Search Results (182)

To solve slot temperature accumulation in high thrust density permanent magnet linear synchronous motors (PMLSMs), this paper proposes an additive manufacturing (AM)-based variable cross-section winding design for coating-cooled tapered PMLSMs. Integrating the magnetic circuit features of tapered PMLSMs and AM windings’ technical merits, the motor’s operating mechanism and electromagnetic distribution are analyzed. With the coating cooling structure as the thermal management foundation, simulation reveals the motor’s temperature distribution under water cooling, defining core slot thermal management requirements. A novel cross-section winding design is then presented: first, a lumped-parameter thermal network model quantifies the coupling between the winding cross-sectional area and slot heat source distribution; second, a greedy algorithm optimizes the winding cross-section globally to reduce the slot hot-spot temperature and suppress temperature rise. Validated by a fabricated tapered PMLSM stator prototype and static temperature-rise experiments, the results confirm that winding cross-section reconstruction optimizes heat distribution effectively, offering a new approach for temperature rise suppression in high thrust density PMLSMs. Full article

In this study, a layered multi-objective optimization design method is proposed for a segmented skewed pole permanent magnet synchronous linear motor (PMSLM), considering thrust ripple suppression. Based on a 2-D analytical model, the effects of end force, cogging force, and winding asymmetry force on thrust ripple in PMSLM are analyzed, and the correctness is verified using finite element analysis and experiments. On this basis, a layered multi-objective optimization method is proposed. The whole optimization is divided into three layers. Metamodels of optimal prognosis are established to optimize the structural parameters in a layered manner, achieving a compromise between reducing thrust ripple and increasing average thrust. The effectiveness of the layered multi-objective optimization method is verified through simulation and prototype experiments. The layered structure aims to improve efficiency while ensuring computational accuracy. Full article

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

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

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

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

Based on the long-stator permanent magnet linear synchronous motor (PMLSM), motor structures with different pole–slot ratios are designed by changing the distribution of armature windings. A magnetic field analytical model of the motor is developed, the no-load magnetic field characteristics of the motor are calculated, and the results are compared and verified with those obtained by the finite element analysis (FEA). The influences of back-electromotive force (EMF) and armature reaction on the no-load magnetic field under different slots are studied. Through fast Fourier transform, the harmonic characteristics of the magnetic field in different structures are analyzed. Then, the cogging force and thrust characteristics generated by the motor in different structures are compared. The research results provide certain references for motor design. Full article

The continuous reduction in critical dimensions and the escalating demands for higher throughput are driving motion platforms to operate under increasingly complex conditions, including multi-axis coupling, structural nonlinearities, and time-varying operational scenarios. These complexities make the trade-offs among precision, speed, and robustness increasingly challenging. Traditional Proportional–Integral–Derivative (PID) controllers, which rely on empirical tuning methods, suffer from prolonged trial-and-error cycles and limited transferability, and consequently struggle to maintain optimal performance under these complex working conditions. This paper proposes an adaptive β–Proximal Policy Optimization with Random Network Distillation (β-PPO-RND) parameter optimization within the Prescribed Performance Control (PPC) framework. The adaptive coefficient β is updated based on the temporal change in reward difference, which is clipped and smoothly mapped to a preset range using a hyperbolic tangent function. This mechanism dynamically balances intrinsic and extrinsic rewards—encouraging broader exploration in the early stage and emphasizing performance optimization in the later stage. Experimental validation on a Permanent Magnet Linear Synchronous Motor (PMLSM) platform confirms the effectiveness of the proposed approach. It eliminates the need for manual tuning and enables real-time controller parameter adjustment within the PPC framework, achieving high-precision trajectory tracking and a significant reduction in steady-state error. Experimental results show that the proposed method achieves MAE = 0.135 and RMSE = 0.154, representing approximately 70% reductions compared to the conventional PID controller. 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

For the traditional one-degree-of-freedom controller, there is a trade-off between the tracking performance and disturbance rejection performance. The traditional two-degree-of-freedom controller can achieve disturbance rejection and tracking simultaneously, when there are one tracking channel and one disturbance rejection channel. To overcome the limitations of the one-degree-of-freedom controller and the traditional two-degree-of-freedom controller, an improved two-degree-of-freedom controller is designed for the speed regulation system of a permanent-magnet synchronous motor (PMSM) for use in electric aircraft scenarios and PMSM control applications. This proposed controller includes three elements: a proportional forward element, a proportional–integral feedback element, and a linear extended state observer element. The proportional forward element is used for tracking, and the proportional–integral feedback element and linear extended state observer element are used for disturbance rejection. The controller can simultaneously achieve tracking and disturbance rejection. The proportional forward element guarantees tracking performance, while the proportional–integral feedback and linear extended state observer elements enhance disturbance rejection. The stability of the proposed controller is experimentally demonstrated, and the results show that the improved controller offers superior tracking and disturbance rejection compared to traditional 1-DoF and 2-DoF controllers. Full article

The ironless tubular permanent magnet synchronous linear motor (TPMSLM) is in high demand for high-precision servo control applications due to its advantages of having zero cogging effect and high dynamic response. However, its electromagnetic field analysis model has not yet been perfected. This paper aims to accurately predict the magnetic field distribution and electromagnetic performance parameters of an ironless TPMSLM. Taking the axially magnetized ironless TPMSLM as an example, and disregarding the influence of the armature magnetic field on the air gap magnetic field, a simplified analytical model of the TPMSLM is established in the cylindrical coordinate system based on the equivalent magnetization current method (EMC), and the analytical formula for the air gap magnetic flux density is then derived. Subsequently, by applying electromagnetic field theory and the analytical formula for the magnetic flux density in the air gap, analytical expressions for the back electromotive force (back EMF) and thrust are derived, reducing analytical complexity while maintaining accuracy. The accuracy and practicality of the proposed analytical formulas are validated through comparisons with finite element analysis (FEA) and experimental prototypes. This analytical approach facilitates the optimization of linear motor parameters and the study of thrust fluctuation suppression, thereby laying the foundation for high-precision servo control of linear motors. Full article

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems. Full article

To improve the dynamic response and disturbance rejection performance of a permanent magnet synchronous motor (PMSM) speed control system, this paper designs a speed control strategy of PMSM based on super-twisting sliding mode corrected differential linear active disturbance rejection (STSM-CDLADRC). First, the speed loop model of a permanent magnet synchronous motor based on traditional LADRC is established. Second, the observer of LADRC is reconstructed according to the principle of error control, and the differential linear extended state observer (DLESO) is obtained. Then, to solve the observation hysteresis problem existing in the DLESO, the phase lead correction unit is introduced, and a corrected DLESO is designed (CDLESO); on this basis, the feedback rate in LADRC is also improved by using the super-twisting sliding mode control algorithm to design the super-twisting sliding mode linear state error feedback rate (STSM-LSEF), which improves the dynamic response performance of the system. Finally, the effectiveness and feasibility of the designed control strategy are verified by MATLAB/Simulink simulation and an experimental platform, and the results show that in the speed control system of the PMSM, the strategy effectively improves the dynamic response performance and anti-disturbance performance of the system. Full article

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields. Full article