基于多模型pH非线性过程的预测控制

Predictive control of pH nonlinear process based on multiple model

对于强非线性系统 ,基于单一模型的传统预测控制系统的瞬态性能一般比较差 ,而克服这一缺点的直观且可行的方法是根据多个不同工作点建立多模型 ,通过在线切换设计自适应预测控制器 ,本文针对典型化工非线性对象 p H中和过程 ,应用多模型处理方法 ,提出了 p H过程的多模型预测控制设计方法 ;根据其工作范围建立了过程的多模型表示 ,然后在此基础上 ,设计了相应的预测控制器 ,通过模糊识别来调度控制器 ,从而获得大范围全局非线性对象的控制量 。

This study showed that for strongly nonlinear systems, the transient behavior of the traditional predictive control based on single model may be very poor in general, and that the predictive control design based on switching between multiple models is an intuitively appealing and practically feasible approach to improve the transient performances. In this work, multiple model presentation of typical pH nonlinear process was developed according to the process's operation range. Then, application of the multiple models dealing method, yielded the multiple model control strategy for pH neutralization process. Based on different equilibrium point states of the nonlinear pH process, the linear model was recognized according to the maximum membership function rule and the corresponding controller was chosen. Simulation result showed that it is efficient over a wide operation range.