Details

Autor(en) / Beteiligte

• Tuan, Le Anh
• Cuong, Hoang Manh
• Trieu, Pham Van
• Nho, Luong Cong
• Thuan, Vu Duc
• Anh, Le Viet

Titel

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

Ist Teil von

• Mechanical systems and signal processing, 2018-11, Vol.112, p.233-250

Ort / Verlag

Berlin: Elsevier Ltd

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Propose a robust adaptive control system for a highly under-actuated shipboard crane whereas only two actuators are unitized for controlling six outputs.•The control system works well even the heavy-duty case wherein lifting a container and moving a trolley are simultaneously activated, and the crane undergoes both sea wind and wave excitation.•Not only simulation but also experiment is conducted to clarify the effectiveness of proposed control system. 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.