Toward reliable designs of data-driven reinforcement learning tracking control for Euler-Lagrange systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Traditional control methods struggle with complex nonlinear dynamics and require precise system models.
• Reinforcement learning offers adaptive control but often lacks guaranteed stability.
• Direct heuristic dynamic programming (dHDP) provides a data-driven approach for optimal control but can be limited in stability analysis.

Purpose of the Study:

• To develop a mathematically sound and practically implementable reinforcement learning-based direct signal tracking control strategy.
• To integrate adaptive learning with guaranteed stability for nonlinear dynamic systems.
• To achieve reproducible neural network-based control solutions.

Main Methods:

• A hybrid control design combining a reinforcement learning (RL) based, data-driven approach with a backstepping-based stability framework.
• Utilizing the direct heuristic dynamic programming (dHDP) learning paradigm for online adaptation and optimization.
• Applying a backstepping design methodology to ensure closed-loop stability for Euler-Lagrange systems.

Main Results:

• Theoretical guarantees for the stability of the closed-loop dynamic system.
• Proof of weight convergence for the approximating nonlinear neural networks.
• Demonstration of Bellman (sub)optimality for the derived control policy.
• Simulations showing significantly improved performance compared to the original dHDP approach.

Conclusions:

• The proposed hybrid control strategy effectively combines reinforcement learning adaptation with backstepping stability for direct signal tracking.
• The method provides a reliable and reproducible framework for neural network-based control of nonlinear systems.
• This approach offers enhanced performance and stability guarantees over existing dHDP techniques.