复杂非线性系统的子系统分解方法

利用社区发现算法研究了一种复杂非线性化工系统的子系统分解方法，并进行了分布式模型预测控制设计。引入信息图论的节点表示系统的状态、输入和输出变量，构建非线性过程系统的加权有向图，节点通过加权边连接，加权反映了节点间连接的强度，因而能够同时反映系统内部的连通性和连接强度。利用社区结构发现算法将所有变量分成子系统的群组，使得每个组内的关联比不同组间的相互作用强，从而得到复杂化工过程系统的子系统分解。针对连续搅拌反应釜过程，实施子系统分解，并设计分布式模型预测控制算法，结果表明，所提出的子系统分解方法更能考虑子系统之间的连接权重，得到更有利于分布式模型预测控制的子系统划分，提升系统控制的性能。

Subsystem decomposition of complex nonlinear systems

The community detection algorithm is used to study a subsystem decomposition method of a complex nonlinear chemical system, and a distributed model predictive control design is carried out. The nodes of information graph were used to represent the state variables, input variables and output variables of nonlinear system. The weighted directed graph of nonlinear process system was then constructed. The nodes were connected by weighted edges and the weight reflects the strength of the connection between nodes. Thus, the weighted directed graph can better reflect the internal connectivity and connection strength of the system. The community structure detection algorithm was used to divide all variables into the combination of subsystems to get the subsystem decomposition of complex nonlinear system. The correlation within each group was much stronger than the interaction between different groups. For the process of continuous stirred tank reactor, the subsystem decomposition was implemented and the distributed model predictive control algorithm was designed. The results show that the proposed subsystem decomposition method can take the connection weight of subsystems into account, which is more conducive to improve the performance of chemical process system with distributed model predictive control.