大规模工业过程稳态优化控制新方法：自适应双迭代算法

A Self-Adaptive Double Iterative Algorithm for Steady-State Optimizing Control of Large-Scale Industrial Processes

In this paper, a self-adaptive double iterative algorithm(SDIA)for steady-state optimizing control of large-scale industrial processes is presented. The SDIA utilizes the interconnection input and output feedback information of real processes to construct a serices of linear models to adaptively follow-up the real processes locally. The SDIA does not need to set up approximation model a priori, and possesses the global convergence. Simulation results show that the new approach in very efficient.