An Iterative Learning Control Technique for Point-to-Point Maneuvers Applied on an Overhead Crane

Abstract

An iterative learning control (ILC) strategy is proposed, and implemented on simple pendulum and double pendulum models of an overhead crane undergoing simultaneous traveling and hoisting maneuvers. The approach is based on generating shaped commands using the full nonlinear equations of motion combined with the iterative learning control, to use as acceleration commands to the jib of the crane. These acceleration commands are tuned to eliminate residual oscillations in rest-to-rest maneuvers. The performance of the proposed strategy is tested using an experimental scaled model of an overhead crane with hoisting. The shaped command is derived analytically and validated experimentally. Results obtained showed that the proposed ILC control strategy is capable of eliminating travel and residual oscillations in simple and double pendulum models with hoisting. It is also shown, in all cases, that the proposed approach has a low sensitivity to the initial cable lengths.