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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]

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. Index Terms--Neural network applications, permanent magnet motors, robustness.

This paper presents a comparative study of a drive system with a permanent magnet synchronous motor fed by a matrix converter and a voltage source inverter with diode rectifier stage. A rotor-oriented vector control is implemented in the drive control. Space vector modulation techniques are used for both converters in the output current modulation process. Initial simulation test results and comparison are presented. The advantages of both solutions are indicated. Special attention is given to the drive control in the low speed range. The main aim of this article is to present drive systems with a matrix converter as an interesting alternative solution for automation systems for precision control of speed and position. The technological contribution of this study lies in its potential application to robotics. [ABSTRACT FROM AUTHOR]

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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 presents original concepts of control systems for an electrical drive with an elastic mechanical coupling between the motor and the driven mechanism. The synthesis procedure of the speed controller uses a proposed quality index (cost function) of system operation ensures the minimization of both tracking errors and torsional vibrations. Proper selection of the cost function focusses more on the reduction of torsional vibrations due to their negative influence on the drive's mechanical coupling vitality. The omission of the plant identification of an adaptive fuzzy controller was proposed. Two types of fuzzy controllers were analyzed, namely with type I and type II fuzzy membership functions. The novelty of the presented approach is in the application of a Petri transition layer in a type II fuzzy controller which reduces the numerical complexity in case of a large number of complicated type II fuzzy sets. The presented simulation and experimental results prove that the best dumping of mechanical vibrations ensures the adaptive fuzzy controller with type II functions and a Petri transition layer. [ABSTRACT FROM AUTHOR]

The authors reflect on the papers submitted at the 13th International European Power Electronics and Drives-Power Electronics and Motion Control (EPE-PEMC) conference held in Poznan, Poland on September 1-3, 2008. They note the topics discussed at the conference like power converters and semiconductor devices and packaging. They also acknowledge the support provided by some professionals including professors Viliam Fedak and Lech M. Grzesiak for the selection process of the papers from 500 specialists.