Asenkron Motorların GKF Tabanlı Model Öngörülü Moment Kontrolü

Year 2021, Issue: 32, 858 - 863, 31.12.2021

Yunus Emre Altınışık , Rıdvan Demir

https://doi.org/10.31590/ejosat.1041552

Abstract

Bu çalışmada rotor direncinin kestirimi ile kontrol başarımı iyileştirilmiş hız-algılayıcısız model öngörülü moment kontrol (model predictive torque control, MPTC) tabanlı asenkron motor (ASM) sürücüsü sunulmuştur. Bu amaçla, MPTC’nin yüksek başarımlı hız kontrolü için gerekli olan hız ve akı bilgisine ek olarak rotor direnci kestirimi; girişinde ölçülen stator gerilim ve akımlarını kullanan genişletilmiş Kalman filtresi (GKF) tarafından gerçekleştirilmiştir. GKF tarafından kestirilen rotor direnci MPTC sistemi içerisine her bir örnekleme adımında güncellenerek parametre değişimlerinden kaynaklanan bozulmalar azaltılmıştır. Tasarlanan GKF algoritması ve bu algoritmayı kullanan MPTC tabanlı ASM sürücüsü yük momenti ve rotor direnci değişimlerini içeren zorlayıcı senaryolar altında geniş bir hız aralığında benzetim ortamında test edilmiş ve doğrulanmıştır. Elde edilen benzetim sonuçları, GKF algoritmasının yüksek kestirim başarımına, buna bağlı olarak hız-algılayıcısız MPTC temelli ASM sürücüsünün ise yüksek kontrol başarımına sahip olduğunu onaylamaktadır.

Keywords

Model öngörülü kontrol, Asenkron motorlar, Genişletilmiş Kalman filtresi.

EKF Based Model Predictive Torque Control of Induction Motors

Abstract

In this study, speed-sensorless model predictive torque control (MPTC) based induction motor (IM) drive that control performance is improved by estimating rotor resistance is presented. For this purpose, in addition to the speed and flux information required for high-performance speed control of MPTC, estimation of rotor resistance was realized by Extended Kalman filter (EKF) that uses the stator currents and voltages which are measured as inputs. The rotor resistance estimated by GKF is updated to the MPTC system at each sampling step, reducing the deteriorates caused by parameter changes. Designed EKF algorithm and MPTC based IM driver which used this algorithm is tested and confirmed in simulation environment over a wide speed range under challenging scenarios including load torque and rotor resistance variations. The obtained simulation results confirm that the GKF algorithm has high estimation performance, and accordingly the speed-sensorless MPTC-based ASM drive has high control performance.

Keywords

Model predictive control, Induction motors, Extended Kalman filter.

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