Attitude Control Based Autonomous Underwater Vehicle Multi-mission Motion Control with Deep Reinforcement Learning

The application of Autonomous Underwater Vehicle (AUV) is expanding rapidly, which drives the urgent need of its autonomy improvement. Motion control system is one of the keys to improve the control and decision-making ability of AUVs. In this paper, a saturation based nonlinear fractional-order PD (FOPD) controller is proposed for AUV motion control. The proposed controller is can achieve better dynamic performance as well as robustness compared with traditional PID type controller. It also has the advantages of simple structure, easy adjustment and easy implementation. The stability of the AUV motion control system with the proposed controller is analyzed through Lyapunov method. Moreover, the controlled performance can also be adjusted to satisfy different control requirements. The outperformed dynamic control performance of AUV yaw and depth systems with the proposed controller is shown by the set-point regulation and trajectory tracking simulation examples.

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Design and motion control of Autonomous Underwater Vehicle, Amogh

2015 IEEE Underwater Technology (UT) ◽

10.1109/ut.2015.7108287 ◽

2015 ◽

Cited By ~ 7

Author(s):

V. Upadhyay ◽

S. Gupta ◽

A.C. Dubey ◽

M.J. Rao ◽

P. Siddhartha ◽

...

Keyword(s):

Motion Control ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle

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Implementation of Intelligent Control System for Autonomous Underwater Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.701-702.704 ◽

2014 ◽

Vol 701-702 ◽

pp. 704-710 ◽

Cited By ~ 2

Author(s):

Viacheslav Pshikhopov ◽

Yuriy Chernukhin ◽

Viktor Guzik ◽

Mikhail Medvedev ◽

Boris Gurenko ◽

...

Keyword(s):

Control System ◽

Motion Control ◽

Intelligent Control ◽

Autonomous Underwater Vehicle ◽

Control Method ◽

Underwater Vehicle ◽

Intelligent Control System ◽

Azov Sea ◽

Complex Simulation ◽

Point To Point

This paper introduces the implementation of intelligent motion control and planning for autonomous underwater vehicle (AUV). Previously developed control system features intelligent motion control and planning subsystem, based on artificial neural networks. It allows detecting and avoiding moving obstacles in front of the AUV. The motion control subsystem uses position-trajectory control method to position AUV, move from point to point and along given path with given speed. Control system was tested in the multi-module simulation complex. Simulation showed good results – AUV successfully achieved given goals avoiding collisions not only with static obstacles, but also with mobile ones. That allows using the proposed control system for the groups of vehicles. Besides simulation, control system was implemented in hardware. AUV prototype passed tests in Azov Sea and proved its efficiency.

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