Research On AUV Motion Control Method Based On Improved S Plane Control

With the promotion of ocean strategy position,the importance of autonomous underwater vehicle(AUV)is becoming more and more important in recent years.Excellent motion control effect is the basic premise and guarantee for AUV to complete underwater task.Thus it is of great significance to study the motion control method of AUV.In this dissertation,the Orange Shark AUV in a certain project is taken as the research object,and the motion control method is studied,which includes the following aspects:The classical S plane control method for AUV is briefly introduced,and the main problems in practical application of this control method are analyzed.According to the specific situation of the object of study,the six degree-of-freedom motion model is simplified and the control model of AUV is established considering the disturbance of marine environment.Combined with model predictive control and bacterial foraging optimization algorithm,the classical S plane control method is improved,and the predictive S surface control method is proposed.It is proved that the proposed control method can overcome the main defects of classical S plane control and has excellent motion control performance.In order to enhance the practicability of predictive S plane control and its adaptability to field adjustment,the idea of using neural network to establish a multi-step recursive prediction model is put forward.Firstly,the navigation data are collected as sample data.Then,the neural network is trained to identify the dynamic model.Finally,a recursive prediction model based on neural network is established.The parallel structure is set up to take on the calculation of prediction model and online learning,to reduce the computational burden by sliding window and learning judgment,and to design a flexible transition to reduce the output jitter caused by model switching.Thus the on-line learning function is added to predict the S plane control,so that the controller can better adapt to the changes of marine environment.