Search Results (459)

A novel method for controlling the speed of interior permanent magnet synchronous motors (IPMSMs), known as the current-sensing-based dynamic direct voltage control method under the maximum torque per ampere (MTPA) concept, is introduced. This technique achieves precise tracking of machine velocity by determining the optimal combination of voltage amplitude and angle for each specific motor velocity and current/load condition. Unlike previous studies, this approach takes into account the transient model of the machine, resulting in improved accuracy during dynamic operating conditions compared with existing methods in the literature. Moreover, a comparative analysis is conducted involving different direct voltage MTPA speed drive approaches: the current-sensing dynamic direct voltage control (CS-DDVC) methodology, the simplified DDVC technique, and the static direct voltage MTPA control strategy. The well-known field-oriented control method is also included in the analysis. The dynamic methodology employs two tuning parameters to achieve the same MTPA objective while eliminating transient effects. Experimental results and quantitative assessment demonstrate that the proposed MTPA control methodology is a highly effective strategy, offering a respectable alternative to existing MTPA methods for driving IPMSMs. It enables the operation of IPMSMs under MTPA working conditions with high efficiency, making it suitable for a wide range of industrial applications. Experimental results demonstrate a reduction in speed dip from 120 rpm to 50 rpm at full load application and a 128% improvement in IAE compared to conventional DVC. From an engineering design perspective, the proposed control framework simplifies the drive-system architecture by eliminating cascaded current-control loops while maintaining effective transient dynamic performance suitable for embedded electric vehicle applications. Full article

For low-speed heavy-load steering of electric forklifts, conventional three-loop proportional–integral (PI) control employs a fixed saturation limit on the position-loop output. Consequently, the maximum allowable speed cannot be adjusted according to load variations. Under light-load conditions, the steering motor speed is excessively constrained, which wastes the available voltage margin. Under heavy-load conditions, the allowable speed may exceed the voltage limit, thereby causing voltage saturation. Moreover, load-torque feedforward compensation is commonly adopted to improve load-carrying capability. However, at medium and high speeds, excessive feedforward action may cause voltage saturation and current-vector offset. This can lead to loss of control of the steering motor. To address these issues, a voltage-limit-constrained dynamic saturation and load-torque feedforward control strategy is proposed for electric forklift steering systems. First, fuzzy PI control is adopted in the position loop. Then, considering the nearly identical direct-axis and quadrature-axis inductances of a surface-mounted permanent magnet synchronous motor (PMSM), the direct-axis current is set to zero. An analytical expression of the maximum safe speed is derived with the quadrature-axis current as the only independent variable. Based on this expression, a dynamic saturation limit is designed for the position-loop output. Finally, a reduced-order disturbance observer (DOB) is utilized to estimate the equivalent load torque in real time. The current feedforward gain is dynamically regulated according to the voltage margin. This compensates for torque limitation caused by speed-loop saturation while preventing voltage saturation. A Simulink simulation platform is developed using a forklift as the case study. The results demonstrate that, compared with the conventional three-loop PI controller, the proposed strategy reduces the no-load 180° step-response time by 30%. Under heavy-load and large-angle steering conditions, the voltage margin is maintained at approximately 10%. Full article

Online parameter identification is important for sensorless permanent magnet synchronous motor (PMSM) drives because motor parameter variation can reduce the accuracy of the controller and observer. However, in the background of sensorless control, the accuracy of online parameter identification is significantly affected by rotor position estimation errors and inverter nonlinearity. To address these problems, this paper proposes a high-frequency d-axis voltage injection-based online parameter identification method with inverter nonlinearity compensation. The proposed online identification method can identify the stator resistance and d-axis inductance independently. It not only overcomes the rank-deficiency problem in conventional voltage-equation-based identification, but also shows through theoretical analysis that the identification results are insensitive to rotor position estimation errors. To improve the identification accuracy, the influence and importance of inverter nonlinearity on parameter identification are analyzed, and a compensation method based on zero-sequence voltage characteristics and a feedforward neural network is developed. The identified voltage error is compensated through equivalent dead-time correction. Simulation and experimental results verify the advantages of the proposed method under different operating conditions. Full article

