Research On Model Predictive Control For Marine Dynamic Positioning System

With the increase of world population,progress of science and technology and growth of economy,people gradually began to explore and exploit the resources of the deep sea.The application of traditional mooring positioning system is unable to satisfy the practical requirements in the deep sea,which leads to the emergence and rapid development of marine dynamic positioning technology.The mooring of ships are not restricted by water depth when marine dynamic positioning technology is used,so the technology has great advantages.The technology has broad application prospects in the seaborne resupply,submarine rescue,marine salvage and deep-sea oil and gas extraction.But the marine dynamic positioning system is very difficult to control because of several factors.The first one is that the system is a nonlinear,strong coupling,time varying and poor working conditions system,the second one is that the system is often subjected to strong disturbance,and the last factor is that the detection signal is often disturbed.In this dissertation,based on the model predictive control,the control of marine dynamic positioning system is studied.The main work and results are as follows:(1)For the dynamic positioning constrained control of ship under wind,wave and current disturbances,a generalized predictive control(GPC)method with a constraint adjustment strategy is proposed.Based on the analysis of the wind,wave and current disturbances,the feedforward control is realized according to the measurements of the disturbances and the thrust constraints of ship are adjusted according to the measurements of the disturbances.Then the constraint adjustment strategy is introduced into the rolling optimization of GPC.The proposed method is applied to design a dynamic positioning constraint controller for a surface supply vessel,and the effectiveness of the controller is verified by simulation.(2)For the dynamic positioning control of ship under random disturbances,a non-switching analytic model predictive control(NSAMPC)method with a nonlinear continuous filter is proposed.Unscented Kalman-Bucy filtering(UKBF)is used to get the state estimates of the ship motion.According to the nonlinear ship motion model,NSAMPC is applied to design a dynamic positioning controller.The simulation results show that the fluctuation of the filtered signals is obviously reduced,and the control values of the designed controller are continuous and the fluctuation of them is small.(3)A dynamic positioning predictive controller with extended Kalman filtering(EKF)moving horizon estimation filter and a dynamic positioning predictive controller with unscented Kalman filtering(UKF)moving horizon estimation filter are proposed.The UKBF algorithm is heavily dependent on the prior information of the disturbances.In order to reduce the dependence on the prior information,an EKF based moving horizon estimation method and a UKF based moving horizon estimation method are applied to design filters,respectively.These two filters are respectively combined with the NSAMPC control to design dynamic positioning controllers.By MATLAB simulation,these two controllers are respectively compared with the previously proposed marine dynamic positioning predictive controller with UKBF,and the advantages and disadvantages of different filters are pointed out.(4)Considering the influence of time varying parameters of ship on dynamic positioning,a design method of dynamic positioning model predictive controller with a radial basis function(RBF)neural network compensator is proposed.The controller is the combination of RBF neural network compensator and model predictive control.The closed loop control of the marine dynamic positioning system is realized by a NSAMPC controller.The compensation values are generated by a self-tuning RBF neural network compensator.The values are used to reduce the impact of model inaccuracy on the control performance.By MATLAB simulation,the proposed dynamic positioning model predictive controller with an RBF compensator is compared with the dynamic positioning dynamic inversion controller with an RBF compensator.The results show that the overshoot of the designed predictive controller is small and the controller has strong robustness.In this dissertation,according to the characteristics of marine dynamic positioning control system and its working environment,the state estimation methods and model predictive control methods are deeply studied.A variety of controller design methods are proposed,and the effectiveness of the methods are verified by MATLAB simulations.The results of this dissertation have some instructive significance for the research and development of ship motion control system.