Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Multi-agent systems (MAS) require sophisticated control for coordinated behavior.
• Optimizing performance and state synchronization in nonlinear MAS is challenging.
• Existing reinforcement learning (RL) methods for optimal control often involve complex algorithms.

Purpose of the Study:

• To propose an optimized leader-following consensus control scheme for nonlinear strict-feedback-dynamic multi-agent systems.
• To simplify RL-based optimal control algorithms by modifying the update law derivation.
• To relax common conditions like known dynamics and persistence of excitation in RL control.

Main Methods:

• Utilizing an optimized backstepping technique for virtual and actual control design.
• Employing neural network approximation-based reinforcement learning (RL) with a critic-actor architecture.
• Deriving RL update laws from the negative gradient of a simplified positive function related to the Hamilton-Jacobi-Bellman (HJB) equation.

Main Results:

• The proposed control scheme simplifies RL algorithms compared to traditional methods.
• The approach achieves optimized system performance and synchronization of multiple system state variables.
• The method successfully relaxes the need for known dynamic and persistence excitation conditions.
• Effectiveness demonstrated through theoretical analysis and simulation results.

Conclusions:

• The developed optimized control scheme is suitable for high-order nonlinear multi-agent systems.
• The simplified RL approach offers a more practical solution for complex control problems.
• The findings contribute to advancements in intelligent control for coordinated multi-agent behavior.