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Event-Triggered Dynamic Coverage Control for Multiple Stratospheric Airships.

Yifei Zhang1, Ming Zhu2, Tian Chen3

  • 1School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic coverage control framework for multiple stratospheric airships (MSAs) to ensure full observation of target regions. An event-triggered mechanism reduces control frequency, enhancing actuator longevity.

Keywords:
artificial dynamic potential fielddynamic coverage controlevent-triggered control

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Artificial Intelligence

Background:

  • The dynamic coverage control problem for multiple stratospheric airships (MSAs) is critical for practical observation missions.
  • Existing systems face challenges with processing delays, dynamic target switching, and external disturbances.

Purpose of the Study:

  • To develop a robust dynamic coverage control framework for MSAs.
  • To enhance the system's ability to adapt to changing observation targets and environmental conditions.
  • To improve the efficiency and longevity of MSA actuators.

Main Methods:

  • Utilized an autoregressive model for predicting target location amidst processing delays.
  • Implemented a two-layer control scheme: a dynamic artificial potential field for virtual control and an adaptive tracking controller with saturation compensation.
  • Incorporated an event-triggered mechanism to optimize control frequency and reduce actuator wear.

Main Results:

  • The proposed framework effectively guides MSAs for dynamic coverage control of target regions.
  • The adaptive tracking controller successfully addresses external disturbances and input saturation.
  • Simulation results validate the framework's capability in practical application scenarios.

Conclusions:

  • The novel dynamic coverage control framework provides a viable solution for MSA systems.
  • The integration of predictive modeling, advanced control strategies, and event-triggering enhances operational efficiency and system reliability.
  • The framework demonstrates significant potential for real-world applications requiring adaptive and persistent surveillance.