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This article proposes an innovative flux modulated permanent magnet (PM) arc motor enhanced with flux-concentrated stator PMs (FCS-FMPMAM). With the primary goal of increasing the average torque output, the FCS-FMPMAM incorporates a set of flux-focused stator PMs. This article initially introduces the configuration and performance of the FCS-FMPMAM. The combination of the flux-switching PMs, consequent-pole PMs and PMs in the stator yoke is beneficial to generate abundant harmonics and bolster the triple flux-modulation effect. Then, the optimal rotor pole number of the proposed machine is analyzed and selected. The sensitivity analysis is performed on the design parameters of FCS-FMPMAM for the consequent optimization of the torque performance of FCS-FMPMAM. This facilitates the subsequent torque performance optimization of FSC-FMPMAM. Based on the multi-objective evolutionary algorithm, the torque capabilities of FCS-FMPMAM are optimized to obtain the maximized average torque output and the minimized torque ripple. The optimized machine is analyzed under different PM excitation conditions to reveal the superiority of the proposed PM arrangement. Finally, the proposed machine is compared with an existing flux-modulated stator-PM arc motor in electromagnetic performances with finite element analysis method to exhibit the superiority of FCS-FMPMAM.

This paper proposes a new variable flux partitioned-stator flux modulation machine with multiple excitations (VF-PSFMMEM), which places permanent magnets (PMs) on the inner stator and rotor simultaneously to enhance torque production, and employs field windings in the inner stator to realize flux weakening. The configuration and operating principle of machine are described. A multi-objective optimization design of machine is developed for obtaining high average torque (Tavg), good flux weakening capability and low torque ripple (Trip). The electromagnetic performances of VF-PSFMMEM at different excitation sources are analyzed through finite element method. The results indicate that VF-PSFMMEM can provide large flux linkage, high Tavg, low Trip, high efficiency and good flux weakening capability.

An asymmetric dual-stator flux modulated permanent magnet (PM) arc motor (ADS-FMPMAM) for the propulsion of large-scale telescopes is proposed in this paper. The topology of ADS-FMPMAM is firstly researched. Subsequently, the operational concept of ADS-FMPMAM is analyzed, incorporating the flux modulation phenomenon with a double-stator PM arc motor. A comparative analysis of various rotor pole configurations is carried out to identify an optimal rotor pole number. The particle swarm optimization technique is used to maximize average torque while minimizing torque fluctuations. Additionally, the performance of ADS-FMPMAM is assessed and contrasted in scenarios involving a single stator. Comparative analysis between the ADS-FMPMAM and a double-stator consequent-pole flux reversal PM arc motor demonstrates that the proposed ADS-FMPMAM exhibits superior torque capabilities and is more suitable for directly driving system applications.

The transmission and conversion of torque is an important link in hybrid electric vehicles (HEV), which is related to the quality and driving experience of such vehicles. A new flux modulated dual-rotor permanent magnet machine (FR-DRPM) is proposed in this paper for torque transmission. The topology and operating principle of the FM-DRPM are proposed and analyzed. Different combinations between the two rotors of the FR-DRPM are investigated to minimize the torque ripple and cogging torque based on finite-element analysis method. A 21/17 rotor pole machine is selected for the further optimization of torque performances. To better optimize the proposed machine, a new method combined with CatBoost and multiple objective particle swarm optimization (MOPSO) is presented. The results based on the proposed method are compared with traditional multiple objective genetic algorithms to verify the properties of the new method. The optimization method is efficient and superior and the FR-DRPM has good torque transmission performance in HEV.

Permanent magnet arc motor (PMAM) is widely used in some scanning systems, such as the astronomical telescopes and large antennas. In order to address the optimization design of the PMAM, a new multi-objective optimization design method by combing gradient boosting decision tree (GBDT) and differential evolution algorithm (DEA) is proposed in this paper. Specifically, the motor structure of the PMAM is firstly presented. And then, a finite-element (FE) model of the PMAM is built to obtain sample data. Based on the sample data, the powerful machine learning algorithm called GBDT is innovatively introduced to establish surrogate model, which can identify the mapping relationships between the optimization objectives and the structural parameters. Subsequently, a popular intelligent search algorithm named DEA is employed to conduct the optimization design. Finally, both FE simulation and prototype experiment are performed to verify the effectiveness and advantages of the proposed method.

This paper proposes two new dual-stator consequent-pole (CP) flux reversal permanent magnet (PM) arc machines (DS-CPFRPMAMs) for large telescope drives. The DS-CPFRPMAMs using CP PM arrangement can effectively reduce flux leakage and increase PM utilization. In addition, the space utilization and torque density of machine are significantly enhanced with the aid of dual-stator structure. The topologies and operation principles of DS-CPFRPMAMs are first described. Then, the design considerations of DS-CPFRPMAMs are analyzed. To reveal the merits of DS-CPFRPMAMs, the electromagnetic characteristics of two DS-CPFRPMAMs are comprehensively investigated and compared. The results indicate that compared to machine II, machine I accommodates lower cogging torque and better over-load torque capability. While, compared to machine I, machine II has higher back-EMF, torque density and PM utilization.

