• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.105-111
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• 2006

This paper describes a Neural Network based PID control scheme for pneumatic NC axes. Pneumatic systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. A self-excited oscillation method is applied to derive the dynamic design parameters of linear model. The gains of PID controller are determined using a self tuning scheme. The experiments of a trajectory tracking control using the proposed control scheme are performed and a significant reduction in tracking error is achieved by comparing with those of a PID control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.603-609
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• 2004

This paper deals with the issue of Neural Network-based control for a rodless pneumatic cylinder system which is utilized for a pneumatic XY-plotter. In order to identify the system design parameters, the open loop response of a pneumatic rodless cylinder controlled by a pneumatic servovalve is investigated by applying a self-excited oscillation method. Based on the system design parameters, the PD feedback compensator is designed and then Neural Network is incorporated with it. The experiment of a trajectory tracking control using a PD-NN has been performed and proved its excellent performance by comparing with that of a PD feedback compensator.