Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear interconnected systems present significant control challenges due to uncertainties.
• Achieving rapid and guaranteed convergence times is crucial for many industrial processes.
• Existing control methods may struggle with unknown dynamics and fixed-time requirements.

Purpose of the Study:

• To propose a novel adaptive fixed-time neural network tracking control scheme.
• To address unknown system uncertainties within a fixed-time framework.
• To provide a robust control solution for nonlinear interconnected systems.

Main Methods:

• Utilizing an adaptive backstepping technique to handle unknown system uncertainties.
• Employing neural networks for real-time identification of these uncertainties.
• Applying Lyapunov stability analysis to guarantee fixed-time convergence.

Main Results:

• Demonstrated that all system states converge to small regions around zero.
• Achieved convergence within a fixed-time, outperforming traditional methods.
• Validated the control scheme's effectiveness through simulation examples.

Conclusions:

• The proposed adaptive fixed-time neural network control scheme is effective for nonlinear systems.
• The method ensures rapid and predictable convergence for system states.
• A step-by-step procedure is provided for practical implementation in industry.