To improve the performance of position sensorless control with a single current sensor in low-speed range, an improved current reconstruction method is proposed. High-frequency voltage is injected to estimate the rotor position. The time interval between two sampling moments is fixed as the minimum sampling window time. Therefore, the phase current can be compensated by an online adaptive one-step method. And the time-aligned three-phase currents can be reconstructed. The influence of the error between time-aligned reconstructed currents and the full sensors sampling currents on sensorless control is discussed. With the proposed method, the injection frequency can be improved to half the switching frequency. Finally, a permanent magnet synchronous motor is sensorless controlled with the proposed method in the experiments.

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled source domain to help the task of target domain with the unlabeled data. It is a key step for UDA to minimize the cross-domain distribution divergence. In this paper, we firstly propose a novel discrepancy metric, referred to as Cross Domain Mean Approximation (CDMA) discrepancy, to evaluate the distribution differences between source and target domains, which calculate the sum of the squares of the distances from the source and target domains to the mean of the other domain. Secondly, Joint Distribution Adaptation based on Cross Domain Mean Approximation (JDA-CDMA) is developed on the basis of CDMA to extract shared feature and simultaneously reduce the marginal and conditional distribution discrepancy between domains during the label refinement process. Thirdly, we construct a classifier utilizing CDMA metric and neighbor information. Finally, the proposed feature extraction approach and classifier are combined to realize transfer learning. Results from extensive experiments on five visual benchmarks including object, face, and digit images, show the proposed methods outperform the state-of-the-art unsupervised domain adaptation.

The permanent magnet linear motor (PMLM) has a variety of outstanding topological structures, such as flux switch structure and transverse flux structure. However, the inherited cogging force of PMLM is difficult to reduce without thrust damage. To solve the above-mentioned problem, this article investigates the thrust force, based on a novel flux-switching transverse-flux PMLM (FSTFPMLM) with a simple structure systematically, and provides the guidance to design the FSTFPMLM to achieve a larger thrust, with smaller cogging force. First, the basic structure and working principle of the FSTFPMLM are introduced. Second, an equivalent permeance model of the FSTFPMLM is proposed to calculate the thrust force, including cogging force, permanent magnet thrust, and winding reluctance force. Then, the leading parameters’ influence on the thrust force is investigated; it is concluded that the method effectively minimizes the cogging force and increases the thrust force. Finally, the prototype is manufactured, experiments are performed, and the experimental results agree well with the finite-element method results. This article verifies the effectiveness and practicability of the motor and the proposed analysis method, pointing out the motor's outstanding advantages in reducing cogging force while maintaining high thrust force.

The use of metallic cans in airgap forms an electromagnetic shielding phenomenon that characterizes a canned electrical motor. Imposed in alternating airgap flux field, cans induct strong and distributive eddy current, the fundamental feature that interacts original flux and determines output features. In particular that induction leads to additional can-shielding loss that is considerably much higher than traditional copper or iron losses. In this article, the can effect in terms of eddy current and loss is studied, based on a state-of-the-art permanent magnet motor. First, distribution characteristics of eddy induction and loss are analyzed via a combination method that each magnetic excitation is separately and then integrally taken. Then, the can loss by magnetic excitation, rotor speed, flux harmonics, as well as the change of traditional losses due to the use of cans, is analyzed. Measurement is taken as verification.

In the field of autonomous driving, safety has always been a top priority, especially in recent years with the development and increasing application of deep reinforcement learning in autonomous driving. Ensuring the safety of algorithms has become an indispensable concern. Reinforcement learning, which involves interacting with the environment through trial and error, may result in unsafe behavior in autonomous driving without any safety constraints. Such behavior could result in the drive path deviation and even collision, causing catastrophic accidents. Therefore, this paper proposes a reinforcement learning algorithm based on a safety experience replay mechanism, which is primarily to enhance the safety of reinforcement learning in autonomous driving. Firstly, the ego vehicle conducts preliminary exploration of the environment to collect data. Based on the performance of completing tasks observed from each data trajectory, safety labels of different levels are assigned to all state-action pairs, which establishes a safety experience buffer. Further, a safety critic network is constructed, which is trained by randomly sampling from the safety experience buffer. This enables the network to quantitatively evaluate the safety of driving actions, and the goal of safe driving for ego vehicle is achieved. The experimental results indicate that the proposed method can effectively reduce driving risks and improve task success rates compared with conventional reinforcement learning algorithms.