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Predictive control of the heat exchanger using Local Model Network

The paper deals with the problem of modeling and control of nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behavior of the process. The approach is verified in a real-time control of Multifunction Process Control Teaching System (MPCTS) - the Armfield PCT 40.