Model Free Adaptive Predictive Control of Multivariate Molten Iron Quality in Blast Furnace Ironmaking

Abstract

The complicated physical and chemical reactions in the internal complex operating environment of smelting process and the Blast Furnace (BF) have led to the difficulty of establishing the model-based controllers. Therefore, model free control methods should be used that meet the actual needs of the engineering systems. However, due to the sparse characteristic of the molten iron quality (MIQ) data in BF ironmaking, traditional model free adaptive control based MIQ control methods cannot control such a complex industrial system with strong nonlinear time-varying dynamics. In this paper, an extended and compact form dynamic linearization (CFDL) based model free adaptive predictive control (MFAPC) scheme (CFDL-MFAPC) is proposed for multivariate MIQ indices by generalizing the CFDL-MFAPC method only for SISO system to MIMO system. Two groups of verification experiments are performed to evaluate the performance of the controller. The results show that the proposed method has not only a better control performance than the compared traditional CFDL based model free adaptive control method and data-driven model predictive control (MPC) method, but also can guarantee the bounded-input bounded-output stability of the MIQ output of the control system for BF ironmaking process.