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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with

Zhenyou Wang1, Xiaoquan Cai1, Ao Luo2

  • 1School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a fixed-time optimal consensus control for stochastic nonlinear multi-agent systems facing sensor faults. The method ensures consensus errors meet performance bounds despite sensor failures using adaptive neural networks and reinforcement learning.

Keywords:
fixed-time prescribed performanceoptimal consensus controlreinforcement learningsensor faultsstochastic nonlinear multi-agent systems

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Stochastic nonlinear multi-agent systems (MAS) present challenges in achieving consensus, especially with sensor faults.
  • Achieving prescribed performance bounds in finite or fixed time is crucial for practical applications.

Purpose of the Study:

  • To develop a fixed-time prescribed performance optimal consensus control strategy for stochastic nonlinear MAS with sensor faults.
  • To address the impact of unknown sensor faults using adaptive compensation and reinforcement learning.

Main Methods:

  • An improved performance function and coordinate transformation for fixed-time convergence.
  • A neural network-based adaptive compensation strategy for sensor fault tolerance.
  • A reinforcement-learning-based backstepping method for optimal control design.

Main Results:

  • Consensus error converges to prescribed performance bounds in fixed time.
  • All closed-loop system signals are bounded in probability, demonstrating stability.
  • Simulation results validate the effectiveness of the proposed control method.

Conclusions:

  • The proposed method effectively achieves fixed-time optimal consensus control in stochastic nonlinear MAS with sensor faults.
  • The integration of adaptive neural networks and reinforcement learning enhances fault tolerance and control performance.