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Distributed adaptive fixed-time neural networks control for nonaffine nonlinear multiagent systems.

Yang Li1, Quanmin Zhu2, Jianhua Zhang3

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266525, China.

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This study introduces a fixed-time neural networks control scheme for nonaffine nonlinear multiagent systems. The adaptive backstepping controllers ensure follower agents track the leader in a fixed time, with calculable settling times.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Multiagent systems (MAS) are crucial in distributed control.
  • Achieving consensus tracking in nonaffine nonlinear MAS is challenging.
  • Existing methods often lack fixed-time convergence guarantees.

Purpose of the Study:

  • To develop a novel fixed-time consensus tracking control scheme for nonaffine nonlinear MAS.
  • To design distributed adaptive backstepping neural networks controllers.
  • To ensure finite-time convergence with a predictable settling time bound.

Main Methods:

  • Adaptive backstepping technique combined with neural networks.
  • Homeomorphism mapping theory for error system derivation.
  • Fixed-time adaptive law for neural network weight training.

Main Results:

  • A distributed adaptive backstepping neural networks control algorithm was formulated.
  • The control scheme guarantees fixed-time leader-follower consensus tracking.
  • The settling time upper bound is calculable without initial system parameters.

Conclusions:

  • The proposed control scheme effectively achieves consensus tracking in fixed time for nonaffine nonlinear MAS.
  • The method offers a practical approach for engineers and researchers.
  • The fixed-time convergence and calculable settling time are key advantages.