This article proposes a novel speed measurement strategy for a permanent magnet arc motor (arc motor) servo system. This method improves the accuracy of a low-resolution encoder at low-speed condition through a newly developed acceleration-based disturbance observer (ADOB) combined with a Kalman observer (ADOB-KMO). A novel model-reference-adaptive inertia identification based on position signal is proposed to accurately construct the motion equation of arc motor. The method can avoid the influence of inaccurate speed on the establishment of the system equation, which is different from the traditional inertia identification strategy relying on the rotor speed. The ADOB is then used to avoid differential and quadratic differential of position signal in existing disturbance observer. In addition, the observation result of ADOB is fed forward to the current control circuit to improve the disturbance rejection performance of the system. Compared with the conventional Kalman velocity observer, ADOB is used to reduce the order of KMO, and ADOB-KMO then replaces the M/T methods to measure the rotor speed. Experiments based on an arc motor and 2500 ppr encoder verify the effectiveness of the proposed method.

In this article, a multiscenario parameter optimization method for an active disturbance rejection controller (ADRC) of a permanent magnet synchronous motor (PMSM) based on deep reinforcement learning (DRL) is proposed as MSPO-DRL. The parameter setting of the nonlinear ADRC has always been one of the difficulties affecting the optimal performance of ADRC, and there will be different optimal parameters under different control requirements. In this article, an artificial intelligence algorithm is introduced into the parameter optimization process of ADRC, and the DRL parameter optimization model that can automatically optimize and adjust the ADRC parameters in different application scenarios is constructed so that the ADRC can achieve the best control effect conveniently and the limitation of current methods has been solved. ADRC is applied to the speed loop of flux weakening control of PMSM for more electric aircraft, and the mathematical model of ADRC in this environment is built above all. The Markov decision process is integrated into ADRC. The interface module and reward function between ADRC and MSPO-DRL are designed. The concept of the ADRC control scenario is defined and integrated into the concept of the Markov decision process to improve the generalization of DRL. Then, the MSPO-DRL model is established, and the deep deterministic gradient strategy is used as the gradient descent strategy to converge the parameters optimization. After the model learning is completed, different environmental conditions are randomly selected for simulation and experiments to verify the optimization effect and generalization performance of the algorithm. Optimizations that are carried out by the heuristic algorithms are used for comparisons, and the superiority and feasibility of the proposed algorithm are verified.

This article proposes new dual-stator flux modulated machines with dual-permanent magnet (PM) excitation (DS-FMDPMMs) for direct-drive applications. The proposed DS-FMDPMMs are characterized by employing consequent-pole (CP) PMs on both the stator and rotor sides. By using dual-layer CP PMs, the DS-FMDPMMs can perform the bidirectional flux modulation effect to enhance the torque capability. In addition, the merits of dual-stator structure, such as high space utilization, high torque density, and power factor are inherited in the proposed machines. The design principle of DS-FMDPMM is provided at first, and the formed three representative DS-FMDPMMs with different stator-PM arrangements are proposed. Then, the design considerations of three DS-FMDPMMs are described in detail, which includes the armature winding design, the selection of stator-slot/rotor-pole combination, and the influence of critical structural parameters on torque. In order to find an optimal direct-drive candidate among the three DS-FMDPMMs, a quantitative electromagnetic performance comparison is developed through finite element analysis (FEA). The results show that among the three machines, DS-FMDPMM-I accommodates the highest torque density, lowest torque ripple, best over-load torque capability, and highest efficiency. Moreover, to further verify its performance advantages, the electromagnetic performances of DS-FMDPMM-I and two typical dual-stator flux modulated PM machines are compared through FEA. Finally, a prototype of the optimal DS-FMDPMM is manufactured and tested to validate the effectiveness of FEA-predicted results.

Single-phase permanent magnet linear oscillation actuators (SP-PMLOAs) have been paid extensive attention in the field of compressors and industry applications. However, the detent force of SP-PMLOA limits the effective stroke and affects the control accuracy of the system. The purpose of this paper is to present a novel C-core SP-PMLOA with double stators arc-teeth and auxiliary slots to reduce detent force. Firstly, the basic structure and dynamic equation of the traditional linear oscillation actuator are introduced. Then, considering the magnetic flux leakage, the detent force formulation is derived by the flux tube method, and a novel structure is presented to reduce detent force and increase the effective stroke by the parametric scan. In addition, the traditional and presented structures of C-core SP-PMLOA are analyzed by 2-D finite element analysis (FEA) mothed. Finally, a prototype is built and tested to verify the results of FEA and calculations. The experimental results show that the presented structure can significantly reduce the detent force and increase the effective stroke.