Constrained Crane Load Transferring and Lowering under Uncalm Sea Conditions Using Adaptive Iterative Learning Control

A novel adaptive iterative learning control (AILC) algorithm is proposed in this work for a class of container crane systems operating under uncalm sea conditions, for the crane trolley and cable to track non-repetitive reference trajectories over the iteration domain. In particular, the desired trolley position can be iteration dependent, and the desired cable length of the crane system can be both iteration and time varying. The trolley position, cable length, and the swing angle of the cable are subject to user-defined constraints during the operation. The path planning algorithm presented in this work relaxes the traditional assumptions regarding system initial conditions in the ILC literature. We show that the control objective can be achieved asymptotically over the iteration domain, beyond a user-defined finite time interval in each iteration of operation. The constraint requirements are satisfied during the operation. In the end a simulation example further demonstrates the efficacy of the proposed algorithm.