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...

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Bibliographic Details
Published in2017 11th Asian Control Conference (ASCC) pp. 847 - 852
Main Authors Van Trieu, Pham, Luu, Do Duc, Cuong, Hoang Manh, Tuan, Le Anh
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Adaptation models
adaptive robust control
Containers
Cranes
floating container cranes
Marine vehicles
Mathematical model
Nonlinear dynamical systems
radial basis function network
Robustness
sliding mode control
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Summary: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.
DOI:10.1109/ASCC.2017.8287281