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Distributed adaptive consensus tracking control for heterogeneous nonlinear multi-agent systems.

Lei He1, Wenhan Dong1

  • 1Aviation Engineering School, Air Force Engineering University, Xi'an, 710038, China.

ISA Transactions
|April 24, 2022
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Summary
This summary is machine-generated.

This study resolves consensus issues in heterogeneous nonlinear multi-agent systems using local information and a command filtered backstepping approach. The method ensures system stability and accurate formation tracking for unmanned aerial vehicles.

Keywords:
Adaptive controlDistributed consensus trackingFormation controlHeterogeneous nonlinear multi-agent systems

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Area of Science:

  • Control Theory
  • Robotics
  • Systems Engineering

Background:

  • Heterogeneous nonlinear multi-agent systems present challenges in achieving consensus due to limited information access.
  • Distributed control strategies are crucial for coordinating multiple agents effectively.

Purpose of the Study:

  • To develop a distributed adaptive consensus controller for heterogeneous nonlinear multi-agent systems.
  • To address the consensus problem using only locally available information.
  • To solve the formation-tracking problem for nonholonomic unmanned aerial vehicles.

Main Methods:

  • Employing the command filtered backstepping approach for controller design.
  • Utilizing local information for distributed control.
  • Analyzing the uniform ultimate boundedness of closed-loop signals.

Main Results:

  • Achieved convergence of consensus tracking error to a small, tunable compact set.
  • Demonstrated the uniform, ultimate boundedness of all closed-loop signals.
  • Successfully applied the control algorithm to the formation-tracking problem of nonholonomic unmanned aerial vehicles.

Conclusions:

  • The proposed distributed adaptive consensus controller effectively resolves consensus issues in heterogeneous nonlinear multi-agent systems.
  • The command filtered backstepping approach enables robust control using local information.
  • The algorithm provides a viable solution for complex formation-tracking tasks in robotics.