Comparison Of An Artificial Neural Network Controller And PID Controller In On Line Of Real Time Industrial Temperature Process Control System
الكلمات المفتاحية:
process control Neural network (NN)، PID controller، Temperature control، System modellingالملخص
The conventional PID (proportional-integral- derivative) controller is widely applied in industrial automation and process control because of its simple structure and robustness. However, it does not work well for nonlinear system, time-delayed linear system and time varying system. This paper provides a new approach of PID controller that is based on an artificial neural network and an evolutionary algorithm. An Artificial Neural Network is an effective tool for a highly nonlinear system. Neuro control algorithms are mostly implemented for the application of robotic systems and 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 manner. 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 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. The performance the ANN is compared to the PID controller based on set point change, effect of load disturbances and processes with variable dead time. The result shows that ANN outperforms the PID controller.
المراجع
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الحقوق الفكرية (c) 2020 Journal of Electrical and Electronic Engineering and Information Technology
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