Research On Control Methods For Dynamic Positioning System Of Ship With Uncertainty And Time Delay

Dynamic positioning system is widely used in scientific investigation,oil development,core drilling,submarine mining,cable/pipe laying,marine fire fighting and other marine operations as an important weapon for marine energy development.Therefore,the research of dynamic positioning technology of ship,especially the research of dynamic positioning controller has important engineering significance.The problems of uncertainty in ship model,changeable environmental disturbance,time delay,actuator saturation and state constraint make the design of dynamic positioning control system more difficult.In this paper,several control problems of dynamic positioning system are studied in depth to provide theoretical guidance for improving the capability of ship.The main works of this paper are as follows:In order to study the uncertainties caused by external environment changes and internal model parameters in dynamic positioning system,two control laws are proposed.Firstly,the fuzzy system is constructed to effectively approximate the uncertainties,and then the adaptive law is applied to update the parameters in the fuzzy system.According to this,an adaptive fuzzy backstepping control law is proposed.The other one is the conventional backstepping control law.Simulation results show that the adaptive fuzzy backstepping control law is able to improve the performance of system and increase the accuracy of ship.There are not only uncertain problems,but also the saturation constraint caused by the physical constraints of the actuator in dynamic positioning system.Aiming at the problems of the uncertainty and saturation constraints in dynamic positioning system,two control methods are proposed。Firstly,using RBF neural network compensator and estimator to compensate saturation constraints and estimate the uncertainties respectively,then the adaptive law is designed to update the parameters in the RBF neural network,combining the adaptive RBF neural network with the sliding mode backstepping technique,an adaptive neural network sliding mode backstepping control method is proposed.The other one is the sliding mode backstepping control.Simulation results show that the adaptive sliding mode backstepping control law shortens the response time of system and achieves better control results.In the dynamic positioning system,there are not only uncertainties and saturation constraints,but also the problem of state constraints due to the requirement of engineering operation.Aiming at the problems of uncertainties,saturation constraints and state constraints in the dynamic positioning system,two control strategies are proposed.One strategy is the adaptive fuzzy backstepping control strategy based on the barrier Lyapunov function.Selecting the appropriate barrer Lyapunov function to constrain the errors of position and the heading,the constraint on the ship’s position and heading is achieved,the adaptive fuzzy system is used to approximate the uncertainties,and combining with the barrer Lyapunov function and the adaptive fuzzy system,an adaptive fuzzy backstepping control law based on barrier Lyapunov function is designed.The other one is the backsteping control strategy based on barrier Lyapunov function.The simulation results show that the adaptive fuzzy backstepping control law has good robustness and good dynamic quality.Finally,the problem of time delay caused by the physical factors of the actuator in dynamic positioning system is researched emphatically.Combining this method with adaptive fyzzy system,using Lyapunov-Krasovskii function to compensate the time-delay term,and adopting adaptive fuzzy system to approximate the uncertain nonlinear term,according to this,an adaptive backstepping control law based on Lyapunov-Krasovskii function is proposed.Then,combing this method with RBF neural network,utilizing the Lyapunov-Krasovskii function to offset the influence of time delay,adopting adaptive RBF neural network to approaching uncertainties,according to this,an adaptive sliding mode backstepping control law based on Lyapunov-Krasovskii function is proposed.In addition,selecting the proper barrier Lyapunov function to constrain the position and heading,and using Lyapunov-Krasovskii function to compensate time delay of system,according to this,an adaptive backstepping control law based on the barrier Lyapunov function and Lyapunov-Krasovskii function is proposed.Simulation results verify the effectiveness of proposed control strategies.In this paper,aiming at the problem of uncertainty in dynamic positioning system,an adaptive fuzzy backstepping control law is designed.Aiming at the problem of saturation constraint in dynamic positioning system,adaptive sliding mode backstepping control with neural network is proposed.Aiming at the problem of state constraint in dynamic positioning system,an adaptive fuzzy backstepping control strategy based on barrier Lyapunov function is proposed.Aiming at the problem of time-delay in dynamic positioning system,a time delay compensation method based on Lyapunov-Krasovskii function is proposed.Simulation results show that the adaptive fuzzy control effectively approximates the uncertainties,the adaptive sliding mode backstepping control reduces the influence of saturation constraints,the adaptive fuzzy backstepping control based on the barrier Lyapunov function achieves the state constraint and the time delay compensation method offsets the effect of the time delay,respectively.