A neural network based fuzzy controller for pneumatic circuit

Abstract EN

Pneumatic circuits are widely used in industrial automation, such as drilling, sawing, squeezing, gripping, and spraying.

Furthermore, they are used in motion control of materials and parts handling, packing machines, machine tools, food-processing industry and in robotics.

In this paper, a Neural Network based Fuzzy PI controller is designed and simulated to increase the position accuracy in a pneumatic servo circuit where the pneumatic circuit consists of a proportional directional control valve connected with a pneumatic rod less cylinder.

In this design, a well-trained Neural Network with a simplest structure provides the Fuzzy PI controller with suitable input gains depending on feedback representing changes in position error and changes in external load force.

These gains should keep the positional response within minimum overshoot, minimum steady state error and compensate the effect of applying external load force.

A comparison between this type of controller with a conventional PID type shows that the PID controller failed to keep the cylinder position with minimum steady state error and failed to compensate the effect of applying external load force as compared with the results when using a Neural Network based Fuzzy PI type controller.

This is because of nonlinearities that exist in the pneumatic circuit.

Thus, the position response using Neural Network based Fuzzy PI controller is better with an average of improvement in position accuracy of (11% ) .