Singularity Avoidance Fixed-Time Adaptive Neural Control for Autonomous Underwater Vehicles Considering Unmodelled Dynamics and Disturbances

This brief concerns the fixed-time backstepping trajectory tracking control problem for uncertain AUVs subject to unknown input saturation. By making use of the command filter technique, the adaptive control method and neural networks, a low-complexity nonlinear controller that contains only three d...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on circuits and systems. II, Express briefs Vol. 71; no. 2; pp. 822 - 826
Main Authors Liu, Yuqing, Liu, Jiapeng, Yu, Jinpeng
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Artificial neural networks
Autonomous underwater vehicles
Backstepping
Circuit stability
Closed loops
command filter
Control methods
Feedback control
fixed-time control
neural network
Neural networks
Nonlinear control
Singularities
Stability analysis
Switches
Tracking control
Trajectory control
Uncertainty
Vehicle dynamics
Online AccessGet full text

Cover

More Information
Summary:This brief concerns the fixed-time backstepping trajectory tracking control problem for uncertain AUVs subject to unknown input saturation. By making use of the command filter technique, the adaptive control method and neural networks, a low-complexity nonlinear controller that contains only three dynamic update laws is proposed. And the new fixed-time stabilizing function is proposed to avoid the singularity problem. System performance analysis shows that the fixed-time stability is guaranteed for the AUV closed-loop system without violating the input saturation. The simulation result is given to demonstrate the effectiveness of our developed strategy.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2023.3314730