Motor control technology based on integrated meta-heuristic algorithm optimizer

This document addresses the speed-torque problem in motor control technology by proposing an optimizer based on a comprehensive meta-heuristic algorithm. This optimizer effectively mitigates the issue of heuristic optimization algorithms frequently becoming trapped in local optima. By monitoring the state of the "active particles," it provides real-time insights into the optimization process of the meta-heuristic algorithm. Additionally, the presence of the "active particle archive" facilitates the exploration of new optimization landscapes. The incorporation of other heuristic algorithms enables quicker and more accurate identification of suitable solutions. Furthermore, comprehensive performance evaluations using relevant motor model test functions have validated the feasibility of the optimizer. Ultimately, the results demonstrate that the optimizer significantly enhances the adjustment of control parameters for motors, leading to superior control outcomes.

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Published
2024-11-29
DOI
https://doi.org/10.1109/ei264398.2024.10991749
Primary Topic
Advanced Decision-Making Techniques
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article
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article

Motor control technology based on integrated meta-heuristic algorithm optimizer

Wei Wu, Du Shaoqing, Geng Jialong
Advanced Decision-Making Techniques
article

Motor control technology based on integrated meta-heuristic algorithm optimizer

Wei Wu, Du Shaoqing, Geng Jialong
article en

Abstract

This document addresses the speed-torque problem in motor control technology by proposing an optimizer based on a comprehensive meta-heuristic algorithm. This optimizer effectively mitigates the issue of heuristic optimization algorithms frequently becoming trapped in local optima. By monitoring the state of the "active particles," it provides real-time insights into the optimization process of the meta-heuristic algorithm. Additionally, the presence of the "active particle archive" facilitates the exploration of new optimization landscapes. The incorporation of other heuristic algorithms enables quicker and more accurate identification of suitable solutions. Furthermore, comprehensive performance evaluations using relevant motor model test functions have validated the feasibility of the optimizer. Ultimately, the results demonstrate that the optimizer significantly enhances the adjustment of control parameters for motors, leading to superior control outcomes.

Shenyang University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 51%
Advanced Decision-Making Techniques
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Motor control technology based on integrated meta-heuristic algorithm optimizer — Wei Wu, Du Shaoqing, et al. · (2024) | TGRS Research Map | TGRS