Adaptive EKF algorithm with novel fading memory: Design and validation

ZERDALİ E., Yildiz R., ÖZBEK L., Barut M.

COMPUTERS & ELECTRICAL ENGINEERING, vol.123, 2025 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 123
  • Publication Date: 2025
  • Doi Number: 10.1016/j.compeleceng.2025.110130
  • Journal Name: COMPUTERS & ELECTRICAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Ankara University Affiliated: Yes

Abstract

In this paper, an adaptive extended Kalman filter (EKF) with a novel fading memory is proposed and validated through its application to the state estimation of an induction motor (IM). A standard EKF (SEKF) observer requires a stochastic system with complete dynamic or measurement equations to achieve estimations. However, in practice, these equations are often partially known or entirely unknown, which degrades the performance of the SEKF. To address this issue, an EKF observer with a novel fading memory is proposed and applied to the state estimation problem of an IM. To validate its effectiveness, the proposed adaptive fading EKF (AFEKF) is experimentally compared to the SEKF and an existing AFEKF from different perspectives. The results confirm that the proposed AFEKF achieves better estimation performance with reduced computational complexity compared to the existing AFEKF approach.