Neural H₂ Control Using Continuous-Time Reinforcement Learning.

Area of Science:

• Control Theory
• Machine Learning
• Nonlinear Systems

Background:

• Controlling unknown nonlinear systems presents significant challenges.
• Traditional control methods often struggle with system uncertainties and nonlinear dynamics.
• H2 control offers a robust framework but requires accurate system models.

Purpose of the Study:

• To develop and validate a continuous-time H2 control strategy for unknown nonlinear systems.
• To leverage differential neural networks for system modeling and reinforcement learning for performance enhancement.
• To ensure the stability and convergence of the proposed neural H2 control approach.

Main Methods:

• System identification using differential neural networks to create an accurate model.
• Application of H2 tracking control based on the derived neural model.
• Integration of reinforcement learning to mitigate neural modeling errors and improve control performance.
• Mathematical proofs for the stability of neural modeling and H2 tracking control.

Main Results:

• The proposed method demonstrates effective H2 tracking control for unknown nonlinear systems.
• Reinforcement learning significantly enhances control performance by addressing neural modeling uncertainties.
• Stability and convergence of the developed control strategy are rigorously proven.
• Validation on two benchmark control problems confirms the method's efficacy.

Conclusions:

• The combination of differential neural networks and reinforcement learning provides a robust solution for H2 control of unknown nonlinear systems.
• The proven stability and convergence highlight the reliability of the proposed approach.
• This method offers a promising direction for advanced control applications in complex dynamic environments.