A feedforward MNN controller for pneumatic cylinder trajectory tracking control

Pneumatic cylinders are used in many industrial applications to position loads using a rectilinear motion. Pneumatic cylinders are limited to a narrow range of applications because their nonlinear dynamics are difficult to control with linear controllers. Conventional linear control techniques can not compensate for both the internal friction and the compressible air flow present in the cylinders. Multilayer neural networks (MNNs) are nonlinear mappings which can be used to compensate for the nonlinear nature of these dynamic systems. A model of a pneumatic cylinder was developed to provide training data for the feedforward MNN controller. The MNN was designed to cancel the cylinder dynamics and was used in conjunction with a proportional feedback controller to control the cylinder motion. The MNN was trained over a range of constant velocity cylinder trajectories, and the resultant controller allows the model to follow a constant velocity trajectory within the trained state space