Distributed Model Predictive Control Based on Cascade Processes

Distributed model predictive control(DMPC) is a useful control theme which is usually used to control large scale systems with multiple inputs and multiple outputs.Every agent communicates with the other in order to control the whole system.The algorithms for distributed model predictive control can be divided into two categories,one is iterative and the other is non iterative.The iterative ones can reach the same performance as the centralized model predictive control(CMPC) when they converge,however,because of the large number of iterations,the communication burden is heavy;while the non iterative ones do not need iteration,the performance is not as good as centralized algorithms.This article proposes a non iterative algorithm of distributed model predictive control based on cascade processes.Our algorithm can save computational burden for cascade processes.Finally,the alumina continuous carbonation decomposition process(ACCDP) is used to prove the effectiveness for the algorithm.We also analyse the performance and proof the stability of the algorithm.