基于免疫算法结构分解的分布式预测控制算法

Distributed Predictive Control Algorithm Based on IA Processing Structure Decomposition

针对分解的分布式模型预测控制(DMPC)系统中子系统间的耦合影响所导致的通信负担重的问题,提出了一种新的基于免疫算法(IA)系统结构分解的DMPC算法。首先,采用IA算法对DMPC系统结构分解中遇到的输入分组(ICD)和输入输出配对(IOPD)问题进行求解,最大程度地减小了系统间输入输出耦合影响;然后,采用DMPC算法对分解后的系统进行分布式控制,有效地减小了子系统间的耦合,并降低了系统的通信负载问题;最后,用重油分馏化工过程进行了仿真实验,并通过与集中式模型预测控制(CMPC)的仿真结果作对比来验证了算法的有效性。

A novel distributed model predictive control(DMPC)approach based on immune algorithm(IA)to find out the optimal system decomposition structure is proposed.The IA is used to solve decomposition problems for input clustering decomposition(ICD)and input‐output pairing decomposition(IOPD),which can minimize the impact of input‐output coupling between systems,and then DMPC algorithm is used to control the decomposed system.This approach effectively reduces the coupling between subsystems,and reduces the communication load of the system.Finally,a heavy oil fractionation chemical process is simulated and compared with the centralized MPC simulation results to verify the effectiveness of the algorithm.