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Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with

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  • 1Center for Control and Optimization, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 8, 2018
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Summary

This study introduces a novel decentralized adaptive neural controller for complex stochastic nonlinear systems with time delays. The method ensures system stability and accurate tracking, reducing learning parameters for efficient control.

Keywords:
Decentralized adaptive controlHigh-order systemsLyapunov–Krasovskii functionalsNeural networksStochastic nonlinear time-delay systems

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Stochastic Systems

Background:

  • High-order large-scale nonlinear systems with stochastic disturbances and time delays present significant control challenges.
  • Existing control methods often struggle with over-parameterization and unknown system dynamics.

Purpose of the Study:

  • To develop a decentralized adaptive backstepping state-feedback control strategy for uncertain high-order large-scale stochastic nonlinear time-delay systems.
  • To address over-parameterization and unknown system dynamics using neural networks and Lyapunov stability theory.

Main Methods:

  • Employed neural networks to handle unknown system dynamics and stochastic disturbances.
  • Utilized Lyapunov-Krasovskii functional and hyperbolic tangent functions to manage unknown time-delay interactions.
  • Designed a decentralized adaptive neural controller based on Lyapunov stability theory.

Main Results:

  • Successfully developed a decentralized adaptive neural controller that decreases the number of learning parameters.
  • Ensured all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB).
  • Demonstrated convergence of tracking error to a small neighborhood of zero.

Conclusions:

  • The proposed control method effectively stabilizes uncertain high-order large-scale stochastic nonlinear time-delay systems.
  • The approach offers an efficient solution by reducing learning parameters.
  • Simulation results validate the efficacy of the developed decentralized adaptive neural controller.