Search Results (1,230)

Permanent magnet synchronous motors (PMSM) are widely favored in industry for their high efficiency, compact size, and robust performance. This study employs a model-based PID control approach for speed regulation of PMSM. In contrast to traditional PID approaches, this method addresses the inherent nonlinearity of PMSM systems and tunes PID coefficients dynamically for fast multi-input and multi-output (MIMO) operations. Traditional PID controllers typically assume linear motor dynamics and determine a single set of coefficients, often through trial and error. However, the nonlinear dynamics of motor drives and variations in motor parameters often lead to instability, limiting the effectiveness of conventional PID controllers. The proposed auto-tuning PID controller adjusts its coefficients in real-time based on the system’s operational state. This method has been implemented in both simulation and experimental setups, with real-time execution facilitated by dSPACE DS1104. A comparative analysis with conventional PI control demonstrates the enhanced stability and adaptability of the proposed approach. Full article

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability. Full article

This work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of $\pm 50\%$; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects. Full article

Aim: This study examined how load size and symmetry affect trunk muscle activation patterns, vertical ground reaction forces, and estimated lumbar spine compression during overground walking in individuals with chronic low back pain (CLBP) and those without symptoms. Methods: Thirty male participants (15 with CLBP, 15 controls; ages 23–28 years) performed walking tests under four load conditions: symmetric and asymmetric carriage at 10% and 20% of body weight. Bilateral surface electromyography measured activation from seven trunk muscles (rectus abdominis, external oblique, internal oblique, latissimus dorsi, lumbar erector spinae, multifidus) and the thoracolumbar fascia region, normalized to maximum voluntary isometric contractions (%MVIC). Force plates recorded vertical ground reaction forces synchronized with heel-strike events. A repeated-measures ANOVA with Bonferroni corrections was used to analyze the effects of load configuration and magnitude. Results: Asymmetric loading at 20% body weight caused significantly higher peak vertical ground reaction forces compared to symmetric loading (mean difference = 47.3 N, p < 0.001), with a significant interaction between load magnitude and configuration (p = 0.004, ηp² = 0.26). Participants with CLBP showed consistently higher trunk muscle activation throughout the gait cycle (peak: 37% MVIC vs. 30% MVIC in controls; p < 0.001, d = 1.68), with maximum recruitment at shorter muscle lengths and 24% less activation at optimal length (95% CI: 18.2–29.8%). The lumbar erector spinae and multifidus muscles exhibited the highest activation during asymmetric 20% loading in CLBP participants (0.282 and 0.263%MVIC, respectively), indicating compensatory neuromuscular strategies. Conclusion: Asymmetric load carriage creates disproportionately high mechanical and neuromuscular demands, effects that are greatly amplified in individuals with CLBP. These findings support rehabilitation strategies that improve load distribution and restore motor control, thereby reducing compensatory strain and enhancing trunk stability. 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

Precise electromagnetic torque estimation for permanent magnet synchronous motors (PMSMs) is crucial for enhancing the dynamic performance and energy efficiency of electric vehicles. To address the dynamic variations in dq-axis inductance caused by magnetic cross-coupling and saturation effects during motor operation—which lead to significant torque estimation errors in traditional fixed-parameter models under variable torque and speed conditions—this paper proposes a dynamic torque estimation method that integrates online dq-axis inductance identification based on a variable-step adaptive linear neural network (ADALINE) with an extended flux observer. The online identified inductance values are embedded into the extended flux observer in real time, forming a closed-loop torque estimation system with adaptive parameter updating. Experimental results demonstrate that, under complex operating conditions with varying torque and speed, the proposed method maintains electromagnetic torque estimation errors within ±3%, with a convergence time of less than 20 ms, while achieving inductance identification accuracy also within ±3%. These results significantly outperform conventional methods that do not incorporate inductance identification. This study provides a highly adaptive and engineering-practical solution for high-precision torque control of interior permanent magnet synchronous motors (IPMSMs) in automotive applications. Full article

