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对于表现出非最小阶段和整合行为的工厂,强大的控制系统的多目标最佳合成
M Sai Neeharika1, V Shobhana1, Nitish Katal2
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, TN, India.
Scientific reports
|November 19, 2025
概括
经典的控制器设计方法往往会为复杂的系统提供低于最佳的性能. 这项研究提出了一种使用遗传算法的多目标优化方法,以提高控制系统的稳定性,性能和稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 优化技术 优化技术
- 机器人技术 机器人技术 机器人技术
背景情况:
- 经典的控制器设计方法与复杂的工厂动态作斗争,导致性能低于最佳和强度有限.
- 挑战包括整体特征,非最小相位零和时间延迟,影响稳定性和时间域行为.
研究的目的:
- 开发一种最佳和强大的控制器合成方法.
- 通过提高稳定性,动态性能和强度来解决经典方法的局限性.
- 为了制定控制器合成作为一个多目标优化问题.
主要方法:
- 控制器综合形式作为一个多目标优化问题.
- 目标函数包括峰值灵敏度,整平方误差,控制力,相位边缘和延迟边缘.
- 用于解决优化问题的多目标遗传算法.
- 采用K-Means集群和乌托邦点确定,从帕雷托最佳解决方案中选择理想的控制器.
主要成果:
- 拟议的控制器在各种系统 (集成,直流电机,水力发电,合) 中显示出显著的定量性能改进.
- 在稳定性,动态性能和强度指标方面显示出优于传统方法的优势.
- 蒙特卡洛模拟证实了拟议的控制器的增强强性.
结论:
- 多目标优化方法为复杂的控制系统设计提供了比经典方法更好的替代方案.
- 拟议的方法有效地提高了控制器的性能,稳定性和稳定性.
- 综合和选择方法提供了一种系统的方式,以实现最佳的控制解决方案.
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