Adaptive Depth Control for Autonomous Underwater Vehicles Based on Feedforward Neural Networks

2006 ◽  
pp. 207-218
Author(s):  
Yang Shi ◽  
Weiqi Qian ◽  
Weisheng Yan ◽  
Jun Li
Keyword(s):  
Neural Networks ◽  
Autonomous Underwater Vehicles ◽  
Feedforward Neural Networks ◽  
Underwater Vehicles ◽  
Depth Control

scholarly journals Coordinated Depth Control of Multiple Autonomous Underwater Vehicles by Using Theory of Adaptive Sliding Mode

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yintao Wang ◽  
Yani Zhang
Keyword(s):  
Control Algorithm ◽  
Sliding Mode ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Loop Control ◽  
Depth Control ◽  
The Common ◽  
Extended Graph ◽  
Rudder Angle ◽  
Theoretical Results

This paper addresses the coordinated depth control problem of multiple autonomous underwater vehicles, which means to maneuver a group of underwater vehicles which move at the same depth synchronously. Firstly, a coordinated error of depth between vehicles and the common desired depth is defined by using extended graph theory in a distributed manner; then a deep-pitch double loop control algorithm based on sliding mode is designed for each vehicle, by which each vehicle is driven to and move at the common depth coordinately. In particular, a pitch reference command is firstly calculated by the predefined coordinated depth error, which can be regarded as the outer loop control, and then, the input rudder angle for each vehicle is derived according to the pitch reference command being as the inner loop control. Considering the uncertainties of the model hydrodynamic parameters, an online parameter adaptive algorithm is introduced to improve the performance of the sliding mode control algorithm proposed. Simulations were performed to verify the theoretical results proposed.

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