Permanent Magnet Synchronous Motors (PMSMs) are central to high-performance servo drives, yet their control accuracy is often compromised by parameter uncertainties and external disturbances. While the Uncertainty and Disturbance Estimator (UDE) offers enhanced robustness by treating such uncertainties as lumped disturbances, it suffers from significant integral windup under output saturation, degrading dynamic response. This paper proposes a symmetry-principle-based UDE control strategy for the PMSM speed loop, which simplifies parameter tuning through derived analytical expressions for PI gains. To address the windup issue, two anti-windup algorithms are introduced and critically compared: a piecewise tracking back-calculation method and an integral final value prediction algorithm. The key finding is that the integral final value prediction algorithm demonstrates a superior performance. Simulation results show that it reduces the convergence time by 6.3 ms and the overshoot by 1.8% compared to the piecewise method. Experimental validation on an STM32F446-based platform confirms these findings. Under a 600 r/min step with load, the UDE controller with the integral final value prediction algorithm reduces speed overshoot by 15% compared to the piecewise algorithm and by 47% compared to the standard UDE controller without anti-windup. These results conclusively show that the proposed integrated strategy—combining symmetry-based UDE control with the integral final value prediction anti-windup algorithm—significantly improves the dynamic response, accuracy, and robustness of PMSM servo systems. Full article

The construction of ultra-high voltage transmission lines puts extremely high demands on the high-altitude operation of heavy-load unmanned aerial vehicles (UAV). Air density and temperature at high altitudes have a great influence on the efficiency and stability of the UAV power system. Traditional regulation methods based on parameters pre-set or simple look-up tables cannot achieve the best adaptability. In this paper, we presents an intelligent method for the high-altitude adaptability control of heavy-load UAV power systems using a deep neural network. The proposed method collects real-time, multi-dimensional environmental parameters, including altitude, temperature, and air pressure, using a barometric altimeter and GPS receiver, constructs a High-Altitude Adaptive Regulation Network (HAARN), and intelligently learns complex nonlinear relationships to predict the optimal motor speed, propeller pitch angle, and current limit under the current environmental conditions so as to dynamically adjust power output. The HAARN model was trained on a dataset of 12,000 synchronized samples collected from both controlled environmental-chamber experiments (temperature range: −10 $°\mathrm{C}$ to 20 $°\mathrm{C}$; pressure range: 100–50 kPa, corresponding approximately to 0–5500 m) and multi-point plateau flight trials conducted at 2000 m, 3000 m, 4000 m, and 4500 m. This combined dataset was used for feature engineering, exhaustive-label generation, and model validation to ensure robust generalization across realistic high-altitude operating conditions. Experimental results show that, compared with traditional PID control and lookup-table approaches, the proposed method reduces thrust attenuation by about 12.5% and improves energy efficiency by 8.3% at the altitude of 4000 m. In addition, HAARN demonstrates consistent improvements across the tested altitude range (0–4500 m). Full article

In order to reduce the impact of various disturbances on the permanent magnet synchronous motor (PMSM) system especially during low-speed (0–300 rpm), this paper proposes a novel repetitive fractional-order active disturbance rejection control (RFO-ADRC) method. It combines repetitive control and fractional-order active disturbance rejection control (FO-ADRC) innovatively in an cascaded structure. Repetitive compensation is employed to handle the periodic disturbances arising from the structural characteristics of PMSM. FO-ADRC is employed to compensate for the residual aggregated disturbances. It novely replaces the nonlinear error feedback control (NLSEF) link in active disturbance rejection control (ADRC) with fractional-order control, and then incoportes an improved smooth function into the extended state observer (ESO), effectively reducing parameter adjustment complexity and mitigating inherent chattering. Finally, the proposed RFO-ADRC is validated on a 1.5 kW PMSM experimental platform and compared with PI, ADRC, and FO-ADRC. Experimental results show that RFO-ADRC achieves a start-up time of 0.36 s fastest, zero overshoot, a steady-state speed error within ±2.27 r/min lowest, a total harmonic distortion (THD) of 6.47% lowest, and a recovery time of 0.22 s under sudden load changes fastest, demonstrating superior performance. Full article

