Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function.

Area of Science:

• Control Theory
• Nonlinear Systems
• Neural Networks

Background:

• Adaptive neural control is crucial for systems with unknown dynamics.
• Output feedback control presents challenges due to limited information.
• Existing methods struggle with ensuring neural network approximation validity and boundedness.

Purpose of the Study:

• To develop an adaptive neural control strategy for nonlinear systems with unknown functions using only output feedback.
• To address the challenge of determining valid approximation sets for neural networks.
• To ensure the boundedness of system signals and tracking error convergence.

Main Methods:

• Utilized on-line neural network (NN) control with output measurements.
• Introduced a barrier Lyapunov function (BLF) to manage unknown function arguments.
• Ensured NN approximation validity by constraining function arguments within a compact superset via BLF boundedness.

Main Results:

• Achieved semiglobal boundedness of all closed-loop signals.
• Demonstrated convergence of the tracking error to a neighborhood of zero.
• Validated the proposed approach's effectiveness through simulations.

Conclusions:

• The proposed barrier Lyapunov function-based adaptive neural control effectively handles unknown functions in output feedback nonlinear systems.
• The method ensures signal boundedness and accurate tracking, overcoming key challenges in neuro-control.
• Simulation results confirm the practical efficacy of this adaptive control strategy.