Self-Learning Robust Control Synthesis and Trajectory Tracking of Uncertain Dynamics

Area of Science:

• Control Engineering
• Machine Learning
• Dynamical Systems

Background:

• Uncertain dynamical systems pose significant challenges for control design.
• Traditional robust control methods often require precise system models.
• Adaptive critic learning offers a promising approach for learning control policies in unknown environments.

Purpose of the Study:

• To develop a self-learning robust control synthesis method for general uncertain dynamical systems.
• To extend the developed method for robust trajectory tracking design.
• To ensure the stability and robustness of the proposed control system.

Main Methods:

• Problem transformation for robust stabilization.
• Adaptive critic learning for control synthesis.
• Optimal control with discounted cost functions for trajectory tracking.
• Lyapunov stability analysis for robustness verification.

Main Results:

• Successful synthesis of a self-learning robust control for uncertain systems.
• Demonstration of robust trajectory tracking capabilities.
• Validation of system robustness and stability through theoretical analysis and simulations.

Conclusions:

• The proposed adaptive critic learning-based method effectively achieves robust control and trajectory tracking for uncertain systems.
• Lyapunov stability analysis confirms the robustness of the control plants.
• Simulation results validate the practical applicability of the developed control strategies.