Jaehoon Shim

In saliency-based sensorless control with switching frequency voltage injection, the accuracy of rotor position estimation is deteriorated by the injected voltage errors arising from pulsewidth modulation (PWM). To resolve this issue, current derivatives within an active voltage vector are employed for precise position estimation. Accurate measurement of current derivatives requires a sufficient active vector time, necessitating additional voltage injection in low-speed regions. Considering the increased losses from additional injection, this article proposes two types of voltage injection methods that ensure only the minimum active vector time without modifying the PWM scheme. In the proposed methods, the magnitude and direction of injected voltages are analytically determined by considering the angle between the injected and fundamental voltages. Consequently, the proposed injection methods reliably ensure the minimum active vector time regardless of the load conditions, thereby improving the position estimation accuracy. Experimental results under various operating conditions verify the effectiveness of the proposed methods.

This paper presents a data-driven method for position sensorless control of permanent magnet synchronous motors (PMSMs), specifically for improving performance in the low-speed region. Unlike previous data-driven approaches, this study introduces three key contributions. First, this study presents the design process of the neural network (NN)-based position estimator, with input feature selection through a grid search method. Second, an effective loss function and learning algorithm are proposed and utilized to improve the training performance of the NN-based estimator. Finally, a data augmentation method is introduced to efficiently construct the training dataset and reduce the data acquisition time. As a result, the proposed input features and training algorithm significantly improve the position estimation accuracy of the NN-based estimator when compared to existing data-driven methods. Furthermore, the developed model is implemented on the digital signal processor (DSP) to validate its real-time operation. The operable region in the four-quadrant operation is expanded by 49.80% in the low-speed region compared to the recently studied model-based method. Moreover, robustness under dynamic state operation is verified.

This study introduces a data-driven approach for diagnosing faults in electric drive systems on embedded systems. The method is designed to diagnose current hall sensor offset, scale errors, and power switch open faults without the need for additional sensors or external computing resources. The proposed approach addresses three key points. Firstly, it identifies and suggests data types in electric drive systems that are rich in fault-related information. Additionally, it uses statistical analysis to propose an effective fusion method for the input data types to create a diagnostic model among the various candidates. Secondly, a down-sampling method utilizing average pooling is introduced to effectively sample data from the large volume of raw data processed by the digital signal processor (DSP), preserving essential information while reducing data size. Lastly, a diagnostic scheme employing a neural network-based classifier is proposed to accurately diagnose faults using the down-sampled data. The proposed method’s effectiveness, from sampling to diagnosis, is demonstrated through both analytical and statistical validation. Furthermore, real-time diagnostic results are presented using the TMS320F28379S DSP.

This study proposes a model for detecting anomalies in induction motors using audio signals. Compared to existing anomaly detection methods based on deep generative models, the model is presented in two main aspects. First, the proposed model consists of two generative adversarial network (GAN) modules, each processing the two types of mel-spectrograms with distinct time and frequency resolutions. These modules are trained to reconstruct input data effectively, enabling each module to extract the high-resolution time and frequency domain features. Secondly, the ensemble approach is proposed for calculating the anomaly score and loss function. This approach ensembles all the reconstruction losses and Comprehensive Feature Distances (CFDs) obtained from the two GAN modules. The CFD is calculated as the Maximum Mean Discrepancy (MMD) between the outputs of the encoder and decoder layers of the generator. The effectiveness of the proposed scheme is validated through experiments conducted with 0.75 kW induction motors.

Estimating contact force for a robot manipulator without an force/torque (F/T) sensor poses challenges due to uncertain torques, such as backlash and flexibility. To address this limitation, this article proposes a data-driven uncertain torque model and an overall gray-box structured approach. The contributions of this article are threefold. First, a joint domain unified neural networks (DUNNs)-based model is proposed to compensate for the uncertain torques. This model effectively captures uncertain torques beyond studies focusing solely on individual uncertainty. Second, the DUNNs model receives dynamic and joint domain information, enabling a single DUNNs model to estimate all joint uncertain torques through joint domain knowledge. This approach reduces the model size while maintaining performance. Third, the structure in which the DUNNs model works with conventional static friction models is introduced. This structure improves contact force estimation performance and enhances robustness against untrained data compared with the black-box model. Experimental results verify the method's effectiveness.

This paper proposes an iterative scheme for the magnetic field synthesis of electromagnetic navigation systems (eMNS) in medical applications. The proposed method synthesizes the magnetic field with the iterative calculation of pseudoinverse while limiting multiple over-current references in an iteration instead of limiting a single maximum over-current reference. The proposed approach is analyzed and compared to the previous approaches of synthesizing the magnetic field. It is shown that the proposed approach achieves 4 mT larger magnetic field and 7 kW less electrical power loss than the previous synthesis methods. Moreover, the execution time of iterative pseudoinverse with limiting multiple over-currents is faster than limiting a single over-current by 20 μs.

Electromagnetic navigation systems (eMNS) for medical applications, such as cardiovascular diseases, employ stationary coils and soft magnetic cores. Electrical currents in the coils synthesize magnetic fields to wirelessly control millimeter-sized microrobots, such as magnetic guidewires. Previous control methods of calculating the current references for the eMNS focus on either low conduction loss with pseudoinverse or wide magnetic-feasible workspace (MFW) with a linear combination of pseudoinverse and minimum infinity-norm. However, with the merits of both wide MFW and low conduction loss, this article proposes an iterative control method that modifies the initial pseudoinverse iteratively, while ensuring that the current limits of the coils are not exceeded. A detailed description of the control method, accompanied by a comprehensive analysis, is provided. The prototype of eMNS with eight coils is used for the experimental verification. It is shown that, in the current limits, MFW is increased by more than 45% compared with the pseudoinverse method, and the conduction loss is reduced by more than 2 kW compared with the linear combination method.