To address the degraded prediction accuracy, increased torque ripple, and weakened dynamic response of conventional finite-control-set model predictive control (FCS-MPC) under magnetic saturation, parameter mismatch, and load disturbances in permanent magnet synchronous motors (PMSMs), this paper proposes a hierarchical FCS-MPC framework based on PINN-RLS and virtual-voltage-vector extension, termed HRPV-MPC. Built upon a unified nonlinear motor model, the proposed method integrates PINN-RLS-based online parameter correction, virtual-voltage-vector extension, disturbance-observer-based feedforward compensation, maximum-torque-per-ampere (MTPA) and quadratic-programming (QP) reference reconstruction, and deep-neural-network (DNN)-based torque-ripple compensation into the same closed-loop control framework. Unlike existing studies that usually optimize parameter identification, disturbance compensation, or ripple suppression separately, the proposed method emphasizes their coordinated interaction within the predictive control chain so as to simultaneously improve steady-state precision, disturbance rejection, and dynamic recovery performance. Simulation results show that the proposed HRPV-MPC achieves coordinated improvements in steady-state precision, dynamic response, and disturbance rejection under various operating conditions; compared with baseline FCS-MPC, it exhibits clear advantages in torque-ripple suppression, torque-error reduction, load-disturbance recovery, and speed-tracking performance, thereby validating the effectiveness and superiority of the constructed hierarchical collaborative framework. Full article

This paper presents a bidirectional power flow control strategy for a grid-connected solar photovoltaic (PV)-based water pumping system employing a brushless DC (BLDC) motor drive. The proposed system enables continuous water pumping operation under varying solar irradiance conditions without the use of phase-current sensors while maintaining the motor at its rated operating speed. A single-phase voltage source converter (VSC) employs a unit vector template (UVT)-based control scheme that regulates bidirectional power flow between the utility grid and the dc-link, thereby supporting both grid-to-load and PV-to-grid power transfer. Excess photovoltaic energy can be exported to the utility grid during periods of reduced pumping demand, improving overall utilization of the available solar power. The voltage source inverter (VSI) driving the BLDC motor employs a PWM_ON_PWM switching scheme to reduce torque ripple while operating at fundamental frequency to minimize switching losses. The proposed system also incorporates maximum power point tracking (MPPT), power factor correction, and harmonic mitigation to improve power quality and ensure compliance with IEEE-519 requirements. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink R2023a simulations under various operating conditions. The simulation results demonstrate stable dc-link voltage regulation, bidirectional power flow capability, continuous pumping operation, and reduced torque ripple, highlighting the suitability of the proposed system for grid-interactive solar water pumping applications. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

The model predictive current control (MPCC) method has the advantages of a simple structure and fast response. It has been regarded as one of the most effective methods for solving multiphase driving systems. However, mismatches in motor parameters will significantly degrade the MPCC method’s control performance. To solve this problem, a novel model-free predictive current control (MFPCC) method for a dual three-phase permanent magnet synchronous motor (DT-PMSM) based on an extended Kalman observer (EKO) is proposed in this paper. Firstly, the modulated virtual voltage vector (MVV) is synthesized to increase the modulation range and reduce the control error. Secondly, an ultra-local model with a parameter-interference term is established to improve the system’s robustness to parameter mismatches. By combining the duty-cycle calculation method without motor parameters, the current tracking accuracy has been significantly improved. Thirdly, the EKO was introduced to observe the nonlinear part to improve the accuracy of the ultra-local model. Fourthly, the triangle wave is proposed as the carrier wave, with the reference value updated at the half-sampling period, generating an asymmetric PWM waveform that accurately tracks the reference voltage vector and simplifies software implementation on a low-cost microprocessor. Finally, the validity of the proposed method was verified experimentally by comparing it with two existing methods. Full article

