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Summary

This study introduces a novel leader-following control strategy for stochastic multiagent systems. The approach effectively handles complex output constraints, ensuring optimal system performance and stability.

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Area of Science:

Control Theory
Artificial Intelligence
Robotics

Background:

Stochastic multiagent systems (MAS) present significant control challenges.
Existing methods struggle with complex and arbitrary output constraints.

Purpose of the Study:

To develop a novel leader-following tracking control approach for stochastic MAS.
To address multibridge-hole output constraints, allowing arbitrary constrained and unconstrained intervals.

Main Methods:

Designed a new shift function to construct a barrier Lyapunov function.
Combined backstepping and adaptive dynamic programming (ADP) techniques for optimal controller design.
Utilized model networks for estimating system uncertainties and disturbances.
Employed critic and actor networks to ensure adherence to the Bellman optimality principle.

Main Results:

Successfully developed an optimal controller for stochastic MAS with multibridge-hole output constraints.
The model network effectively estimated unknown system disturbances and uncertainties.
The critic and actor networks ensured the controller's optimality.
Simulation results verified the proposed method's effectiveness.

Conclusions:

The proposed control method is versatile and compatible with various output-constrained problems.
It offers a unified framework for unconstrained, constrained, and delay-constrained control.
This approach provides a robust solution for complex stochastic multiagent systems.