Comparison of PID and Artificial Neural Network Controller in on line of Real Time Industrial Temperature Process Control System

Abstract

Due to its simple structure and robustness, the traditional proportional-integral-derivative (PID) controller is commonly used in the field of industrial automation and process control, but it does not function well with nonlinear systems, time-delayed linear systems and time-varying systems. A new type of PID controller based on artificial neural networks and evolutionary algorithms is presented in this paper. An powerful instrument for a highly nonlinear system is the Artificial Neural Network. The interest in the study of the nonlinear system has increased through the implementation of a high-speed computer system,. In complex systems such as robotics and process control systems, the Neuro Control Algorithm is often applied. Systems of process management is also nonlinear and hard to control consistently.. This paper presents a comprehensive analysis in Which is offline trained by a multilayered feed forward back propagation neural network to act as a process control system controller, That is to say, a temperature control device without prior knowledge of its dynamics. Via the implementation of a range of input vectors to the neural network, the inverse dynamics model is developed. Based on these input vectors, the output of the neural network It is being studied by explicitly configuring it to monitor the operation. In this paper, based on set-point adjustment, impact of disturbances in load and variable dead time, compassion between the PID controller and ANN is conducted. The outcome shows that ANN outperforms the controller of the PID.