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Artificial Neural Network is an effective tool for highly nonlinear system. With the advent of high-speed computer system, there is more increased interest in the study of nonlinear system. Neuro control algorithm is mostly implemented for the application to robotic systems and also some development has occurred in process control systems. Process Control systems are often nonlinear and difficult to control accurately. Their dynamic models are more difficult to derive than those used in aerospace or robotic control, and they tend to change in an unpredictable way. This paper gives an example where a multilayered feed forward back propagation neural network is trained offline to perform as a controller for a temperature control system with no a priori knowledge regarding its dynamics. The inverse dynamics model is developed by applying a variety of input vectors to the neural network. The performance of neural network based on these input vectors is observed by configuring it directly to control the process. In this paper, we have compared the performance of PID controller with ANN [1] based upon Set point change, Effect of load disturbances and Processes with variable dead time. The result shows that ANN outperforms the PID controller.

Comparison of ANN Controller and PID Controller for Industrial Water Bath Temperature Control System using MATLAB Environment