Globally Adaptive Neural Network Tracking for Uncertain Output-Feedback Systems

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• Global tracking control is crucial for uncertain nonlinear systems.
• Existing neural network (NN) control methods face challenges with unknown disturbance bounds and unmeasured states.
• Output feedback control is essential for practical applications where all states are not measurable.

Purpose of the Study:

• To develop a robust global neural network (NN) tracking control strategy for uncertain nonlinear systems with unknown disturbance bounds.
• To address challenges posed by unmeasured states and system uncertainties in output feedback control.
• To ensure the globally uniformly ultimately bounded stability of all closed-loop signals.

Main Methods:

• A novel controller combining a neural network (NN) approximator and a robust component.
• A smooth switching function to transition between the NN and robust controllers.
• Lyapunov stability analysis to rigorously prove system stability and error convergence.

Main Results:

• The proposed control scheme guarantees global uniform ultimate boundedness for all closed-loop signals.
• Output tracking errors are shown to converge to an arbitrarily small neighborhood.
• The effectiveness of the control strategy is validated through numerical and practical examples.

Conclusions:

• The developed NN tracking control method offers a robust solution for uncertain nonlinear systems under disturbances.
• The smooth switching mechanism enhances stability and performance.
• The approach provides a reliable framework for achieving precise tracking control in complex systems.