最佳代学习PI控制器用于SISO和MIMO流程,具有用于性能预测的机器学习验证
M Nagarajapandian1, S Kanthalakshmi2, P Arun Mozhi Devan3
1Department of Electronics and Instrumentation Engineering, Sri Ramakrishna Engineering College, Coimbatore, 641022, Tamil Nadu, India. nagarajapandian.m@srec.ac.in.
Scientific reports
|October 9, 2024
概括
一个新的代学习控制器,用混合算法进行优化,以补偿截止时间PI,增强工业过程控制. 这种先进的控制器在单输入单输出和多输入多输出系统中显著提高了系统稳定性和响应时间.
科学领域:
- 控制工程 控制工程 控制工程
- 优化算法 优化算法
- 机器学习应用 机器学习应用
背景情况:
- 多变量流程在工业中至关重要,但由于动态变化和可变相互作用,因此难以控制.
- 传统的比例整合 (PI) 控制器虽然简单,但却难以应对多输入多输出 (MIMO) 系统的复杂性.
- 需要先进的控制策略来解决现有的工业过程控制的局限性.
研究的目的:
- 为加强工业过程控制提出一个代学习控制器截止时间补偿PI (ILC-DPI).
- 使用一种新的混合模拟化-狮优化 (SA-ALO) 算法进行控制器调整.
- 使用机器学习 (ML) 验证控制器性能,用于系统响应预测.
主要方法:
- 开发了一种新的ILC-DPI控制器,采用SA-ALO优化算法.
- 在单输入单输出 (SISO) 和四重系统 (MIMO) 上模拟和实验测试了控制器.
- 使用回归和集合树ML模型来预测基于错误值的系统响应.
主要成果:
- 拟议的ILC-DPI控制器在模拟和实时实验中表现出卓越的性能.
- ML模型准确地预测了实际的系统响应,验证了控制器的有效性.
- 控制器将超标量减少了近一半,并改善了结算时间,在SISO过程中实现了29.96%的快速响应,在MIMO过程中达到14.61%.
结论:
- SA-ALO优化的ILC-DPI控制器为工业过程提供了系统稳定性和稳健性的显著改进.
- 机器学习技术为验证先进控制系统性能提供了有效的工具.
- 开发的控制器为复杂的SISO和MIMO工业控制挑战提供了可行的解决方案.
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