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Related Experiment Videos

Optimized predefined-time control for high-order nonlinear MASs via ICA and reinforcement learning.

Qunsheng Zhang1, Jianqiang Hu1, Jinde Cao1

  • 1School of Mathematics, Southeast University, Nanjing 211189, China.

ISA Transactions
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive controller for nonlinear multi-agent systems (MASs) achieving practical predefined-time consensus. The novel approach integrates reinforcement learning (RL) and sliding mode control (SMC) for faster, adaptable system coordination.

Keywords:
Nonlinear multi-agent systemsPredefined-time controlPrescribed performanceReinforcement learningSliding mode control

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Multi-agent systems (MASs) often face challenges in achieving coordinated behavior due to their high-order nonlinear dynamics.
  • Existing consensus algorithms may lack adaptability and precise convergence time guarantees.
  • Predefined-time control offers faster convergence than traditional finite-time methods but requires careful design for practical implementation.

Purpose of the Study:

  • To develop an adaptive optimized controller for high-order nonlinear MASs enabling practical predefined-time consensus.
  • To ensure tracking errors are bounded within prescribed performance limits.
  • To achieve convergence to a compact residual set within an adjustable predefined time, independent of initial conditions.

Main Methods:

  • Integration of reinforcement learning (RL) and sliding mode control (SMC) within an identifier-critic-actor (ICA) framework.
  • Utilization of prescribed-performance control to shape tracking errors.
  • Application of a projection-correction technique in neural-network (NN) weight updates to prevent drift and ensure stability.
  • Minimization of a cost function balancing consensus error and control input.

Main Results:

  • The proposed controller successfully achieves practical predefined-time consensus in high-order nonlinear MASs.
  • Convergence time is adjustable via design parameters and independent of system initial states.
  • The projection-correction technique effectively stabilizes neural network weights.
  • Numerical simulations confirm the controller's effectiveness and performance.

Conclusions:

  • The adaptive ICA framework with RL and SMC provides an effective solution for predefined-time consensus in complex MASs.
  • The method offers enhanced control over convergence time and stability, crucial for real-world applications.
  • This approach advances the state-of-the-art in coordinated control for nonlinear multi-agent systems.