Predicted control for strip thickness based on information fusion

A state-space model of the control system in hot continuous rolling was proposed by using a recursive least squares algorithm by linearizing and discretizing the rolling force and thickness control equations. After an optimal information fusion algorithm based on Kalman filtering was presented, an asynchronous information fusion estimation algorithm was built for the complex multi-variable system of hot continuous rolling. This model was applied into the prediction of strip thickness and plasticity coefficient Q in the hot continuous rolling process. At last, the real-time forecast results of the coming strip thickness and plasticity coefficient of strips were synthetically utilized in the thickness control system of hot continuous rolling to improve the quality of final coming strip thickness.