Control of pH neutralization plant using model predictive control and local model network

Abstract

The paper addresses the application of Local Model Networks (LMN) in the field of identification and control of nonlinear systems. LMN are networks which are composed of locally accurate models, where output is interpolated by smooth locally active validity functions. This divide-and-conquer strategy is a general way of coping with complex systems. The architecture of LMN benefits from being able to incorporate a priori knowledge and conventional system identification methodology. The LMN structure also gives transparent and simple representation of the nonlinear system. In an initial off-line identification phase the structure and parameters of local model have to be identified. The obtained global model is then used for prediction of future outputs in Model Predictive Control (MPC) of the pH neutralization plant.