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Related Experiment Video

Updated: Jul 3, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Predefined-time distributed optimal formation control for constrained UAV-UGV systems.

Wei Yang1, Jiapeng Liu1, Yumei Ma2

  • 1School of Automation, Qingdao University, Qingdao, 266071, Shandong Province, China.

ISA Transactions
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new control strategy for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to achieve optimal formation control in a predefined time, ensuring system constraints are met.

Keywords:
Command filtered backsteppingDistributed controlFuzzy adaptive controlHeterogeneous UAV-UGV systems

Related Experiment Videos

Last Updated: Jul 3, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Heterogeneous systems of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present complex control challenges.
  • Achieving optimal formation control with predefined-time convergence and state constraints is a significant research gap.

Purpose of the Study:

  • To develop a distributed predefined-time optimal adaptive formation control strategy for heterogeneous UAV-UGV systems.
  • To address asymmetric state constraints within the planar subsystem.

Main Methods:

  • A nonlinear mapping and relaxation function were used to handle asymmetric state constraints.
  • A single critic structure was integrated into a command filtered backstepping framework.
  • Adaptive fuzzy approximation was employed to model unknown nonlinearities and performance functions.

Main Results:

  • The proposed controller ensures formation errors converge within a predefined time.
  • Vehicle positions are maintained within specified constraints throughout the operation.
  • Simulation results validate the effectiveness of the developed control scheme.

Conclusions:

  • The study successfully demonstrates a novel approach for distributed predefined-time optimal adaptive formation control in heterogeneous robotic systems.
  • The method effectively handles state constraints and unknown system dynamics.
  • This work contributes to advancements in coordinated control for multi-robot systems.