Search

Electrical drive systems are commonly applied for the mechanisms of precise movement, where having a high-quality position and high-quality speed control is especially valuable. Very often, the mechanical part of these systems reveals resonant properties that are related to the limited stiffness of the interconnection between subsequent parts of the mechanism. In most cases, this sort of system may be described as a model of several linked masses. If only the structure of the mechanical part is known and the corresponding parameters are constant and identified, the demanded control quality may be obtained using a properly tuned ADRC or PID controller equipped with appropriate anti-resonance filtration. However, if the parameters of the mechanical part are variant, adaptive control may be considered as a solution. In this paper, artificial neural network (ANN) is considered to be a speed controller and its training method assures adaptation to the unknown mechanical parameters. The paper is particularly focused on a three-mass system, which possesses, due to its structure, two resonant frequencies. The unique property of the analyzed system is the application of drive units at both ends of the system, so that the controller has the ability to influence the resonant system from both sides. The coordination of the drive unit is performed by the aforementioned ANN, from which two outputs affect the drive units independently. The derivation of the mathematical model is followed by its implementation in a computer simulation and finally the evaluation in a dedicated laboratory setup, the construction of which is also presented in the paper. [ABSTRACT FROM AUTHOR]

In this paper, the synthesis and the properties of the neural speed controller trained online are presented. The structure of the controller and the training algorithm are described. The resilient backpropagation (RPROP) algorithm was chosen for the training process of the artificial neural network (ANN). The algorithm was modified in order to improve controller operation. The specific properties of the controller, i.e., adaptation and auto-tuning, are illustrated by the results of both simulation and experimental research. An electric drive with permanent magnet synchronous motor (PMSM) was chosen for experimental research, due to its impressive dynamics. The obtained results indicate that the presented controller may be implemented in industrial applications. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of robust speed control of electrical servodrives. A robust speed controller is developed using an artificial neural network (ANN), which creates a nonlinear characteristic of controller. An original method of neural controller synthesis is presented. The synthesis procedure is performed in two stages. The first stage consists in training the ANN and at the second stage controller settings are adjusted. The use of the proposed controller synthesis procedure ensures robust speed control against the variations of moment of inertia and stator magnetic flux. Simulations and laboratory results validate the robustness of the servodrive with permanent magnet synchronous motor. [ABSTRACT FROM AUTHOR]