This paper introduces a new solution for the speed and current sensor fault-tolerant direct field-oriented control of induction motor drives. Two self-adjusting observers derived from a modified current-based model reference adaptive system (CB-MRAS) are presented. Finally, the recursive least squares method was used to estimate the parameters of the used observers. The method, in the proposed solution, provides a very fast and accurate finding of the observer parameters while maintaining relative simplicity and ease of implementation. The presented algorithm eliminates the CB-MRAS observer dependence on the induction motor parameters and also compensates for the inaccuracies in the evaluation of the stator voltage vector. The proposed fault-tolerant control offers the drive operation while either a speed sensor or one/two current sensors fault occurs. The drive still works with the direct field-oriented control even when no current sensors are healthy. The proposed scheme was simulated in the MATLAB/Simulink software environment. Then the algorithm was implemented in a floating-point digital signal controller (DSC) TMS320F28335 and tested on an induction motor drive prototype of rated power of 2.2 kW to validate the proposed schemes.

This paper focuses on the development and optimization of a special hybrid electric vehicle arrangement known as a four-quadrant rotary converter. The introduction summarizes the main advantages and disadvantages of existing topologies in radial and axial flux arrangements. Based on previous experience, we developed a novel axial flux arrangement that eliminates the problems and disadvantages associated with existing radial flux solutions. In addition, this paper evaluates and subsequently describes the optimization of permanent magnet parameters in an axial flux rotary converter unit. A number of 3D finite element method optimizations were performed to find the optimal mass distribution of permanent magnets on the frontal area of the outer rotor in the axial flux rotary converter unit. The optimization involved the permanent magnets’ material, shape, and thickness in order to achieve maximal efficiency of the entire unit while leaving its nominal output power and speed unaffected. The results show an increase in the overall theoretical efficiency of the outer rotor unit from 90.2% to 94.4% following the optimization.

The paper presents the results of our research on the use of artificial neural networks for sensorless control of induction motor drives. A feedforward artificial neural network with one hidden layer was designed and trained offline to act as a model of induction motor, which directly provides the actual speed of a drive. The model was subsequently incorporated in the field-oriented control scheme, where it fully replaces an incremental encoder. The presented solution was tested out using an experimental drive equipped with a 2.2 kW induction machine and controlled by a control system which is based on the TMS320F28335 digital signal controller. The obtained experimental results show a high level of accuracy in the low speed range.

This article presents a comprehensive system for testing and verifying shunt active power filter control methods. The aim of this experimental platform is to provide tools to a user to objectively compare the individual control methods. The functionality of the system was verified on a hardware platform using least mean squares and recursive least squares algorithms. In the experiments, an average relative suppression of the total harmonic distortion of 22% was achieved. This article describes the principle of the shunt active power filter, the used experimental platform of the controlled current injection source, its control system based on virtual instrumentation and control software and ends with experimental verification. The discussion of the paper outlines the extension of the experimental platform with the cRIO RTOS control system to reduce the latency of reference current generation and further planned research including motivation.

This paper describes a hardware and software solution for data acquisition [1]–[4] for implementation in the field of modern sensorless control methods of induction motor drive [5], where especially adaptive control methods based on Artificial Neural Network (ANN) require a large amount of training data for their operation. Also in industrial applications nowadays, diagnostic systems are often used. These systems work on the principle of data acquisition and enable detection of unwanted faults during stable operation of automated technologies and data collection of quantities that are accessible to superior systems and seemingly unrelated to a motor drive control, such as information about the temperatures and mechanical state. In this way, for example, it is possible to overall increase productivity and safety over the lifetime of the automated technology. This article is divided into several parts. In the first part, based on the requirements, analysis, and design of a suitable solution for data acquisition and advantages over other solutions is performed. The second part of the paper deals with the hardware design of the data acquisition system solution. The third part is focused on the software design and implementation in the LabVIEW and MATLAB environment. The last part of the article is devoted to the analysis of experimental results.

High Frequency Multipurpose SiC MOSFET Driver