Abstract
In this paper, a neural network (NN) based internal model control (IMC) -PID controller is proposed for a non–linear process. The controller structure has been outlined and its performance is demonstrated on a conical tank process. The control of liquid level in a conical tank is nonlinear due to the variation in the area of cross section of the tank system with its change in shape. The model of the process is identified using standard step response based system identification technique and it is approximated to be first order plus dead time (FOPDT) model. From the results it is observed that fuzzy controller shows much better integral absolute error (IAE) and integral squared error (ISE) performance criteria than the conventional controller.
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