Neural network S-plane control method for autonomous underwater vehicle

An underwater robot and neural network technology, applied in the field of control, can solve problems such as difficult to adapt to complex and changing marine environments, difficult to obtain optimal control parameters, and affect the effect of motion control, etc., to achieve the effect of strong anti-interference ability

Active Publication Date: 2019-06-18
HARBIN ENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention solves the problem that the existing S-plane control method of AUV is difficult to obtain optimal control parameters or difficult to adapt to the complex and changing marine environment, thus affecting the motion control effect

Method used

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  • Neural network S-plane control method for autonomous underwater vehicle
  • Neural network S-plane control method for autonomous underwater vehicle
  • Neural network S-plane control method for autonomous underwater vehicle

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Experimental program
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specific Embodiment approach 1

[0037] Before describing this embodiment, the parameters are described first;

[0038] Controller structure related parameters:

[0039] k 1 ,k 2 are the control parameters of the S surface controller; u is the output control quantity of the S surface control module; y in Control target amount for AUV movement; y m is the predicted value of the AUV state output by the prediction model module; y p is the predicted value of the AUV state output by the feedback correction module; y out is the state quantity actually output by AUV; N is the number of control beats contained in each parameter adjustment beat.

[0040] Related parameters of the S surface control link:

[0041] o s for the control output; is the rate of change of the deviation between the actual state quantity of the AUV and the target quantity; T max The maximum thrust (torque) that can be provided for the autonomous underwater vehicle; T c is the actual output thrust (torque) after inverse normalization;...

specific Embodiment approach 2

[0135] The AUV control model described in this embodiment can have various forms, that is, the control method of the present invention can be applied to various forms of AUV control models. In some embodiments, the AUV control modeling process is as follows:

[0136] The following two right-handed coordinate systems are established: one is the fixed coordinate system E-ξηζ, which is fixed on the earth; the other is the moving coordinate system O-xyz, which moves with the underwater robot [4]. The origin E of the fixed coordinate system E-ξηζ can be selected at any point on the earth, the ξ axis is located on the horizontal plane, and the projection of the main course of the underwater robot on the horizontal plane is taken as the positive direction; the η axis is also located on the horizontal plane, and the Eξ axis is clockwise according to the right-hand rule Rotate 90°; the ζ axis is perpendicular to the ξEη coordinate plane, pointing to the center of the earth is positive....

specific Embodiment approach 3

[0183] In this embodiment, the input-output relationship of each layer in the Elman neural network is determined in the following manner:

[0184] The Elman neural network is used to establish the multi-step recursive prediction model of the controller. The standard Elman neural network structure is generally divided into input layer, hidden layer, structure layer and output layer. The nonlinear state space expression is as follows

[0185]

[0186] In the formula, u(t) is the input of the input layer at time t; y(t) is the output of the input layer at time t; x c (t) is the output of the structural layer at time t; x(t) is the output of the hidden layer at time t; w (1) is the weight between the structural layer and the hidden layer; w (2) is the weight between the input layer and the hidden layer; w (3) is the weight between the hidden layer and the output layer; θ (1) is the hidden layer unit threshold; θ (2) is the threshold of the output layer unit; f( ) and g( ) a...

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Abstract

The invention relates to a control method for an autonomous underwater vehicle, in particular, a neural network S-plane control method for an autonomous underwater vehicle. According to an existing S-plane control method for an autonomous underwater vehicle, it is difficult to obtain optimal control parameters or difficult to adapt to complicated and varied marine environments, and as a result, amovement control effect is affected, while with the control method of the invention adopted, the above problem of the existing S-plane control method can be solved. According to the control method ofthe invention, as for an AUV control model, closed-loop control is performed on the AUV through an S-plane control method; an S-plane control link outputs control quantities in each control beat; andthe control parameters k1 and k2 of the S-plane control link of a controller are realized by a neural network-based prediction model and is determined through a multi-step prediction link, a feedbackcorrection link and a rolling optimization link. The control method of the invention is applicable to the control of autonomous underwater vehicles.

Description

technical field [0001] The invention belongs to the technical field of control, and in particular relates to a control method of an autonomous underwater robot. Background technique [0002] With the improvement of the strategic position of the ocean, the importance of autonomous underwater vehicles (AUV) has become increasingly prominent in recent years. AUV involves computer, control, materials and other disciplines, and integrates many key technologies such as advanced design and manufacturing technology, energy and propulsion technology, underwater navigation technology and underwater communication technology. Among them, motion control technology is an important part of AUV technology. Only when AUV has good control performance can it ensure the smooth completion of tasks in complex marine environments. [0003] As a commonly used AUV motion control method, S-plane control combines the ideas of fuzzy control and PID control, and uses sigmoid surface function to fit the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 孙延超万磊唐文政秦洪德杜雨桐张栋梁李凌宇
Owner HARBIN ENG UNIV
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