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An iterative Q-learning based global consensus of discrete-time saturated multi-agent systems.

Mingkang Long1, Housheng Su1, Xiaoling Wang2

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This study introduces a model-free Q-learning algorithm for discrete-time multiagent systems (DTMASs) with input saturation. This approach enables global consensus without needing agent dynamics information.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Networked Systems

Background:

  • Discrete-time multiagent systems (DTMASs) face challenges with input saturation and unknown agent dynamics.
  • Existing low-gain feedback (LGF) methods for semiglobal consensus require knowledge of agent dynamics to compute LGF matrices via Riccati equations.

Purpose of the Study:

  • To develop a model-free algorithm for achieving global consensus in DTMASs with input saturation.
  • To overcome the limitation of requiring agent dynamics information in previous consensus algorithms.

Main Methods:

  • A Q-learning algorithm is proposed, defining a Q-learning function and deriving a Bellman equation.
  • An iterative Q-learning algorithm is developed to compute the LGF matrix without prior knowledge of agent dynamics.
  • The algorithm is designed to ensure global consensus for the DTMASs.

Main Results:

  • The proposed Q-learning algorithm successfully obtains the LGF matrix for DTMASs.
  • Global consensus is achieved for the DTMASs under input saturation.
  • Simulation results validate the algorithm's effectiveness and analyze convergence rates based on initial states and saturation limits.

Conclusions:

  • The model-free Q-learning approach effectively addresses the consensus problem in DTMASs with input saturation.
  • This method eliminates the need for agent dynamics information, offering a significant advancement over existing techniques.
  • The algorithm provides a practical solution for achieving global consensus in complex multiagent systems.