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Integral Reinforcement-Learning-Based Optimal Containment Control for Partially Unknown Nonlinear Multiagent Systems.

Qiuye Wu1, Yongheng Wu1, Yonghua Wang1

  • 1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an integral reinforcement learning algorithm for optimal containment control in nonlinear multiagent systems with unknown dynamics. The method ensures system stability and effective control using neural networks and input-output data.

Keywords:
adaptive dynamic programmingcontainment controlintegral reinforcement learningmultiagent systemsneural networks

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Multiagent systems (MAS) present complex control challenges, especially with partially unknown dynamics.
  • Optimal containment control is crucial for coordinating MAS behavior and ensuring desired formations.

Purpose of the Study:

  • To develop an optimal containment control strategy for nonlinear multiagent systems with unknown dynamics.
  • To relax the requirement for drift dynamics and ensure algorithm convergence.

Main Methods:

  • Integral reinforcement learning (IRL) algorithm applied to nonlinear MAS.
  • Utilizing a single critic neural network to solve the Hamilton-Jacobi-Bellman equation.
  • Employing a modified updating law for asymptotic stability of weight error dynamics.

Main Results:

  • The IRL method is proven equivalent to model-based policy iteration, ensuring convergence.
  • Approximate optimal containment control protocols are derived using input-output data and neural networks.
  • The closed-loop containment error system stability is guaranteed.

Conclusions:

  • The proposed integral reinforcement learning scheme effectively achieves optimal containment control for nonlinear MAS.
  • Simulation results validate the robustness and effectiveness of the developed control strategy.