Multiple model predictive control applied in grinding and classification process

Stably controlling concentration and fineness of first and second overflow in their quality index range are the control objectives of grinding and classification process.Grinding and classification process is a fat system,and the controller still has free degree after its dynamic optimization objectives realized,so local steady-state economic optimization is considered.For this objective,a multiple model predictive control considering local steady-state economic objectives is proposed.Firstly,based on the field database,transfer function matrix models of ball-mill and classifications are built up.By considering local economic performance,steady-state economic objectives are embedded into dynamic objectives as a penalty function.To eliminate the effect of model mismatch,based on the law of ball changing,a multiple model switching strategy is built.The simulation result shows the effectiveness of the proposed control method.