Adaptive neural network sliding mode control of shipboard container cranes considering actuator backlash

Offshore container crane is a highly under-actuated nonlinear system whereas only two control inputs are employed for driving six system outputs. Controlling such a system is not easy since it faces with many challenges composed of actuator backlash, geometrical nonlinearities, seawater viscoelasticity, cable flexibility, strong wave and wind disturbances, and considerable lack of actuators. This article proposes a robust adaptive system for a ship-mounted container crane with the disadvantages mentioned above. The controller structure is constructed using second-order sliding mode control (SOSMC), and a modeling estimator is designed on the basis of radial basis function network (RBFN). While other adaptive control techniques only estimates system parameters, the adaptive RBFN algorithm approximates almost all the structure of a crane model, including system parameters. Simulations and experiments are conducted to verify the superiority of the proposed control system.