Multiple model modeling and predictive control of the pH neutralization process

Abstract

The requirement for improved efficiency and safety induce the need for sophisticated control systems. Model predictive control represents such control method which makes explicit use of a model of the process to obtain the control signal. The performance of control algorithm depends on the quality of the derived model. A possible approach is to decompose the nonlinear dynamics into multiple linear models and switch or interpolate them based on the current operating conditions. Multiple models structure for modeling and control allow the transfer of many methods from the linear control theory to the nonlinear systems. The process operations are partitioned into several operating regions and within each region, a local linear model is developed to approximate the process. To save on computational load, a linear model is obtained by interpolating these linear models at each sample point and then obtained model is used in a Generalized Predictive Control (GPC) framework. The manipulated variable adjustments are computed through optimization at each sampling interval. The proposed identification and control method is illustrated by the simulation study on a nonlinear process.