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ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints.

Zijie Guo1, Qi Zhou2, Hongru Ren2

  • 1School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.

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Summary

This study develops an adaptive dynamic programming (ADP) method for nonlinear multiagent systems (MASs) with irregular state constraints and actuator faults. The approach ensures fault-tolerant consensus control and optimal tracking performance.

Keywords:
Actuator faultsAdaptive dynamic programming (ADP)Finite-time distributed observerIrregular state constraintsNonlinear multiagent systems (MASs)

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Area of Science:

  • Control Theory
  • Artificial Intelligence
  • Robotics

Background:

  • Nonlinear multiagent systems (MASs) face challenges with state constraints and actuator faults.
  • Existing methods often overlook irregular, time-varying, or emergent state constraints.

Purpose of the Study:

  • To address consensus control for MASs with irregular state constraints and actuator faults.
  • To develop a fault-tolerant control strategy using adaptive dynamic programming (ADP).

Main Methods:

  • System transformation to convert constrained MASs into unconstrained ones.
  • Finite-time distributed observer for leader state estimation.
  • Critic-only ADP-based fault-tolerant control strategy with online fault compensation.
  • Improved weight learning law for neural networks (NNs) using historical data.

Main Results:

  • Achieved optimal tracking control for each agent despite irregular constraints and actuator faults.
  • Ensured that actuator faults do not propagate to neighboring agents.
  • Demonstrated convergence of NN weights under finite excitation conditions.
  • Validated the method with a multiple manipulator systems example.

Conclusions:

  • The proposed ADP-based method effectively handles irregular state constraints and actuator faults in MASs.
  • The approach ensures robust and optimal consensus control.
  • The method enhances learning efficiency and stability of the control system.