一个精英的方法来重新设计Aquila优化器,以实现高效的AFR系统控制
Davut Izci1,2, Serdar Ekinci1, Abdelazim G Hussien3,4
1Department of Computer Engineering, Batman University, Batman, Turkey.
PloS one
|September 20, 2023
概括
本研究介绍了一种增强的Aquila优化器 (ImpAO),用于精确控制精益燃烧发动机中的空气-燃料比率 (AFR). ImpAO显著提高了控制精度和短暂响应,为减少排放提供了优越的解决方案.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 燃烧科学 燃烧科学
背景情况:
- 在精益燃烧火花点火发动机中优化空气-燃料比 (AFR) 控制对于减少排放和应对气候变化至关重要.
- 现有的控制方法需要改进,以满足严格的环境法规并提高发动机效率.
研究的目的:
- 开发和评估一个增强的 Aquila 优化器 (ImpAO) 以优化 AFR 控制系统中的前 (FF) 和比例整数 (PI) 控制器参数.
- 为了证明ImpAO在控制精度,稳定性和短暂响应方面比最先进的和元启发式算法更优越.
主要方法:
- 开发了一种增强的Aquila优化器 (ImpAO),结合了修改的精英反对派学习技术.
- 使用ImpAO优化了前 (FF) 机制的参数和用于AFR控制的比例整数 (PI) 控制器.
- 进行了模拟研究,将ImpAO的性能与既有和最近的元启发算法进行比较.
主要成果:
- ImpAO实现了0.6759的最低成本函数值,显示出强大而稳定的性能 (平均±std dev:0.6823±0.0047).
- 统计分析 (Wilcoxon签名等级测试) 与其他算法相比证实了显著的性能差异 (p<0.001).
- ImpAO表现出优越的短暂响应指标,包括较低的上升时间 (1.1845秒) 和沉降时间 (3.0188秒),并减少了超越 (0.1679%).
结论:
- 拟议的ImpAO算法为优化AFR控制系统提供了高效和可靠的解决方案.
- 在控制精度,瞬态性能和计算效率方面,ImpAO超越了现有的最先进和元启发算法.
- ImpAO代表了精益燃烧发动机在AFR控制方面的重大进步,有助于减排和气候变化努力.
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