An intelligent serial connected hybrid control method for gantry cranes

Abstract

The cranes are usually controlled by a variety of actions including handling, lifting, carrying, then lowering and gripping the load. An intelligent control system could provide the operator with important advantages when performing all these operations. As the load cycle times are reduced, the operations are then performed more efficiently, and hence saving time, costs and increasing the overall productivity. As the control system optimizes the motion of the crane, the crane and its components are subjected to load and related stresses for shorter length of time leading to longer lifespans. Most importantly, the likelihood of human error is reduced and security is increased. Especially the transportation of fragile, sensitive materials or load transfer in dangerous conditions makes the operator control very difficult. The present study demonstrates how to perform a artificial intelligence assisted transport operation under variable conditions with hybrid control methods on a simple pendulum. Zero Vibration (ZV) input shaping (IS) method was implemented to a simple pendulum experiment setup with different heights and variable weights. Neural Network (NN), General Regression NN (GRNN), and Radial Basis Function Network (RBFN) were used with serial as a closed-loop intelligent control methods. The presented results indicate that the proposed approach constributes to the overall performance for elimination of residual vibrations.