Predictive control of a continuous stirred tank reactor based on neuro-fuzzy model of the process

In this paper, a predictive control strategy based on neuro-fuzzy (NF) model of the plant is applied to continuous stirred tank reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neuro-fuzzy predictive control, can be a better match to govern the system dynamics. In the article, the neuro-fuzzy model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. An optimizer algorithm based on evolutionary programming technique (EP) uses the identifier-predicted outputs and determines input sequence in a time window. The present optimized input is applied to the plant, and the prediction time window shifts for another phase of plant output and input estimation. Afterwards, the control aims, the steps in the design of the control system, and some simulation results are discussed. Using the proposed neuro-fuzzy predictive controller, the performance of PH tracking problem in a CSTR process is investigated. Obtained results demonstrate the effectiveness and superiority of the proposed approach.