Distributed Optimal Consensus Problem of Input Constrained Nonlinear Discrete-Time MASs: A Mode-Free Reinforcement...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Optimal consensus control is crucial for coordinating multiagent systems.
• Nonlinear discrete-time systems with input constraints pose significant control challenges.
• Solving coupled discrete Hamilton-Jacobi-Bellman (HJB) equations is computationally intensive.

Purpose of the Study:

• To develop a model-free reinforcement learning (RL) approach for optimal consensus control in nonlinear discrete-time multiagent systems.
• To address actuator input constraints and distributed synchronization issues in these systems.

Main Methods:

• An actor-critic reinforcement learning framework is employed to solve the optimal consensus control problem.
• A well-defined cost function guides the online learning updates of actor and critic networks.
• A gradual transition control (GTC) method, with update-free and update-weak policies, is introduced to manage actuator constraints.
• A synchronization blocking method is designed to handle distributed synchronization challenges in networked agents.

Main Results:

• The proposed RL approach successfully achieves optimal consensus control for nonlinear discrete-time multiagent systems.
• The gradual transition control method effectively handles actuator limitations.
• The synchronization blocking method ensures reliable distributed operation.
• Simulations demonstrate the approach's effectiveness across different scenarios.

Conclusions:

• The model-free RL strategy provides an effective solution for optimal consensus control in constrained multiagent systems.
• The integration of GTC and synchronization blocking enhances the robustness and applicability of the control system.
• The proposed methods offer a viable framework for real-world distributed control applications.