Communication Delay-Based Under-Actuated MASVs Distributed Formation Tracking Control With Unknown Ocean Disturbances and Input Quantization | JoVE Visualize

Area of Science:

Robotics and Control Systems
Marine Engineering
Autonomous Systems

Background:

Formation control of multiple autonomous surface vehicles (MASVs) is complex due to under-actuation, communication delays, and environmental uncertainties.
Existing control methods often struggle with input quantization and external marine disturbances, limiting practical application.

Purpose of the Study:

To develop a robust two-level distributed guidance and quantization control architecture for under-actuated MASVs.
To address challenges including communication delays, input quantization, marine disturbances, and internal model uncertainties.
To enhance the practical applicability of MASV formation control in marine engineering.

Main Methods:

A two-level distributed guidance and quantization control architecture utilizing the Nussbaum function.
A time-delay distributed event-triggered extended state observer (ESO) for leader state estimation and communication resource conservation.
Fuzzy logic systems for estimating uncertain model terms and a linear model for handling input quantization.
Fuzzy adaptive quantization tracking control laws for accurate guidance signal tracking and reduced actuator frequency.

Main Results:

The proposed control architecture effectively estimates states of neighboring agents and unknown ocean disturbances.
Fuzzy adaptive control laws achieve accurate tracking of guidance signals while minimizing actuator usage.
Stability analysis confirms that all signals within the closed-loop system are uniformly ultimately bounded.
Simulation experiments demonstrate the method's rationality and effectiveness.

Conclusions:

The developed control strategy provides a robust solution for the formation control of under-actuated MASVs under challenging conditions.
The integration of ESO, fuzzy logic, and Nussbaum functions enhances control accuracy and system applicability in marine environments.
The proposed method offers a significant advancement for practical marine engineering applications requiring coordinated autonomous vehicle operations.