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Reinforcement learning-based consensus control for MASs with intermittent constraints.

Ao Luo¹, Qi Zhou¹, Hongru Ren¹

¹School of Automation, Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, 510000, Guangdong, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 17, 2024

Summary

This summary is machine-generated.

This study presents a new method for controlling multiagent systems with intermittent constraints. The approach effectively handles constraints without feasibility conditions, ensuring system stability and optimal performance.

Keywords:

Composite learning Intermittent constraints Multiagent systems Neural networks Reinforcement learning

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Area of Science:

Control Theory
Artificial Intelligence
Systems Engineering

Background:

Multiagent systems (MAS) are crucial in various applications.
Controlling MAS with strict-feedback structures and intermittent constraints is challenging.
Existing methods often require feasibility conditions or suffer from complexity.

Purpose of the Study:

To develop an adaptive optimal consensus control strategy for MAS with intermittent constraints.
To address the challenges of intermittent constraints without feasibility conditions.
To overcome the complexity explosion issue in backstepping control.

Main Methods:

Designing a novel switch-like function and coordinate transformation to handle intermittent constraints.
Employing a composite learning algorithm and neural networks for an identifier-actor-critic-based reinforcement learning strategy.
Utilizing nonlinear dynamic surface control (DSC) to mitigate complexity.
Applying Lyapunov stability theory to prove system boundedness.

Main Results:

Successfully converted constrained states to unconstrained states, resolving intermittent constraint issues.
Developed a simplified identifier-actor-critic-based reinforcement learning approach for approximate optimal control.
Eliminated the "explosion of complexity" problem inherent in backstepping control.
Demonstrated boundedness of all signals in the closed-loop system via Lyapunov stability theory.

Conclusions:

The proposed method effectively solves the adaptive optimal consensus control problem for MAS with intermittent constraints.
The approach removes the need for feasibility conditions and simplifies the control design.
Simulation examples confirm the method's practical effectiveness and robustness.