Tomasz Pajchrowski

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

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.

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.

Robust control of PMSM system using the structure of MFC

Purpose – The purpose of the paper is to find a speed control structure with two degrees of freedom robust against drive parameters variations. Application of structure model following control (MFC) and fuzzy technique in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approach – The use of proper structure with two degrees of freedom and non‐linear characteristic introduced by fuzzy technique ensures the robustness of the speed control system. The paper proposes a novel approach to MFC synthesis to be performed in two stages. The first stage consists in the set value of P type controller of model and the process controller simultaneously should be designing by fuzzy technique. At the second stage of the synthesis consist in tuning parameters of process fuzzy controller by the swarm of particles method (particle swarm optimization) on the basis of a defined quality index formulated in the paper. The synthesis is performed using si...

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...

Application of neuro‐fuzzy techniques to robust speed control of PMSM

Purpose – The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.Design/methodology/approach – The robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setu...

Artificial neural network based robust speed control of permanent magnet synchronous motors

Purpose – The aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.Design/methodology/approach – The robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control ...

Two Approaches to Speed Control for Multi-Mass System With Variable Mechanical Parameters

This paper is dedicated to the motion control system of a multimass mechanism with occurring resonances and variable moment of inertia. The proposed concept of control structure for such mechanism consists of a filter damping higher resonance frequencies and an adaptive speed controller. The filter has properly selected fixed characteristics giving a compromised filtering effect in a range of resonance frequency variation, caused by the variable moment of inertia. The used neural adaptive controller realizes active damping of lower resonance frequencies, which are not eliminated by the resonance filter. This original concept is compared with a more complex solution, treated as a reference, which contains an offline-tuned resonance filter together with an offline-adjusted PI-two degrees of freedom controller. Good speed control dynamic properties of the proposed concept in comparison with the reference complex solution are proven by laboratory results.