Neural Network Learning and Robust Stabilization of Nonlinear Systems With Dynamic Uncertainties

Area of Science:

• Control Engineering
• Nonlinear Systems
• Artificial Intelligence

Background:

• Dynamical uncertainties pose challenges for nonlinear control systems, particularly in adaptive critic control design.
• Ensuring robustness is crucial for reliable performance in the presence of unpredictable system variations.

Purpose of the Study:

• To develop a robust stabilization scheme for nonlinear systems with general uncertainties.
• To leverage neural network learning and adaptive critic techniques for controller design.
• To ensure stability for both nominal and uncertain system dynamics.

Main Methods:

• A robust stabilization scheme is developed using a neural network learning component.
• System transformation and adaptive critic techniques are employed to adapt controllers for uncertain dynamics.
• An improved critic learning formulation facilitates convenient initialization of neural network weight vectors.

Main Results:

• The approximate optimal controller designed for the nominal plant achieves robust stabilization for the uncertain dynamics.
• Stability analyses are performed for the closed-loop systems under the approximate optimal control law.
• Simulations on a nonlinear system and a power system demonstrate effective control performance.

Conclusions:

• The proposed method effectively achieves robust stabilization for nonlinear systems with general uncertainties.
• The integration of neural networks and adaptive critic control offers a viable approach for handling system uncertainties.
• The findings are validated through practical system simulations, confirming the control strategy's efficacy.