Krzysztof Zawirski

Application of Artificial Neural Network to Robust Speed Control of Servodrive

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

New approaches to selected problems of precise speed and position control of drives

The paper reviews the literature in the field of electric drives control, published in the years 2010 - 2012. Attention is focused on three selected themes: torque and force ripple elimination for converter controlled drives, friction identification and compensation, damping of torsional vibrations. These problems are critically important, especially for servo drives used in robots and machine tools.

Application of artificial neural network for adaptive speed control of PMSM drive with variable parameters

Purpose – The aim of the research was to find out a method of adaptive speed control robust against variation of selected parameters of system like moment of inertia, time constant of torque control loop or torque coefficient of the motor.Design/methodology/approach – The main goal of the research was achieved due to application of artificial neural network (ANN), which was trained on line on the base of speed control error. The good results were gained by elaboration of enough fast and precise training algorithm and proper ANN structure.Findings – The work shows a structure of artificial neural network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some versions of this algorithm were analysed and verified by simulation and experimental tests.Research limitations/implications – The research should be continued to determine a final version of training algorithm and its influence on controller properties.Practical implications – The elaborated adaptive controller ca...

Neural Speed Controller Trained Online by Means of Modified RPROP Algorithm

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.

Robust speed and position control of PMSM

In the paper, the problem of robust speed and position control of a permanent magnet synchronous motor is analysed. The idea of the proposed speed controller is based on the combination of sliding mode control with fuzzy logic rules. The introduction of an integral module to the sliding mode controller leads to fast system dynamics of slight sensitivity to the load parameters variation, effective chattering reduction and elimination of steady state control error. Fuzzy adaptive mechanism tuning a switching gain of sliding-mode controller reduces the speed dynamic error caused by change of load torque. Experimental results obtained during investigation of constructed laboratory stand verify theoretical results and prove good quality of speed and position control.

Adaptive observer of rotor speed and position for PMSM sensorless control system

This paper deals with the problem of rotor speed and position detection in sensorless permanent magnet synchronous motor (PMSM) drives. A concept based on detecting the back EMF induced in stator windings was developed and modified. A general structure of an adaptive observer with the proportional‐integral function of a corrector is introduced. The non‐stationary character of the observer presented in this paper requires an adaptive change of observer corrector settings. Such observer structure was implemented on a DSP system and verified experimentally. Both simulation and experimental results show good properties of the proposed observer structure.

Speed controller for a drive with complex mechanical structure and variable parameters

The paper presents the problem of obtaining a good dynamics of the PMSM drive with complex mechanical structure and variable moment of inertia. Proposed approach bases on proper design of feedback filter to reduce influence of mechanical resonances and applying a neural speed controller trained online, which can adapt itself to variable inertia. As an alternative, robust PI controller with set-point weighting is presented. The results of simulation and experimental investigations show good properties of designed control system.

Modified sliding-mode speed controller for servo drives

In the paper a problem of robustness of sliding-mode speed control for AC drives is analysed. Some high sensitivity of sliding mode speed controllers to high frequency periodic disturbances is shown. The proposed modified structure of sliding mode controller bases on introduction of filtering module, what leads to good system dynamics of slight sensitivity to the load parameters variation, effective chattering reduction and elimination of steady state control. Proposed general structure is universal and can be applied not only in speed control systems. Results of computer simulation study proved low sensitivity of classical sliding mode controller to high frequency disturbances and showed clearly the advantages of elaborated control method. Experimental investigation of constructed laboratory stand verify theoretical investigations and prove main simulation results.

Adaptive neural speed controller for PMSM servodrive with variable parameters

The paper presents an analysis of adaptive neural controller operation for servodrive with variable moment of inertia. An influence of assumptions concerning neural network structure and its training method on control properties are presented on the base of simulation. Simulation results are proved by experiment.

Optimal control of current switching angles for high‐speed SRM drive

Purpose – The purpose of this paper is to propose a method of optimal control of current commutation of switched reluctance motor drive.Design/methodology/approach – The problem of optimal current commutation control is solved by off‐line selection of switching‐on and switching‐off angles. Selection of optimal values of angles is provided on computer model of the drive with help of particle swarm optimisation method. The optimal angle values are detected as functions of phase current and rotor speed. These calculated optimal values are stored in microcomputer control system memory in form of two‐input look‐up tables. The results are validated on laboratory set up.Findings – Three different criteria of optimal control, which are taken into account: the maximum electromagnetic torque for given reference current, the maximum ratio of electromagnetic torque to root mean square value of phase current and the minimum electromagnetic torque ripples, gave a good results validated by simulation and experimental in...