复杂系统的控制与一般化嵌入和经验动态建模的复杂系统的控制.
Joseph Park1,2, George Sugihara1, Gerald Pao2
1Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United States of America.
PloS one
|August 1, 2024
概括
本研究引入了一种新的数据驱动方法,用于控制复杂的非线性系统,使用通用状态空间嵌入和模型预测控制. 这种方法提供了可解释的控制,而不需要复杂的数学模型或广泛的培训.
科学领域:
- 控制工程 控制工程 控制工程
- 复杂系统科学 复杂系统科学
- 数据驱动建模数据驱动建模
背景情况:
- 有效的系统控制依赖于理解过程动态,通常通过数学或数据驱动模型实现.
- 复杂的系统给传统建模带来了挑战,导致抽象的数据驱动方法缺乏明确的动态连接或需要广泛的培训.
- 现有的方法难以应对高级控制应用所需的复杂性和可解释性.
研究的目的:
- 提出一种使用一般化状态空间嵌入的模型预测控制 (MPC) 的新方法.
- 为控制非线性,复杂系统提供数据驱动和可解释的方法.
- 为了证明这种方法在各种控制和动态系统中的适用性.
主要方法:
- 开发了一个通用的状态空间嵌入技术来表示复杂的系统动态.
- 将这种嵌入与模型预测控制 (MPC) 集成为系统指导.
- 验证了大规模基于代理的模型 (1200个代理) 产生的非线性动态的方法.
主要成果:
- 从一般化状态空间嵌入成功演示了模型预测控制.
- 该方法为复杂的非线性系统提供了数据驱动和可解释的控制策略.
- 这种方法甚至对复杂的基于代理的模型产生的动态有效.
结论:
- 一般化状态空间嵌入为复杂系统的数据驱动控制提供了一个强大的工具.
- 这种方法克服了传统数学建模和抽象数据驱动技术的局限性.
- 该方法广泛适用于在状态空间中可表示的任何控制器和动态系统.
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