Interior permanent magnet synchronous motors (IPMSMs) are widely adopted in electric vehicles due to their high torque density and efficiency, and they require flux-weakening operation to achieve high-speed performance under certain driving conditions. However, the traditional current vector control (CVC)-based flux-weakening strategies suffer from performance degradation when motor parameters, such as inductances and flux linkage, vary with temperature and operating conditions. To address this issue, this paper proposes a robust flux-weakening control strategy against multiple parameter variations. First, three sequential sliding-mode observers (SMOs) that form a sliding-mode observer suite (SMOS), whose stability is analyzed using Lyapunov theory, are designed to estimate the flux linkage, q-axis inductance, and d-axis inductance, respectively. Second, an error-analysis extraction (EAE) is developed to refine the parameter estimation accuracy by analytically solving a set of linear equations derived from observer results. Third, the accurately estimated parameters are applied to the CVC framework to generate adaptive reference currents, achieving robust and stable flux-weakening control performance. Finally, simulation and experiment are conducted to demonstrate that the proposed strategy effectively enhances control performance under multiple parameter variations. Full article

In this paper, a permanent-magnet-assisted synchronous reluctance motor (SYNRM) coupled with a newly built QDBC and a voltage-fed inverter (VFI) for a standalone PV water pumping system is suggested. Because power supply oscillations can result in short-term disruptions that affect drive performance in industrial applications involving these motors, a robust smooth control system is required to guarantee high efficiency and uninterrupted operation. According to the suggested architecture, a newly built quadratic boost regulator with a very high voltage gain, called a quadruple-diode boost converter (QDBC), is used to first elevate PV voltage to high levels. Additionally, to optimize the power output of the solar PV module, the perturbation and observation highest power point tracking approach (P&O) is implemented. To provide smooth synchronous motor starting, field-oriented control (FOC) of a voltage-fed inverter (VFI) is combined with hysteresis current control of the QDBC. The optimization algorithms discussed in this paper aim to enhance the efficiency of the SYNRM, particularly in operating a synchronous motor powered by variable energy sources such as solar PV. These algorithms function within a cybernetic system designed for water pumping, incorporating feedback loops and computational intelligence for improved performance. Afterward, the three-phase permanent-magnet synchronous motor that drives the mechanical load is fed by the resulting voltage via a voltage source inverter. Furthermore, a thorough hysteresis current control method implementation of the QDBC was suggested in order to attain optimal efficiency in both devices, which is crucial when off-grids are present. Even when the DC-link voltage dropped by up to 10% of the rated voltage, the suggested method was shown to maintain the required reference torque and rated speed. To verify the efficacy of the suggested method, a simulation setup according to the MATLAB 2022b/Simulink environment was employed. To gather and analyze the data, multiple scenarios with varying operating conditions and irradiance levels were taken into consideration. Finally, a working prototype was constructed in order to validate the mathematical analysis and simulation findings of the suggested framework, which includes a 1 kW motor, current sensor, voltage sensor, QDBC, and VCS inverter. Full article

The continuous pursuit of power density, efficiency, and miniaturization poses significant challenges to the heat dissipation and temperature-rise control of permanent magnet synchronous motor (PMSM) for new energy vehicles. This paper proposes a novel S-shaped axial return passage in the motor casing and a combined oil-cooling scheme integrating S-shaped and end-spraying passages. The geometric structure and parameters of the S-shaped passage and end-spraying passage were designed and optimized, and a finite-element temperature-field model of a PMSM equipped with the combined oil-cooling system is established. The results show that, compared with a traditional right-angle axial returning passage, the pressure loss of the new S-shaped returning passage is reduced by 50%, while the wall heat transfer coefficient remains comparable. At a cooling oil flow rate of 12 L/min, the highest temperature of the end winding is 92.6 °C, only 1.5 °C higher than that of the stator core under rated operating conditions. An experimental prototype was fabricated, and the measured results indicate that the simulated end-winding temperature shows close agreement with the experimental values, with a maximum deviation of only 3.8 °C. The proposed combined oil-cooling scheme efficiently enhances the cooling of both the stator core and end winding and significantly improves the temperature uniformity of the PMSM. Full article

