO) and an improved quadrature phase locked loop (IQPLL) with a third-order LESO. In the back electromotive force estimation scheme, by introducing a compensation loop, the proposed IC-ELESO suppresses DC bias and improves position estimation accuracy compared to traditional LESOs. In the position estimation scheme, the IQPLL combines the third-order LESO with a quadrature phase locked loop (QPLL) to eliminate errors introduced by ramp signals. Finally, a PMSM experimental platform is built to conduct a comparative experiment between the method proposed and the traditional LESO, which verifies the feasibility and superiority of the method proposed in this article. Full article

The ISMC+IPLL (Improved Sliding Mode Control method with Improved Phase-Locked Loop) is proposed to reduce the jitter phenomenon of sensorless control for a permanent magnet synchronous motor (PMSM). Through influence analysis for the structure parameters of SMO to PMSM performance, the basis for determining the switching function and forming coefficient of ISMC+IPLL is constructed. Compared with traditional PI control and anti-integral saturation ASR control, the observation behavior of the ISMC+IPLL under step load and step speed is optimized to varying degrees, which effectively weakens the inherent jitter phenomenon of sensorless control for PMSM and provides a theoretical basis for establishing a high-performance control strategy for PMSM in the whole operation stage. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

This paper presents a comprehensive overview of permanent magnet synchronous motors (PMSMs), including their classifications, applications, and vector control strategies. It explores various control techniques, including maximum torque per ampere (MTPA), maximum current (MC), field weakening (FW), maximum torque per voltage (MTPV), sensorless control, and parameter identification, as discussed in this paper. These methods address key challenges in PMSM control, such as improving motor efficiency and accurately estimating rotor position and speed. Additionally, this paper presents the PMSM parameters due to many factors such as electric current, phase angle, saturation, and temperature. The survey findings provide a deeper understanding of PMSMs’ control strategies, aiding in the more efficient and reliable motor studies. Full article

The permanent magnet synchronous motor (PMSM) is the core of new energy vehicle drive systems, and its temperature status is directly related to the safety of the entire vehicle. However, the temperature of rotor permanent magnets is difficult to measure directly, and traditional sensor schemes are costly and complex to deploy. With the development of Artificial Intelligence (AI) technology, deep learning (DL) provides a feasible path for sensorless modeling. This paper proposes a prediction model that integrates a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory Network (BiLSTM), and multi-head attention mechanism (MHA) and introduces a Hybrid Grey Wolf Optimizer (H-GWO) for hyperparameter optimization, which is applied to PMSM temperature prediction. A public dataset from Paderborn University is used for training and testing. The test set verification results show that the H-GWO-optimized TCN-BiLSTM-MHA model has a mean absolute error (MAE) of 0.3821 °C, a root mean square error (RMSE) of 0.4857 °C, and an R² of 0.9985. Compared with the CNN-BiLSTM-Attention model, the MAE and RMSE are reduced by approximately 11.8% and 19.3%, respectively. Full article