Research On Attitude Control Of AUV Recovery

AUV(Autonomous Underwater Vehicle),as one of the important tools for human to explore the underwater world,plays an irreplaceable role in both civil and military fields.In order to allow the AUV to have more cruising time underwater and get a faster interaction speed,the research on AUV underwater recovery docking project is particularly important.Among them,the attitude control of AUV,as the basis of docking and recycling research,has now become one of the research hotspots in this technical field.This article is mainly based on the autonomous docking project of underwater vehicles(AUV).To solve the problem that the attitude of AUV needs to have higher accuracy and better stability in the docking process,an attitude controller combining RBF neural network and improved fractional order integral sliding is designed.The main research arrangements are as follows:1.In view of the AUV underwater docking environment,this chapter establishes the kinematic model and dynamic model of the "T-SEA I" underwater robot.At the same time,the corresponding specific parameters are substituted to obtain the mathematical simulation model,which provides the basis for the design of the AUV attitude controller.2.According to the PID controllers commonly used in actual engineering,the corresponding controllers are designed and its practicability is analyzed through simulation.At the same time,we can know from the theory that the PID controller has a certain error in the control of the nonlinear system with time-varying interference.In order to improve the robustness and adaptive ability of the AUV attitude controller,this chapter considers using sliding mode control to design AUV attitude controller.At the same time,in order to reduce the influence of the underwater environment disturbance and chattering on the stable operation of the AUV,a fuzzy sliding mode controller was proposed.Among them,fuzzy control is mainly used to dynamically adjust the approach law coefficient in sliding mode control.But fuzzy sliding mode control has the problems of small errors caused by disturbances.It also needs long adjustment time,which should be improved with better control algorithms.3.For the AUV attitude control in the process of underwater docking recovery,it needs to have higher accuracy and better stability.We should design an attitude controller with better control effect.This chapter intends to use an attitude controller combining RBF neural network and improved fractional order integral sliding mode.The RBF neural network is used to approximate the uncertain interference and the perturbation terms of the AUV model in the docking process.Fractional integration can gradually forget the nature of the past,and the fractional sliding mode surface of the control system is designed to effectively reduce the overshoot phenomenon.At the same time,in order to make the system state converge to the fractional sliding mode surface quickly without chattering,a new type of fast-convergence two-power reaching law is proposed.Finally,the superiority of the designed controller is verified through simulation.4.Aiming at the practical feasibility of attitude control in docking recovery,an experimental system with perfect docking recovery function was completed.This chapter introduces the design of the overall software and hardware of the experimental system.At the same time,the designed experiment system conducted a lake test.Through fixed depth control experiment,underwater square autonomous mission tracking experiment and water surface recovery experiment,it can be seen that the attitude control has a certain degree of stability and accuracy.The attitude control system can meet the needs of the recovery docking experiment project.