非线性过程系统的数据驱动控制使用三度自由模型-按需模型预测控制框架
Sarasij Banerjee1, Owais Khan1, Mohamed El Mistiri1
1Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, Arizona 85287, United States.
Industrial & engineering chemistry research
|October 27, 2025
概括
本研究引入了一种模拟需求 (Model-on-Demand,MoD) 方法,与基于卡尔曼波器的三度自由度模型预测控制 (3DoF-KF MPC) 集成,用于增强非线性系统的系统识别和控制.
科学领域:
- 控制工程 控制工程 控制工程
- 系统识别系统识别系统
- 非线性系统是非线性系统.
背景情况:
- 传统模型预测控制 (MPC) 算法通常需要复杂的调整. 系统识别方法在计算上可能很苛刻.
- 现有的方法可能会与非线性和复杂的系统作斗争,限制性能和易用性.
研究的目的:
- 提出一种与基于卡尔曼波器的三度自由度模型预测控制 (3DoF-KF MPC) 框架相集成的新型的需求模型 (Model-on-Demand,简称MOD) 估计方法.
- 为了证明单输入单输出 (SISO) 和多输入多输出 (MIMO) 系统的性能改进和直观调整.
- 为控制复杂的非线性过程系统提供计算效率高和强大的方法.
主要方法:
- 开发了一种混合的MoD估计技术,将本地和全球建模结合起来,以提高计算效率.
- 在3DoF-KF MPC框架中整合了基于MoD的预测模型,用于自适应控制行动.
- 使用流行病SIR模型和非线性MIMO CSTR模型的案例研究验证了该方法.
主要成果:
- 基于MoD的3DoF-KF MPC框架在SISO和MIMO控制方面实现了与传统ARX模型相比显著的性能提升.
- 在控制非线性MIMO连续动坦克反应堆 (CSTR) 模型方面表现出卓越的性能和稳定性.
- 提供了设计有效数据库的指导方针,用于基于国防部的MIMO识别和控制实施.
结论:
- MoD估计和3DoF-KF MPC的协同集成为系统识别和控制提供了显著的技术和实践改进.
- 这种方法为操作复杂的非线性过程系统提供了有效的策略,提高了性能和易用性.
- 基于MOD的3DoF-KF MPC框架代表了用于先进过程控制的计算效率高和强大的解决方案.
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