Distributed Model Predictive Control Algorithm Based on PSO-IGA Processing System Structure Decomposition

A novel distributed model predictive control (DMPC) approach based on genetic immune algorithm through particle swarm optimization (PSO-IGA) to find out the optimal system decomposition structure is proposed. The PSO-IGA 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 case study of a heavy oil fractionation chemical process is presented to demonstrate the effectiveness of the proposed approach.