Control and Parameter Estimation of PMSM by Runge-Kutta Model Based Predictive Control

Akpunar, Adile; İplikçi, Serdar

doi:10.1109/atee52255.2021.9425209

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…There are different methods for online parameter estimation such as recursive least square (RLS) [23]− [28], model reference adaptive system (MRAS) [29], extended Kalman filter (EKF) [30]− [32], particle swarm optimization (PSO) [33]− [38], genetic algorithm-based methods [39], modified Jaya algorithm [40], machine learning (ML) algorithm [41], moving horizon estimator (MHE) [42], Runge-Kutta model based predictive method [43], recursive error prediction method (RPEM) [44], impedance methods [45], [46], and Gauss Newton method [47]. This paper includes different numerical methods with their characteristics and basic working process.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of features of online numerical methods used for parameter estimation of PMSM

Naikawadi

Patil

Kalantri

et al. 2022

IJPEDS

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As permanent magnet synchronous motors (PMSM) have high power density, efficiency, good dynamic performance, and small size they are becoming popular in electric vehicle (EV) applications. Control performance and the efficiency of the system get affected due to electrical, mechanical parameters. Parameters value gets affected by voltage source inverter (VSI) non-linearities, temperature and magnetic saturation effects. If exact parameters for particular torque speed requirement are found, the efficiency of system increases. There are various offline and online methods for finding parameters. Offline methods are easy to implement but requires extra setup and estimate parameters in steady state. Because the effects of transient conditions are taken into account during identification, online methods for obtaining real-time data under running conditions are becoming more popular. An overview about online numerical methods to estimate electrical parameters of PMSM is given. It discusses difference between various methods in terms of computational cost, convergence speed, noise and identification error. Choosing of method will be easy using this work. For inductance estimation, the extended Kalman filter (EKF) algorithm has an identification error of 0.24% under temperature effect and -0.3% under VSI non-linearities effect. The identification error for Rs and ψf using the recursive least square (RLS) method is 0.5% and 0.02%, respectively, when temperature is considered. EKF and RLS algorithms are proposed.

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