Neural network integrated sliding mode control of floating container cranes

We constitute an adaptive robust controller for floating container cranes based on the second-order sliding mode control (SOSMC) integrating neural network. SOMSC is utilized for constructing the frame of a controller while a radial basis function network (RBFN) is integrated to estimate the system modeling. The disturbances composed of viscoelasticity of sea water, elasticity of suspended wire rope, and sea-excited motions of ship are fully considered. The control system tends to two important behaviors: (i) robustness with parameter uncertainties and disturbances; (ii) adaption with both unknown system parameters and no information of system modeling. The simulation results reveal that the proposed control system work well and stabilize all the system responses.