关键词: 多时间尺度, 分散化预测控制, 协调优化

Abstract: In view of large industrial processes, an alterable horizon predictive control algorithm based on reduced state-space decomposition is proposed. Operated in rolling-horizon optimization form, the size of optimization problem is decreased through the decomposition of interior and exterior states in each rolling horizon, on that basis, the coordinated optimization problem between multi-time-scale sub-systems is skillfully solved by using alterable horizon method. Simulation experiment is carried out using the actual production data from a certain iron works, and comparisons with three existing predictive control algorithms are made. Results show that this method deals extraordinarily well with performance index and real-time computation ability, and has the best overal performance.

Key words: multi-time-scale, decentralized predictive control, coordinated optimization