This study details the modelling and simulation analyses performed on a mathematically modelled permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) driven by a field-oriented controlled (FOC) voltage source inverter (VSI) coupled with a half-bridge bidirectional buck-boost DC/DC converter for two-wheeler electric vehicle (EV) applications. The 5 kW, 1500 rpm PMa-SynRM employed here has a shorter response time and is also naturally lighter and cost-effective, making it suitable for two-wheeler EVs. Field-oriented control simplifies the control strategy for PMa-SynRM by decoupling torque and flux, effectively matching the behaviour of a DC motor. A half-bridge buck-boost converter is a DC-DC converter capable of bidirectional power flow, stepping up and down voltages. This makes it ideal for both motoring and regenerative braking in electric vehicles. The buck-boost converter with its controller effectively adjusts the inverter and battery voltage for efficient power flow during motoring and maximum power recovery during regenerating braking. The developed model aims at demonstrating forward and reverse motoring, as well as forward and reverse braking to validate the four-quadrant torque-speed characteristics of two-wheeler EVs. The proposed model attains less than 2% torque ripple and less than 1% speed ripple, respectively. Further, the current ripples are minimised to reduce losses and to improve efficiency. The work presented in this paper implements a PMa-SynRM-based drive system for EVs, a technology which is in the exploratory stage and not commercially widespread. This adds novelty to the proposed work. A MATLAB Simulink environment was used for modelling and simulation. Full article

In this study, parameter estimation-assisted sensorless control methods are proposed for compressor motors. As sensorless control strategies, rotating high-frequency injection (RHFI), pulsating high-frequency injection (RHFI), and an adaptive-gain sliding mode observer (AG-SMO) are employed. During startup, HFI-based methods are utilized, whereas AG-SMO is activated under steady-state operating conditions. To mitigate parameter variations and inverter nonlinearities, Adaline Neural Network (ANN), Recursive Least Squares (RLS), and Extended Kalman Filter (EKF) algorithms are integrated for the real-time estimation of stator resistance and dead-time voltage. The proposed framework is validated through both simulation and experimental studies on a 30 W, 20 V interior permanent magnet motor commonly used in compressor applications. The results demonstrate that sensorless control algorithms alone provide robust operation, while the incorporation of parameter estimation effectively eliminates stability issues and ensures reliable transitions from low to high speeds. Comparative analysis reveals that ANN has a simple structure, RLS achieves faster convergence, and EKF provides smoother estimates under noisy conditions. Overall, the integration of sensorless control algorithms with ANN/RLS/EKF-based parameter estimation and dead-time compensation offers a cost-effective and reliable solution for high-performance compressor applications. Full article

Aiming to improve the robustness of two-phase buck converters powering DC bus of permanent magnet synchronous motor drives, this article presents a novel voltage regulation scheme. The proposed scheme comprises a three-switching-surface nonsingular fast terminal sliding mode controller (TSS-NFTSMC) for output voltage regulation and a current balancing controller to equalize the inductor currents. Due to the fast terminal sliding mode surface, the output voltage error converges more rapidly both when far from zero and when approaching zero. The phase plane is split into four regions by three independent switching surfaces. Based on the region where the sliding variable resides, the TSS-NFTSMC can directly decide the number of enabled high-side switches, which helps suppress internal disturbances effectively. The stability and convergence of the presented control system are verified via Lyapunov stability analysis. The convergence property of TSS-NFTSMC is independent of the current controller. Both simulation and experimental results demonstrate that the proposed control strategy achieves satisfactory dynamic response and strong disturbance rejection capability. Full article

Low-voltage (LV) alternating current (AC) power distribution systems are widely used, where phase-to-neutral short-circuit faults are a major cause of electrically induced fires. Prior to a circuit breaker interruption, arc discharges may develop between conductors, leading to intense localized heating of the cable insulation and a potential ignition risk. In this study, a magnetohydrodynamic (MHD) model of 220 V AC short-circuit arcs is established to investigate the coupled electrical and thermal behavior of arc discharges and their induced heating effects on conductor insulation. The transient temperature distribution in the arc region and insulation layer is numerically analyzed under different tripping currents and tripping times, and insulation ignition risk is evaluated based on characteristic thermal thresholds. To validate the simulations, a controllable 220 V AC short-circuit experimental platform is developed using a motor-driven wire contact mechanism. Circuit breakers rated at 20 A, 32 A, and 63 A are tested, and short-circuit current and voltage waveforms are recorded. The results indicate that insulation ignition risk is jointly governed by short-circuit current magnitude and breaker tripping time. Delayed interruption significantly increases insulation temperature and ignition susceptibility, whereas rapid interruption effectively suppresses arc-induced heating. Full article

