A neural network self tuner based on input shapers behavior for anti sway system of gantry cranes

Abstract

The rapid and sway-less conveyance of crane loads has been an essential task and a field of research; considering the broad application of transportation of goods and materials by these systems. In this paper, a neural network self tuner (NNST) controller, which aims to move the load precisely as well as to eliminate its sway, is presented. This controller is capable of being trained flexibly for either increasing the speed of load transportation or decreasing its sway. The developed training algorithm, in this paper, is inspired by a sway elimination method of input shapers. The proposed training method provides the controller with the capability to remove the majority of load sway, at the outset of transportation, and transport it with a slight sway. The results show that the neural network controller with the proposed training method, in the same time, is capable of removing the sway of the load in a better way and be more appropriate for longer distances compared to a proportional-integral-derivative controller with constant gain. Besides, compared to standard input shapers, the NNST controller is more robust to cable length variations as well as length uncertainty by utilizing the feedback. Using a lab scale gantry crane model, the feasibility of empirical implementation is proved and results are verified.