The conventional PID (proportional-integral- derivative) controller is widely applied to industrial automation and process control field because its structure is sample and its robust is well, but it do not work well for nonlinear system, time-delayed linear system and time varying system. This paper provides a new style of PID controller that is based on artificial neural network and evolutionary algorithm according to the conventional one’s mathematical formula. 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. This paper compared the performance of PID controller with ANN based upon Set point change, Effect of load disturbances and Processes with variable dead time. The result shows that ANN outperforms the PID controller
Process control, Neural network (NN), PID controller, Temperature control, System modeling
Cite this paper
Abdulgani Albagul, Hafed Efheij, Beleid Alsharif. (2019) Comparison of Artificial Neural Network Controller and PID Controller in on Line of Real Time Industrial Temperature Process Control. International Journal of Control Systems and Robotics, 4, 27-32
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