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数据驱动的分布式EMPC用于互连系统的经济优化:汉克尔矩阵方法.
Fatemeh Ostovar1, Ali Akbar Safavi1, Leonhard Urbas2
1Department of Power & Control Engineering, Shiraz University, Shiraz, Iran.
ISA transactions
|February 8, 2026
概括
本研究为复杂系统引入了一种新的数据驱动的分布式经济模型预测控制 (EMPC) 方法. 它只使用输入输出数据来提高能源效率和稳定性,优于其他模型预测控制方法.
科学领域:
- 控制系统工程 控制系统工程
- 分布式优化 分布式优化
- 数据驱动建模数据驱动建模
背景情况:
- 分布式系统需要有效的控制策略来优化经济目标.
- 传统的模型预测控制 (MPC) 对分布式系统来说可能是计算密集的.
- 数据驱动的方法为简化控制设计提供了潜力.
研究的目的:
- 提出一种新的非代性,数据驱动的分布式经济模型预测控制 (EMPC) 方案.
- 使子系统能够使用本地输入-输出数据优化经济目标.
- 确保分布式系统中的递归可行性和闭环稳定性.
主要方法:
- 为线性时间不变系统开发了一个非代的,数据驱动的EMPC框架.
- 利用输入-输出数据在子系统内进行本地优化.
- 嵌入的一致性约束来自汉克尔矩阵的邻居相互作用.
- 设计的终端成分使用输入-输出轨迹来保证稳定性.
主要成果:
- 在数据驱动的框架中实现了强大的二元性和消散性,具有一般的供应率.
- 通过理论分析证明了递归可行性和闭环稳定性.
- 通过模拟验证了该方法的能源效率和有效性.
结论:
- 拟议的数据驱动分布式EMPC方案有效地优化分布式系统中的经济目标.
- 与现有的MPC方法相比,该方法在能源效率和稳定性方面具有显著的优势.
- 这种方法为复杂的分布式控制问题提供了可计算的解决方案.
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