Many industrial applications use pneumatic cylinders to position loads using a rectilinear motion. Classical industrial control techniques allow pneumatic cylinders to position loads to a high degree of accuracy. However, these techniques do not allow for trajectory tracking control because they cannot compensate for the nonlinear nature of the compressed air flow and of the internal friction present in the cylinders. Multilayer neural networks (MNN) can be used to compensate for the nonlinear nature of these dynamic systems. For this study, a MNN was designed to be an inverse model of the cylinder and was used in conjunction with a PID feedback controller for the cylinder motion. An off-line adaptive MNN provides initial training to the controller resulting in the ability to track the desired trajectory. Once the controller has been designed, an online adaptive MNN is used for continued learning as the system dynamics change over time.
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