The Application Of Fuzzy Neural Network Controller In The Boiler Temperature Control System Based On PLC

According to the nonlinear complex objects that have time-vary and delay characteristic in the industrial process control, moreover, it is difficult to obtain the satisfactory control performance with the conventional control algorithm. In recent years, the intelligent control technology strategy, such as fuzzy control, neural network, genetic algorithms, expert control and so on, has obtained widespread application in the field of automatic control. In the process of research and application, a finer control performance can be made by combining properly one single control strategy with others.Due to programmable logic controller (PLC) has many technological advantages, such as high reliability, anti-interference ability, feature-rich and so on. The PLC’s function of computing and data handling has increased notably, this makes it possible to realize the advanced control algorithms embedded on PLC.In this paper takes boiler coil temperature control system as the study object, firstly,the step response characteristics of the object is analyzed theoretically based on the energy balance. then, The mathematical model of the system was built by using the step response curve method with the test data. In this paper, simulation experiment is performed firstly by using Matlab, then the AB CompactLogix 1769-L32E PLC is chosen as the controller of the system. The method and concept of structural programming are applied in the process of PLC programming. The fuzzy neural network strategy is programmed successfully on PLC in the ladder diagram language. The tempetature closed-loop control system is set up based on experimental equipment, and the ideal control performance of intelligent controller is received. In order to overcome the disturbance of boiler bladder, the cascade control system is adopted, but it still has overshoot when PID control is used in the main and auxiliary controller, so FNNC is used to replace the PID control in the auxiliary controller, thus the system stability and robustness is improved.