Improving performance of PID controller using artificial neural network for disturbance rejection of high pressure steam temperature control in industrial boiler

The temperature of high-pressure steam is very important to be controlled in order to perform other processes safely, especially for boiler-turbine system. Typically, PID regulator with fixed parameter is used for that purpose. However this method may usually deteriorate the control performance, particularly if the systems exhibit highly coupled behaviour. This paper will present the integration of intelligent control technique, especially artificial neural network, to challenge some deficiencies of PID regulator in dealing with such problem. The proposed control algorithm consists of a neural network controller, which is implemented parallel to the PID controller. The presented neural network controller involves HP steam temperature and its set-point as input and error control signal as a learning signal to be minimized. The ability of proposed algorithm is tested through step-like load disturbance into boiler plant model. Remarkable results have been obtained during this disturbance test. These results showed better performance to reject the disturbances compare with the controller which involves PID regulator alone.