Approximation-based adaptive fixed-time tracking control for uncertain high-order nonlinear systems subject to...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence in Control

Background:

• High-order nonlinear systems often exhibit complex dynamics and uncertainties.
• Existing fixed-time tracking control (FTTC) methods struggle with time-varying parameters and unknown input nonlinearities.

Purpose of the Study:

• To develop an adaptive neural network (NN) based FTTC scheme for uncertain high-order nonlinear systems.
• To address challenges posed by time-varying parameters and unknown input nonlinearity.
• To achieve fixed-time convergence of tracking errors.

Main Methods:

• Utilized neural network (NN) approximation for system modeling.
• Applied adaptive control and fixed-time control theory.
• Incorporated backstepping control and Nussbaum gain function (NGF) techniques.
• Developed adaptive control laws for parameter estimation.

Main Results:

• The proposed FTTC scheme guarantees tracking error convergence to a small neighborhood of zero within a fixed time.
• All signals within the closed-loop system remain bounded.
• The strategy effectively handles unknown control gain due to input nonlinearity.

Conclusions:

• The adaptive NN-based FTTC strategy provides a robust solution for uncertain nonlinear systems.
• The method demonstrates effectiveness in achieving precise tracking under challenging conditions.
• Simulation results validate the theoretical performance of the proposed control scheme.