Research On Active Anti-Swing Control Strategy Of Ship-Mounted Crane System With Multiple Disturbances

As a kind of ship deck equipment,the major task for ship-mounted cranes is to launch / recover and transfer payload.Due to the influence of multiple internal and external disturbances,in practical engineering,the passive retraction mode presents many shortcomings,such as slow working efficiency,poor swing reduction effect and low safety factor.Therefore,the active anti-swing control for ship-mounted cranes has become an interest in this field.Hence,to eliminate the negative benefits brought by multiple disturbances,the refinement modeling,controller design and control strategy of the ship-mounted crane system are studied.It is of great significance to achieve effective anti-swing control under different working conditions.The main contents are as follows.Based on the mechanism structure and physical characteristics of ship-mounted crane,the mathematical model is abstracted.The dynamic model of the system is built with the Euler-Lagrange equation,and the energy equation of the system is listed.The nonlinear dynamic model that considered the time-varying rope length is established without any simplification.Moreover,the rolling motion is regarded as main external disturbance,and the motion law of state variables is analyzed under different conditions.To solve the problem of continuous disturbance and residual swing,the method of LQR controller based on Improved Grey Wolf Optimization(IGWO)is proposed.To overcome the shortcomings of local optimization and insufficient population richness,nonlinear function and refraction principle strategy are introduced to improve the iterative performance of GWO.On this basis,the approximation ability of RBF neural network is used to track and compensate the residual swing of payload.The swing angle finally oscillates in a small range.The control design with internal disturbance and unknown payload mass parameters is studied,and the anti-swing method based on adaptive sliding mode control(ASMC)is determined.On this basis,a nonlinear extended state observer is introduced to estimate the internal unmodeled disturbance.Without knowing the payload mass parameter in advance,the adaptive mechanism is built to ensure online convergence to the actual value.The dual control objective of payload swing reduction and state variables positioning is realized under complex disturbances,which further improves the robustness of the controller.To verify the feasibility of the above scheme,the simulation platform is built to carry out simulation under multiple sea states and different conditions.The simulation results show that the anti-swing efficiency of IGWO-LQR is up to more than85% under 5 sea state,and the response speed is improved by about 5-10 s compared with the traditional LQR method.After being compensated by RBF neural network,the residual swing oscillates within ± 0.3°.In addition,the anti-swing effect of ASMC is increasing by 64.24%,61.35% compared with traditional SMC and LQR methods under unknown payload mass parameter and internal unmodeled disturbance.The good control performance and robustness of the controller are verified by comparative simulations.