Deepening controlling method of underactuated automatic underwater vehicle based on neural network back stepping method

Abstract

A deepening controlling method of an underactuated automatic underwater vehicle based on a neural network back stepping method relates to the technical field of control of the underactuated automatic underwater vehicle. The deepening controlling method includes first collecting pressure information through a pressure sensor, obtaining corresponding depth of the automatic underwater vehicle (AUV) by calculation according to the pressure information, then building a mathematical model of the underactuated automatic underwater vehicle and a robust deepening controller model, building a mathematical model of the underactuated AUV according to ocean current environment and AUV water power parameter, designing the robust deepening controller model by adopting the feedback gained back stepping method, finally obtaining online learning arithmetic based on neural network weight and self-adaptive law of self-adaptive robust controller parameter, conducting online recognition and error estimation on uncertainty existing in the obtained mathematical model, compensating and optimizing final output signals of the controller, and achieving deepening control of the underactuated AUV by adopting the controller.

Description

technical field [0001] The invention relates to the technical field of control of an underactuated autonomous underwater vehicle. Background technique [0002] The exploration and mapping of seabed topography is of great significance to the development of deep sea resources. Underactuated autonomous underwater vehicle AUV (Autonomous Underwater Vehicle) has good maneuverability and endurance, and plays an important role in the surveying and mapping of seabed topography and landforms. Since the actuators of the underactuated autonomous underwater vehicle AUV are usually configured as tail axial thrusters, tail rudders, and tail elevators, there is no direct drive mechanism (such as propellers) in the vertical direction, and only the tail elevators are used in autonomous underwater vehicles. The aircraft AUV has additional forces and moments generated at a certain speed to achieve depth control. The multi-beam side-scan sonar sensor carried by the autonomous underwater vehicle...

AI Technical Summary

Problems solved by technology

The disadvantage of the above methods is that it is assumed that the upper bound of the estimation error or uncertainty is a known constant value, which causes the controller to generate unnecessary control signals with high gain

In order to realize the online estimation of the uncertainty in the model and optimize the output signal of the controller, Li et al. in the literature "A neural network adaptive controller design for free-pitch-angle diving behavior of an autonomous underwater vehicle" (Robotics and Autonomous Systems, 2005 , Vol. 52, No. 2) proposed an adaptive depth control method based on neural networks, which can realize online compensation for bounded disturbances and unmodeled dynamics by using the approximation characteristics of neural networks to nonlinear functions. The traditional backstepping method to design controllers leads to the existence of high-order derivatives of virtual control variables in the iterative process, making the controller form too complex

None of the depth controllers proposed above discusses the depth control effect under different initial pitch angles, and cannot meet the application requirements of multiple working conditions in actual engineering.

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