基于分布式状态空间模型预测控制的多级流程雁阵阵形调整

流程雁阵（process goose queue，PGQ）是一种新颖的流程工业系统分解协调优化方法．针对在过程干扰下多级流程雁阵的阵形调整问题，采用递阶求解的分布式模型预测控制算法，利用输入输出数据的Hankel矩阵，通过子空间辨识方法直接获取流程雁阵的脉冲响应序列，建立了预测控制算法．将此算法应用于一个氧化铝碳酸化分解过程，仿真验证了方法的有效性.

Multi-level Process Goose Queue Adjustment Based on Distributed State-space Model Predictive Control

PGQ (process goose queue) is a novel approach to deal with the decomposition and coordination optimization of complex industrial processes. For the PGQ formation adjustment problem in the absence of process disturbance, we propose a predictive control algorithm based on a hierarchically distributed model. This is based on Hankel matrices of input and output data, and uses subspace identification to directly obtain impulse response sequences of the system for model predictive controller design. The alumina carbonation decomposition process is used as a case study to demonstrate the effectiveness of the proposed approach.