Design of artificial neural network controller for continually stirred tank heater

Process control systems use controllers with adjustable settings which control and coordinate the process operations over a wide range of operating conditions. The classical control theory is the basis for the development of simple automatic control systems using P, PI, and PID controllers. In conventional control, linear approximation of the plant properties itself has some disadvantages, like, linear approximation becomes computationally impractical, if the plant is very complex and highly dynamic and also there are difficulties in adapting itself to changing plant parameters. To overcome the limitations of the conventional and other controllers, the work has been started towards the development of artificial neural network (ANN) based intelligent controllers in the recent times. In this paper neuro-control techniques are implemented to a process system-continually stirred tank heater (CSTH). The artificial neural networks can be feasibly implemented for real-time control implementations and its performance is better than conventional control methods.