Predefined-Time Adaptive Neural Tracking Control of Switched Nonlinear Systems

Area of Science:

• Control Theory
• Artificial Intelligence
• Nonlinear Systems

Background:

• Switched nonlinear systems present challenges in control due to their complex dynamics.
• Achieving precise tracking control within a finite, predetermined time is crucial for many applications.
• Existing methods may struggle with unknown system nonlinearities and arbitrary switching behaviors.

Purpose of the Study:

• To develop a neural-network-based adaptive controller for predefined-time tracking in switched nonlinear systems.
• To address the challenge of unknown nonlinear functions within the system dynamics.
• To guarantee boundedness of system states and convergence of tracking errors within a specified time.

Main Methods:

• Employing neural networks for approximating unknown nonlinear functions.
• Utilizing finite-time differentiators for virtual controller derivative estimation.
• Applying backstepping control techniques combined with the common Lyapunov function (CLF) method.
• Developing a novel adaptive predefined-time control strategy.

Main Results:

• The proposed controller ensures all signals in the switched closed-loop system remain bounded under arbitrary switchings.
• The tracking error is theoretically proven to converge to zero within the predefined time.
• Simulation results validate the effectiveness of the developed predefined-time control approach.

Conclusions:

• The neural-network-based adaptive predefined-time controller is effective for switched nonlinear systems.
• The controller achieves robust tracking performance with guaranteed convergence time.
• This approach offers a promising solution for precise control of complex dynamic systems.