Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Uncertainty and time-varying disturbances pose significant challenges in output feedback control systems.
• Existing adaptive control methods may struggle with global stability guarantees in the presence of complex uncertainties.

Purpose of the Study:

• To propose a globally neural-network-based adaptive control strategy for uncertain output feedback systems.
• To address time-varying bounded disturbances and ensure global uniform ultimate boundedness (GUUB) of all closed-loop signals.
• To achieve precise output tracking error convergence to a predefined bound.

Main Methods:

• Development of a globally neural-network-based adaptive control framework.
• Incorporation of a flat-zone modification for enhanced robustness.
• Introduction of a high-order continuously differentiable switching function within filter dynamics for global compensation of uncertain functions.

Main Results:

• Demonstration of global compensation for uncertain functions via the switching function.
• Proof of global uniform ultimate boundedness (GUUB) for all closed-loop signals.
• Verification that the output tracking error converges to a prespecified neighborhood of the origin.

Conclusions:

• The proposed neural-network-based adaptive control strategy effectively handles uncertain output feedback systems with disturbances.
• The flat-zone modification and switching function ensure global stability and accurate tracking.
• Simulation examples confirm the practical effectiveness of the developed control method.