Two-stage Particle Swarm Optimization-based Nonlinear Model Predictive Control Method for Reheating Furnace Process

The steel slab temperature control of reheating furnace process plays an important role in the production of high quality reheated slab. Because of the characteristics of nonlinearity, long time-delay and uncertainty, high-accuracy slab temperature control is a challenging problem. This paper proposes a two-stage particle swarm optimization (PSO)-based nonlinear model predictive control (NMPC) method to solve the problem. In this method support vector machine (SVM) is utilized to construct the nonlinear predictive model based on the real production data. To obtain better predictive model dynamically, PSO optimizes the parameters of SVM for different problems. Then PSO solves the rolling optimization problem in NMPC to obtain the proper control variables. Finally, the production data collected from a real reheating furnace process are utilized to test the proposed method. Numerical experiments are done by computer simulation based on the real production data. The experiment results illustrate that the PSO-based SVM can obtain accurate predictive model. Moreover, the proposed nonlinear model predictive control method can obtain outstanding control accuracy in steel slab temperature control.