多模型预测控制在磨矿分级过程中的应用

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.

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.