Intelligent Model Building and GPC-PID Based Temperature Curve Control Strategy for Metallurgical Industry

Laminar cooling process is a large-scale, nonlinear system, so the temperature control of such system is a difficult and complex problem. In this paper, a novel modeling method and a GPC-PID based control strategy for laminar cooling process are proposed to control the global temperature curve to produce high quality steel. First, based on the analysis of the cooling process of laminar flow, a new TS fuzzy model which possesses intelligence and self-learning ability is established to improve the temperature prediction accuracy. Second, the target temperature curve can be divided into several subgoals and each subgoal can be described by a CARIMA type of model. Then, by the decentralized predictive control method, GPC-PID based control strategy is introduced to guarantee the laminar cooling process to achieve subtargets, respectively; in that way the steel plate temperature will drop along the optimal temperature curve. Moreover, by employing the dSPACE control board into the process control system, the matrix process ability is added to the production line without large-scale reconstruction. Finally, the effectiveness and performance of the proposed modeling and control strategy are demonstrated by the industrial data and metallography detection in one steel company.