Structured Model Order Reduction of Parallel Models in Feedback
Structured Model Order Reduction of Parallel Models in Feedback
复制标题
反馈中并行模型的结构化模型降阶
DOI：
10.1109/tcst.2012.2192735
复制
发表时间：
2013
影响因子：
4.8
通讯作者：
Jirí Rehor
中科院分区：
文献类型：
--
作者：
P. Trnka;C. Sturk;H. Sandberg;V. Havlena;Jirí Rehor
在高级工艺控制（锅炉，涡轮机，化学反应堆等）的工业应用中，经常遇到闭环操作中的平行工作单元。控制策略通常需要平行单位配置的每个配置的低阶模型。这些不同的模型通常是通过应用于平行模型的启发式方法获得的。为了取代这些启发式方法，本文提出了一种基于结构化模型顺序的系统解决方案。考虑了两种方法，第一个方法对稳定的闭环系统具有一般适用性，但没有任何先验误差范围。第二个线性矩阵不等式（LMI）的方法具有明确的误差界限，但不能应用于一般模型。但是，这表明，对于由稳定子系统级联组成的模型和严格正面实际子系统的负面反馈，LMI始终是可行的。这两种方法均在带有多个锅炉的热电器发电厂的实际例子上证明。事实证明，第二种基于LMI的方法始终可以应用于与本文中考虑的锅炉头系统类似结构的一般问题。
Parallel working units in closed-loop operation are frequently encountered in industrial applications of advanced process control (boilers, turbines, chemical reactors, etc.). Control strategies typically require different low-order models for each configuration of parallel units. These different models are usually obtained by heuristics applied to the parallel models. To replace these heuristics, this paper proposes a systematic solution based on structured model order reduction. Two methods are considered, the first has general applicability to stable closed-loop systems, but gives no a priori error bounds; the second linear matrix inequality (LMI)-based method comes with an explicit error bounds, but cannot be applied to general models. However, it is shown that for models composed of cascades of stable subsystems and negative feedbacks of strictly positive real subsystems, the LMIs are always feasible. Both methods are demonstrated on a practical example of a cogeneration power plant with multiple boilers. It is proved that the second LMI-based method can always be applied to general problems with structures similar to the boiler-header systems considered in this paper.