All-Solid-State Marx Generator Based on Genetically Optimized Fuzzy PID Control Algorithm

To address the nonlinear characteristics of the all-solid-state Marx generator that vary over time, a fuzzy PID control strategy optimized by a genetic algorithm is proposed. The genetic algorithm's global optimization capability is utilized to recalibrate the quantization and proportional factors in the fuzzy PID controller, enhancing control response speed and system robustness. The overall hardware structure and operating principle of the all-solid-state Marx generator are analyzed, and the implementation steps of the genetic algorithm-optimized fuzzy PID algorithm are designed. MATLAB/Simulink simulations, and experimental validation are conducted. The results demonstrate that, under identical conditions, the genetic algorithm-optimized fuzzy PID controller achieves faster response, reduced overshoot, and superior control performance compared to conventional fuzzy PID and PID controllers.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/icaace65325.2025.11020373
Citations
1
Primary Topic
Advanced Surface Polishing Techniques
Type
article
Field-Weighted Citation Impact
0.67
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article

All-Solid-State Marx Generator Based on Genetically Optimized Fuzzy PID Control Algorithm

Hongzheng Dong, Yunwang Ge, Yunfan Yang
1 citations
Advanced Surface Polishing Techniques
0.67
article

All-Solid-State Marx Generator Based on Genetically Optimized Fuzzy PID Control Algorithm

Hongzheng Dong, Yunwang Ge, Yunfan Yang
article en
1 citations

Abstract

To address the nonlinear characteristics of the all-solid-state Marx generator that vary over time, a fuzzy PID control strategy optimized by a genetic algorithm is proposed. The genetic algorithm's global optimization capability is utilized to recalibrate the quantization and proportional factors in the fuzzy PID controller, enhancing control response speed and system robustness. The overall hardware structure and operating principle of the all-solid-state Marx generator are analyzed, and the implementation steps of the genetic algorithm-optimized fuzzy PID algorithm are designed. MATLAB/Simulink simulations, and experimental validation are conducted. The results demonstrate that, under identical conditions, the genetic algorithm-optimized fuzzy PID controller achieves faster response, reduced overshoot, and superior control performance compared to conventional fuzzy PID and PID controllers.

Henan University of Science and Technology (CN), Luoyang Institute of Science and Technology (CN)
Openalex Percentile: Top 31%
Advanced Surface Polishing Techniques
0.67
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