Author
Abstract
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, foodprocessing
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 rodless 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 %).
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