Cascade Control of a Pneumatic Servo Positioning System Using Neural Networks
This paper deals with the nonlinear control of pneumatic servo positioners. It is proposed the use of a neural network technique associated with a nonlinear cascade control strategy, as a means of bypassing strong difficulties common to the practical implementation of model based strategies in pneumatic systems control. Such difficulties are associated to the precise plant identification that, in the case of pneumatic servo positioners, is usually very complex and hard to find. The experimental results of position tracking control presented in the paper allow us to conclude that the neural network technique applied in this work can avoid the need of execution of time expensive experiments usually required to perform precise identification of the plant characteristics used in model based control strategies for pneumatic servo positioners.