Linear Inductance Model Reshaping-Based Sensorless Position Estimation Method for SRM With Antimagnetic Saturation Capability

Sun, Qingguo; Lan, Tianze; Liu, Xu; Li, Shanhu; Niu, Feng; Gan, Chun

doi:10.1109/jestpe.2023.3300881

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Despite their high-speed capabilities, DC motors suffer from a non-absolute efficiency characteristic [31], [32]. A key weakness lies in the occurrence of disturbances in the form of noise ripple when DC motors operate near objects generating magnetic or electromagnetic fields [33]- [35].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis

Setiawan,

Ma'arif,

Rekik

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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The accurate estimation of speed is crucial for optimizing the performance and efficiency of DC motors, which find extensive applications in various domains. However, the presence of noise ripple, caused by interactions with magnetic or electromagnetic fields, poses challenges to speed estimation accuracy. In this article, we propose the implementation of the Kalman Filter method as a promising solution to address these challenges. The Kalman Filter is a recursive mathematical algorithm that combines measurements from multiple sources to estimate system states with improved accuracy. By employing the Kalman Filter, it becomes possible to estimate the true speed of DC motors while effectively reducing the adverse effects of noise ripple. This research focuses on determining the optimal values for the Kalman Filter parameters and conducting experiments on a DC motor to evaluate the performance of the proposed approach. The experimental results demonstrate that the Kalman Filter significantly improves the control of speed oscillations and enhances the stability of the DC motor system. Furthermore, a comprehensive analysis of the system's response and parameter tuning reveals the impact of different parameter combinations on settling time, overshoot, and rise time. By carefully selecting appropriate parameters, the proposed approach contributes to accurate speed estimation and effective control of DC motors, advancing the understanding and application of the Kalman Filter in various relevant fields. Overall, this research provides valuable insights into enhancing the performance and efficiency of DC motors through the integration of the Kalman Filter method.

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