To address the torque ripple and speed fluctuation issues in high-frequency square-wave injection-based sensorless control of interior permanent magnet synchronous motors (IPMSM) caused by low-order stator current harmonics (primarily the fifth and seventh), this paper proposes a harmonic voltage compensation strategy based on a sixth-order quasi-proportional resonant (QPR) controller, which effectively suppresses these specific harmonic disturbances. The proposed method, building upon conventional high-frequency square-wave injection, introduces a harmonic current extraction technique based on multiple synchronous reference frame transformations to separate the fifth and seventh harmonic components accurately; then, according to the established harmonic voltage compensation equation, generates targeted compensation voltage commands; finally, further precisely suppresses the corresponding harmonic currents through a sixth-order QPR controller connected in parallel with the current proportional-integral (PI) controller. This paper comprehensively establishes the mathematical models for harmonic extraction and voltage compensation, and conducts a detailed analysis of the parameter design of the sixth-order QPR controller. Simulation results demonstrate that the proposed strategy can significantly suppress stator current distortion, effectively reduce torque and speed ripples, and substantially improve rotor position estimation accuracy, thereby verifying the superiority of the novel harmonic-suppression-based sensorless control strategy. Full article

This study presents a systematic comparative analysis of nine stator current discretization methods within the Model Predictive Current Control (MPCC) framework for Permanent Magnet Synchronous Motors (PMSMs). These methods have generally been examined individually or in limited combinations in previous research, and this holistic and comprehensive comparison constitutes the core contribution of this work by addressing a significant gap in the existing literature. The investigated MPCC methods—Forward Euler (FE), Backward Euler (BE), Midpoint Euler (ME), Fourth-Order Runge–Kutta (RK4), Runge–Kutta Ralston (RKR), Taylor Series (TS), Verlet Integration (VI), Crank–Nicolson (CN), and Adams–Bashforth (AB)—are comprehensively evaluated for their dynamic performance, including speed tracking, torque response, settling time, rise time, overshoot, and Total Harmonic Distortion (THD). Additionally, these analysis results are benchmarked against conventional Proportional–Integral–Derivative (PID) and Field-Oriented Control (FOC) methods. In terms of key performance indicators, the MPCC–RKR method proved optimal for speed tracking under no-load conditions, achieving the lowest overshoot, specifically ranging from 0.097% to 1.450%. Conversely, MPCC–ME and MPCC–CN demonstrated superior transient performance under sudden-load conditions (1.7 Nm), yielding the smallest torque deviations, fastest settling times. Specifically, MPCC-ME recorded the lowest overshoot (1.512%) at the 7 s load step, while MPCC-CN performed best at 9 s (1.220%) and 11 s (1.577%). Among the predictive schemes, the MPCC–RKR method achieved the highest current quality with a minimum THD of 3.69% at nominal speed. Finally, it has been confirmed through the applied statistical analysis techniques that the performance differences among the discretization methods are significant. The comparative analysis examines both the dynamic performance of the methods and the fundamental trade-off between accuracy and computational burden in MPCC design. Simple single-step explicit methods (FE, ME, RKR, VI, AB) offer low computational cost and are well suited for high–sampling-frequency real-time applications, especially with sufficiently small sampling times, whereas more complex multi-step or implicit methods (BE, RK4, TS, CN) may increase the processor load despite their potential gains in accuracy and stability. This study provides practical, evidence-based guidelines for selecting an optimal discretization method by balancing accuracy and dynamic performance requirements for PMSM applications. Full article

This paper presents a systematic review of linear motor-based electromagnetic suspension, a key technology for reconciling vehicle comfort, handling stability, and energy consumption. The review focuses on two core areas: actuator configuration and control strategy. In configuration design, a comparison of moving-coil, permanent magnet synchronous (PMSLM), and switched-reluctance linear motors identifies the PMSLM as the mainstream approach due to its high-power density and performance. Key design challenges for meeting stringent vehicle operating conditions, such as mass-volume optimization, thermal management, and high reliability, are also analyzed. Regarding control strategy, the review outlines the evolutionary path from classical to advanced and intelligent control. It also examines the energy-efficiency trade-off between vibration suppression and energy recovery. Furthermore, the paper summarizes three core challenges for industrialization: nonlinear issues like thrust fluctuation and friction, the coupling of electromagnetic–mechanical–thermal multi-physical fields, and bottlenecks related to high costs and reliability verification. Finally, future research directions are envisioned, including new materials, sensorless control, and active safety integration for autonomous driving. Full article