Optimal Synchronization Control of Multiagent Systems With Input Saturation via Off-Policy Reinforcement Learning

Area of Science:

• Control Systems Engineering
• Optimization Theory
• Machine Learning

Background:

• Optimal synchronization is crucial for coordinated system behavior.
• Input saturation presents a significant challenge in control system design.
• Analytical solutions for Hamilton-Jacobi-Bellman (HJB) equations are often intractable, especially for coupled systems.

Purpose of the Study:

• To investigate the optimal synchronization problem for generic linear systems with input saturation.
• To develop a data-driven approach for learning optimal control policies.
• To address the challenges posed by coupled HJB equations and potential lack of model information.

Main Methods:

• Establishment of coupled Hamilton-Jacobi-Bellman (HJB) equations with nonquadratic input energy terms.
• Application of a data-based off-policy reinforcement learning algorithm.
• Utilization of actor and critic neural networks for policy and cost function approximation.

Main Results:

• Optimal controllers are derived from HJB equation solutions, forming an interactive Nash equilibrium.
• The off-policy reinforcement learning algorithm demonstrates insensitivity to probing noise.
• The implemented algorithm's estimated control policies are proven to converge to optimal ones.

Conclusions:

• The proposed data-based off-policy reinforcement learning method effectively solves the optimal synchronization problem for linear systems with input saturation.
• The use of actor-critic neural networks provides a practical implementation for learning optimal control.
• The algorithm's robustness to noise and potential model uncertainties is a key advantage.