In the paper a comparative study of the two control structures based on MPC (Model Predictive Control) for an electrical drive system with an induction motor are presented. As opposed to the classical approach, in which DFOC (Direct Field Oriented Control) with four controllers is considered, in the current study only one MPC controller is utilized. The proposed control structures have a cascade free structure that consists of a vector of electromagnetic (torque, flux) and mechanical (speed) states of the system. The first investigated framework is based on the finite-set MPC. A short horizon predictive window is selected. The continuous set MPC is used in the second framework. In this case the predictive horizon contains several samples. The computational complexity of the algorithm is reduced by applying its explicit version. Different implementation aspects of both MPC structures, for instance the model used in prediction, complexity of the control algorithms, and their properties together with the noise level are analyzed. The effectiveness of the proposed approach is validated by some experimental tests.

This paper presents an application of an Unscented- and a Fuzzy Unscented- Kalman Filter (UKF and FUKF) to the estimation of mechanical state variables and parameters in a drive system with an elastic connection. The cascade control structure incorporating an IP controller supported by two additional feedbacks and suitable adaptation mechanism is investigated in this study. The coefficients of the control structure are retuned on the basis of the value of mechanical parameters estimated by filter. The effectiveness of the proposed approaches (classical and fuzzy) is researched through simulation and experimental tests.

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

In this work, issues related to the application of the D-decomposition technique to selection of the controller parameters for a drive system with flexibility are presented. In the introduction the commonly used control structures dedicated to two-mass drive systems are described. Then the mathematical model as well as control structure are introduced. The considered structure has only basic feedbacks from the motor speed and PI type controller. Due to the order of the closed-loop system, the free location of the system’s poles is not possible. Large oscillations can be expected in responses of the plant. In order to improve the characteristics of the drive, the tuning methodology based on the D-decomposition technique is proposed. The initial working point is selected using an analytical formula. Then the value of controller proportional gain is decreasing, until the required value of overshoot is obtained. In the paper different advantages of the D-decomposition technique are presented, for instance calculation of global stability area for the selected gain and phase margin, the impact of parameter changes, and additional delay evident in the system. Theoretical considerations are confirmed by simulation and experimental results.

Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically the most common. This article focuses on presenting a diagnostic tool for bearing conditions based on mechanic vibration signals using convolutional neural networks (CNN). This article presents an alternative to the well-known classical diagnostic tools based on advanced signal processing methods such as the short-time Fourier transform, the Hilbert–Huang transform, etc. The approach described in the article provides fault detection and classification in less than 0.03 s. The proposed structures achieved a classification accuracy of 99.8% on the test set. Special attention was paid to the process of optimizing the CNN structure to achieve the highest possible accuracy with the fewest number of network parameters.

Model Reference Adaptive Systems (MRASs) are one of the most commonly applied techniques to estimate induction motor speed. They ensure simple and algorithmic way to obtain all necessary variables to create a sensorless drive, or to provide the estimated speed signal for redundant system. However, one of the most popular MRAS-type estimators, MRAS-CC (C – Current estimator, C – Current flux simulator) is unstable in a large part of the regenerating mode operation. In this paper it is proposed to apply the multilayer observer theory to ensure the stability, while preserving large insensitivity to motor parameters. The proposed MRAS-ML (ML – MultiLayer) estimator includes two different MRAS-type estimators, unlike previous multilayer solutions with multiple identical observers, but with different parameters. Additionally, no auxiliary parameters need to be selected and it is not necessary to switch off any additional adaptation mechanism. It is shown that the MRAS-ML is stable in the whole regenerating mode operation using both simulation and experimental tests.