Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered...

Area of Science:

• Control Theory
• Nonlinear Systems
• Artificial Intelligence

Background:

• High-order nonlinear systems present significant control challenges due to uncertainties.
• Input saturation limits controller performance and stability.
• Existing methods often struggle with finite-time convergence and system uncertainties.

Purpose of the Study:

• To develop a robust finite-time tracking control strategy for uncertain high-order nonlinear systems.
• To effectively handle input saturation using advanced control techniques.
• To ensure rapid convergence of tracking errors and signal boundedness.

Main Methods:

• A finite-time control strategy is proposed, integrating a neural state observer.
• Command filtered backstepping is employed, utilizing a finite-time command filter (FTCF).
• Fraction power-based error compensation and an auxiliary system address filtering errors and input saturation.

Main Results:

• The designed controller achieves finite-time convergence of the output tracking error to a specified neighborhood of the origin.
• All signals within the closed-loop system are demonstrated to be bounded within a finite time.
• Simulation examples validate the effectiveness of the proposed control strategy.

Conclusions:

• The proposed finite-time control strategy effectively manages uncertainties and input saturation in high-order nonlinear systems.
• The combination of neural networks, FTCF, and error compensation ensures robust and efficient tracking performance.
• The approach offers a promising solution for real-world applications requiring precise and fast control.