Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization

In this paper, a design method is presented for path-following control of underactuated autonomous underwater vehicles subject to velocity and input constraints, as well as internal and external disturbances. In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 66; no. 11; pp. 8724 - 8732
Main Authors: Peng, Zhouhua, Wang, Jun, Han, Qing-Long
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous underwater vehicles
Autonomous underwater vehicles (AUVs)
Bridge construction
Computer simulation
Constraints
Control theory
Design optimization
Disturbances
extended state observer (ESO)
input and state constraints
Kinematics
Kinetic theory
Neural networks
neurodynamic optimization
Neurodynamics
Observers
Optimization
path following
State observers
Surges
ISSN:0278-0046, 1557-9948
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a design method is presented for path-following control of underactuated autonomous underwater vehicles subject to velocity and input constraints, as well as internal and external disturbances. In the guidance loop, a kinematic control law of the desired surge speed and pitch rate is derived based on a backstepping technique and a line-of-sight guidance principle. In the control loop, an extended state observer is developed to estimate the extended state composed of unknown internal dynamics and external disturbances. Then, a disturbance rejection control law is constructed using the extended state observer. To bridge the guidance loop and the control loop, a reference governor is proposed for computing optimal guidance signals within the velocity and input constraints. The reference governor is formulated as a quadratically constrained optimization problem. A projection neural network is employed for solving the optimization problem in real time. Simulation results illustrate the effectiveness of the proposed method for path-following control of autonomous underwater vehicles subject to constraints and disturbances simultaneously in the vertical plane.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2885726