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Surface permanent magnet synchronous machines are one of the most widely adopted machine topologies in high-speed applications where efficiency and power factor cannot be compromised. Although the design of such machine type has been extensively investigated in both industry and academia, this work aims at addressing its limitations when applied in high-speed applications. First, this paper proposes an accurate design methodology for continuous-duty high-speed surface-mounted permanent magnets synchronous machines, capable of accounting for the rise of the speed-dependent losses and structural needs with a limited impact on the computational burden. The outlined approach can be used to speed-up the initial design stage as it allows to reduce the number of solutions to evaluate before commencing the refinement stages required before the definition of the final design. Indeed, the introduced design approach is used to systematically assess the maximum power capability as function of the maximum speed and the airgap thickness for a given outer envelope and cooling system. The influence of the high-speed limiting factors is deeply investigated also considering their effects on the machine geometries providing the highest torque. The selection of the final design is discussed and justified. Experimental results of the 4.2 kW-80 kprm prototype validate the design methodology.

This article proposes a modulated model predictive torque and flux control (M2PTFC) method with low complexity for a two-level voltage source inverter (2L-VSI)-fed permanent magnet synchronous motor (PMSM). The proposed strategy aims to reduce the computation burden and simplify the control implementation of the conventional M2PTFC scheme by reducing the number of candidate voltage vectors at every control sample to the minimum (i.e., only two candidates) and eliminating the weighting factor of the cost function and its corresponding tuning procedures. For these purposes, the proposed method devotes different control objectives to the duty modulation and the cost function evaluation processes while executing them sequentially. First, aiming at torque ripple reduction, a duty modulation strategy is proposed based on the analysis of torque deviations produced by different voltage vectors, which ensures proper selection of the two candidates and restricts active voltage vectors (AVVs) that cause high deviations. Then, these candidates are evaluated based on a cost function assigned to achieve the stator flux control objective. The effectiveness of the proposed control scheme is verified through comparative assessment with the conventional M2PTFC and two existing simplified methods by means of simulation and experimental results.

Human–robot-collaboration requires robot to proactively and intelligently recognize the intention of human operator. Despite deep learning approaches have achieved certain results in performing feature learning and long-term temporal dependencies modeling, the motion prediction is still not desirable enough, which unavoidably compromises the accomplishment of tasks. Therefore, a hybrid recurrent neural network architecture is proposed for intention recognition to conduct the assembly tasks cooperatively. Specifically, the improved LSTM (ILSTM) and improved Bi-LSTM (IBi-LSTM) networks are first explored with state activation function and gate activation function to improve the network performance. The employment of the IBi-LSTM unit in the first layers of the hybrid architecture helps to learn the features effectively and fully from complex sequential data, and the LSTM-based cell in the last layer contributes to capturing the forward dependency. This hybrid network architecture can improve the prediction performance of intention recognition effectively. One experimental platform with the UR5 collaborative robot and human motion capture device is set up to test the performance of the proposed method. One filter, that is, the quartile-based amplitude limiting algorithm in sliding window, is designed to deal with the abnormal data of the spatiotemporal data, and thus, to improve the accuracy of network training and testing. The experimental results show that the hybrid network can predict the motion of human operator more precisely in collaborative workspace, compared with some representative deep learning methods.

Synchronous reluctance (Syn-Rel) machines with embedded permanent magnets (PMs) are research hotspots in variable speed motor drives due to their robust rotor structure and wide constant power speed range (CPSR). In this article, the potential of PM-assisted Syn-Rel machine to be next generation heavy duty traction motor solution has been investigated, with special attention put on one key geometric parameter, i.e., airgap length. Careful machine design and optimization has been conducted based on geometric parametrization including airgap length variation, for 15000 rpm peak speed and 350 kW peak power output. In low speed operations, the influence of airgap length on different torque components has been analyzed in detail based on the frozen permeability method. In field weakening region, the variation trend of several key parameters such as output power, torque ripple, and power losses have been investigated along with airgap length. It is found that with high electric and magnetic loading, reducing the electromagnetic airgap length is not always beneficial. There exists a suitable airgap length value to comprehensively balance torque/power density, cooling capability, efficiency and reliability. Numerical FEA and experimental tests of the prototype are combined to verify the conclusions.