Artificial neural network based inverse model control of a nonlinear process

In process industries the non linear process control is a challenging and difficult task due to its non linear behavior, delays and time variation between inputs and outputs of system. Conical tank system is one such non linear process which is widely used in process industries due of its non linear shape and easy flow of liquid across its cross section area. As conical tank is inherently non linear it becomes difficult to model the linear plant equation for the same. The control of liquid level in conical tank is a complex and complicated task because of its constantly changing cross section area. So, Artificial Neural network (ANN) based controller is designed because of its ability to model non linear systems and its inverses. The Direct Inverse Control (DIC) designed using ANN is mainly dependent on the inverse response of the system which is difficult task to obtain it analytically. In this paper, ANN based DIC is trained by Levenberg Marquardt Back propagation algorithm and helps to obtain optimized response/performance of the system. The simulation results show that direct inverse control realize a good dynamic behaviour of interacting and non interacting conical tank system.