A learning strategy based partial feedback linearization control method for an offshore boom crane

This paper proposes an efficient nonlinear controller for an offshore boom crane, which is a combination of a learning strategy and a partial feedback linearization method. An offshore boom crane is a kind of typical underactuated system which has less number of actuators than the degrees of freedom (DOFs), and it is also a sophisticatedly nonlinear system with strong-coupling characteristics, therefore, controller design for this kind of systems becomes an extraordinary challenging task. Furthermore, different from an overhead crane fixed on land, an offshore boom crane fixed on a vessel suffers from some peculiar disturbances of the attached ship´s multi-dimensional movement induced by waves and ocean currents, which implies that the motion of the ship can cause a tremendous effect on this system. Considering the periodic property of sea waves, in this paper, we propose a control strategy containing a learning law to deal with the aforementioned practical problems. The developed control system is robust for unknown interferences, and the stability of the designed closed-loop system is guaranteed in the Lyapunov sense. Simulation results are presented to demonstrate the efficiency of the proposed control method.