Dual three-phase interior permanent magnet synchronous motors (DT-IPMSMs) are widely used in high-power and high-reliability applications, and accurate rotor polarity identification at startup is a critical prerequisite for their stable and efficient operation. This study aims to address the problem of initial position acquisition during the startup of DT-IPMSMs by proposing a simple and fast rotor polarity identification method. The proposed method is based on the high-frequency square-wave voltage injection (HFSWVI) in the vector space decomposition (VSD) space, where both the current and voltage are injected into the d-axis. The single-pulse direct current (DC) injection is used to alter the magnetic saturation. Then, the change rates of the d-axis high-frequency response current are compared before and after DC injection to identify the rotor magnetic polarity. In addition, a moving average filter (MAF) is applied to suppress the fluctuations in the current change rate, which increases the accuracy of polarity identification. Moreover, a simple compensation technique is designed to make the estimated d-axis current change smoothly when the estimated angle changes from N-pole to S-pole. The effectiveness of the proposed method is proved by the experimental results in both standstill and free-running states for the prototyped DT-IPMSMs. This method provides a practical and efficient solution for initial position identification of DT-IPMSMs, contributing to the advancement of control technology for dual three-phase motor systems in related fields. Full article

The torque ripples of robotic permanent magnet synchronous motors (PMSMs) degrade motion smoothness and positioning accuracy of the system, while inevitable load transients in robotic tasks further complicate torque ripple attenuation. To address this issue, this paper develops an event-triggered torque ripple attenuation method that explicitly distinguishes torque ripple from dynamic load transients. First, a sliding-mode torque observer is constructed to obtain real-time torque information, whose stability is rigorously analyzed using a Lyapunov function. Second, frequency-selective torque ripple extraction schemes are proposed to accurately isolate steady-state high-frequency torque ripple from the estimated torque signal. In particular, two specially designed filtering structures are developed and compared, one of which is selected to preserve ripple-related frequency content during test, ensuring robust and accurate ripple identification under varying operating conditions in robotics. Third, a torque-ripple-regulation-based compensation strategy is used within a vector-controlled PMSM drive, in which the extracted torque ripple is processed by a dedicated ripple regulator to generate voltage compensation signals. This strategy achieves effective steady-state torque ripple attenuation with low implementation complexity, while avoiding performance degradation during dynamic load transients. Finally, experimental results are provided to validate the effectiveness of the proposed methods. Full article

With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current control (MPCC) variants still face challenges regarding high computational overhead and strong dependency on accurate motor parameters, which limit their industrial applicability. To address these issues, this paper proposes an adaptive recursive MPCC for PMLSM drives based on the Cartesian distance minimization principle. An adaptive recursive prediction scheme that is inspired by the feedback structure of recurrent architectures is first introduced. By cyclically utilizing the previously sampled current to predict the next period’s state, the strategy effectively decouples the control law from inductance variations. The dependence on resistance is further mitigated by analyzing the correlation between the ideal current vector and voltage vector deviations. Second, the selection of the optimal voltage vector is transformed into a geometric problem: minimizing the Cartesian distance between the reference voltage and 19 candidate deviations within a proposed virtual voltage vector hexagon. To minimize the computational burden, the vector space is partitioned into eight regions, allowing the optimal candidate to be selected from only two pre-derived deviations. The experimental results demonstrate that the proposed method significantly outperforms existing MPCC benchmarks. Specifically, the execution time is reduced by 63.6%. Under severe parameter mismatch, the current THD is reduced from 14.82% to 6.35%, and the thrust ripple is improved from 12.06 N to 5.25 N, validating its superior robustness and efficiency. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article