Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems with unmodeled dynamics and nonlower triangular structures present significant control challenges.
• Existing control methods often struggle with simultaneous uncertainties and complex system configurations.

Purpose of the Study:

• To develop an adaptive neural control strategy for nonlower triangular nonlinear systems.
• To address challenges posed by unmodeled dynamics and dynamic disturbances.
• To ensure stability and accurate trajectory tracking for complex systems.

Main Methods:

• A dynamic signal is employed to manage unmodeled dynamics.
• A variable partition technique is utilized for nonlinear function handling.
• Neural networks are integrated for adaptive tracking control.

Main Results:

• The proposed controller guarantees semi-global boundedness of all closed-loop system signals.
• System output converges to a small neighborhood of the desired trajectories.
• Simulation results confirm the effectiveness of the developed techniques.

Conclusions:

• The research presents a feasible neural network-based tracking control for uncertain nonlinear systems.
• The adaptive controller successfully handles unmodeled dynamics and nonlower triangular forms.
• The study demonstrates a robust approach to complex control problems.