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基于改进粒子群算法的泄漏容腔高精度压力控制

钱鹏飞 刘海洋 曹宇轩 黄凯 沈海军

江苏大学学报(自然科学版)2026,Vol.47Issue(5):542-549,8.
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江苏大学学报(自然科学版)2026,Vol.47Issue(5):542-549,8.DOI:10.3969/j.issn.1671-7775.2026.05.006

基于改进粒子群算法的泄漏容腔高精度压力控制

High-precision pressure control of leakage chamber based on improved particle swarm algorithm

钱鹏飞 1刘海洋 1曹宇轩 1黄凯 1沈海军2

作者信息

  • 1. 江苏大学 机械工程学院,江苏镇江 212013
  • 2. 上海卫星工程研究所,上海 201100
  • 折叠

摘要

Abstract

To solve the problems of low efficiency and poor adaptability to complex operating conditions in traditional tuning of pressure controller parameters due to the relying heavily on manual trial-and-error based on empirical experience,the scheme for optimizing controller parameters based on the improved particle swarm optimization(PSO)algorithm was proposed.By the proposed algorithm,the global search capability and optimization performance were improved by removing the velocity term in the traditional position update formulation and incorporating chaotic mapping and somersault foraging strategies.The simulation comparison results on 10 benchmark functions show that the improved algorithm is more advantageous in terms of overall performance.The pressure control results for 30 L chamber show that compared to the trial-and-error method,the optimization method achieves 56.8%reduction in the steady-state error of system under the leakage condition.When the optimized parameters are further applied to the step pressure control under closed condition and constant leakage condition,the system steady-state errors are less than 40 Pa and 90 Pa,respectively,which illuminates that the optimization scheme has good adaptability under different conditions.

关键词

压力控制/参数优化/改进粒子群算法/混沌映射/翻筋斗觅食策略/模糊PI

Key words

pressure control/parameter optimization/improved particle swarm algorithm/chaotic mapping/somersaulting foraging strategy/fuzzy PI

分类

信息技术与安全科学

引用本文复制引用

钱鹏飞,刘海洋,曹宇轩,黄凯,沈海军..基于改进粒子群算法的泄漏容腔高精度压力控制[J].江苏大学学报(自然科学版),2026,47(5):542-549,8.

基金项目

国家自然科学基金资助项目(52575062) (52575062)

江苏大学大学生创新训练计划项目(X2025102990558) (X2025102990558)

江苏大学第24批学生科研课题立项项目(24A020) (24A020)

江苏大学学报(自然科学版)

1671-7775

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