Simulation Research On Hydrodynamics And Hover Control Of Open Rack Remotely Operated Vehicle

Since the 21 st century,with the development of science and technology,the rapid increase of population and the consumption of resources,countries all over the world have regarded the ocean as the strategic space and resource treasure house for the sustainable development of human society.Remotely operated vehicles(ROVs)are advanced marine engineering equipment widely operated in diving operations.Compared with manned submersible,remotely operated vehicles can avoid high-risk operations and diving depth restrictions,and have many advantages such as strong operation adaptability,unlimited operation time and flexible function expansion.In order to meet the requirements of remotely operated vehicles in the complex underwater environment,ROV needs to have superior control performance.In view of the above problems,this thesis studied the hover control of the open-frame remotely operated vehicle,analyzed the hydrodynamic performance of ROV by numerical simulation method,built the motion control simulation model and carried out the analysis.The specific work contents and achievements are as follows:(1)Based on the transfer function of ROV hovering motion,computational fluid dynamics method and overlapping grid technique were used to simulate the hydrodynamic performance of ROV in pure heave and yaw motion.The acceleration coefficients and velocity coefficients of ROV in pure heave and yaw motion were obtained.According to the hydrodynamic coefficient of numerical simulation,the transfer function between the depth of ROV moving at fixed depth and the thrust of the ducted propeller,and the transfer function between the turning angle of ROV moving at fixed head and the pushing moment of the ducted propeller were obtained.(2)Combined with the neural network method,the calculation conditions of hydrodynamic coefficients of pure heave and yaw of ROV obtained by numerical simulation were less optimized.The motion amplitude,frequency and longitudinal inflow of ROV under pure heave and yaw of ROV were taken as influencing factors.BP neural network,K-fold cross validation optimization BP neural network and genetic algorithm optimization BP neural network were used to establish the prediction model,and the neural network optimization ROV hydrodynamic coefficients under a large number of operating conditions were obtained.(3)Using the motion reference coordinate system method,the forward and astern hydrodynamic performance of the ducted propeller under different progress coefficients were solved,and the relationship among thrust,torque and speed of the ducted propeller as well as changes in the flow field at the tail were analyzed.Based on the volumetric force propeller method,the actual thrust of the ducted propeller is simulated,and the coupling effect between the ducted propeller and ROV is established.The kinematic response performance of the ROV under the action of the ducted propeller is numerically simulated.The research results show that the ROV can complete the space heading movement with the coordination of six ducted propellers and has good maneuvering performance.(4)According to the hydrodynamic coefficient of ROV numerical simulation and the hydrodynamic coefficient optimization of BP neural network,the motion control model is established respectively,and the transfer function of ROV depth control and alignment to control is obtained.The simulation was carried out in MATLAB/Simulink toolbox to realize the control of ROV depth and heading angle.The numerical simulation hydrodynamic coefficient control model and BP neural network optimization hydrodynamic coefficient control model under the same PID parameters are compared and analyzed.Proposed RBF neural network PID controller,BP neural network optimization of the hydrodynamic coefficient control model,compared with the traditional PID controller control effect simulation,to verify the accuracy of the RBF neural network PID controller and antiinterference performance.The results show that the numerical simulation hydrodynamic coefficient control model and BP neural network optimization hydrodynamic coefficient control model have similar dynamic characteristics for ROV depth control and steady heading motion control based on the same PID parameters.The effect of RBF neural network controller is better than that of traditional PID controller.