多模型预测控制在磨矿分级过程中的应用

Multiple model predictive control applied in grinding and classification process

磨矿分级过程的控制目标是将一、二级溢流浓度和细度稳定控制在质量指标区间内.磨矿分级过程是胖系统,完成动态优化目标后控制器仍有剩余自由度,因此需考虑局部稳态经济优化.针对这一目标,提出一种考虑局部稳态经济目标的多模型预测控制方案.首先,建立了基于现场数据库的球磨机和分级机传函矩阵模型;然后考虑局部经济性能,将稳态经济目标以罚函数形式嵌入动态优化目标函数;为消除球磨机换球引起的模型失配的影响,建立了一种基于换球规律的多模型切换策略.仿真结果表明了所提出控制方案的有效性.

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.