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Distributed model reference adaptive containment control of heterogeneous uncertain multi-agent systems.

Chengzhi Yuan1, Wei Zeng2, Shi-Lu Dai3

  • 1Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA.

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|December 4, 2018
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Summary

A new distributed adaptive control framework ensures multi-agent systems achieve containment control despite uncertain dynamics. This model reference adaptive control (MRAC) approach works even with limited leader information.

Keywords:
Containment controlDistributed model reference adaptive controlHeterogeneous multi-agent systems

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Area of Science:

  • Robotics
  • Control Theory
  • Systems Engineering

Background:

  • Heterogeneous multi-agent systems (MAS) often face challenges in achieving coordinated containment control due to uncertain dynamics and limited information.
  • Existing cooperative output regulation frameworks have limitations in handling stringent control environments and inaccessible leader information.

Purpose of the Study:

  • To propose a novel distributed model reference adaptive control (MRAC) design framework for containment control of uncertain heterogeneous MAS.
  • To develop adaptive control protocols that accommodate general linear dynamics for both leader and follower agents.

Main Methods:

  • Developed two distributed adaptive control protocols within the MRAC framework.
  • The first protocol uses distributed observers and state-feedback adaptive controllers for measurable leader inputs.
  • The second protocol employs robust adaptive control with nonlinear compensators for unmeasurable leader inputs.

Main Results:

  • Achieved exact containment control performance with the first protocol.
  • Guaranteed convergence of containment control errors to a controllable neighborhood of the origin with the second protocol.
  • Demonstrated effectiveness through extensive simulations, including nonholonomic mobile robots.

Conclusions:

  • The proposed MRAC framework offers a robust alternative for heterogeneous MAS containment control.
  • It effectively addresses scenarios with uncertain follower dynamics and limited leader information.
  • The approach enhances control capabilities in complex and restrictive operational environments.