Neuro-fuzzy controller design for industrial process controls

Abstract

In this paper, an industrial controller is designed with the neuro-fuzzy model based on Sugeno-type fuzzy inference. The designed controller is a nonlinear system, which uses the relation between input and output data. The fuzzy system is employed as the controller, which can be tuned itself by the neural network mechanism based on a gradient descent technique. The controller is implemented with M-file and graphic user interface (GUI) of Matlab program. The program uses MPIBM3 interface card to connect with the industrial processes. The proposed controller provides the online tunable mode to adjust the fuzzy rule bases with real time. In the experimentation, the proposed method is tested by varying of the process parameters, set points and load disturbance. Two processes, which consist of the level and temperature controls, are used to evaluate the efficiency of our controller. The results of the both processes are compared with two PID systems that are 3G25A-PIDO1-E and E5AK of OMRON. From the comparison results, our controller performance can be archived in the case of more robustness than the two PID systems.