Development of the automatic supervisory control system based on fuzzy inference

Abstract

Abstract The development of the supervisory control system designed to ensure the automatic stabilization of the petroleum processing parameters in a multi-flow furnace is considered. The structure of the subsystem that generates the setpoints of the local flow controllers in order to optimize technological processes is proposed. The supervisory subsystem is designed to minimize two criteria of the controlled values. The genetic algorithm is used to tune the parameters of the fuzzy inference based supervisory subsystem. The description of the system implementation based on intelligent controllers is suggested as well as the results of system simulation in MATLAB.