Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback...

Area of Science:

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

Background:

• The dynamic surface control (DSC) technique simplifies backstepping design for nonlinear systems.
• Existing DSC methods struggle with linearly parameterized uncertainty.
• The
• explosion of complexity
• remains a challenge in backstepping control.

Purpose of the Study:

• To develop a novel backstepping-based adaptive control design for nonlinear systems with arbitrary uncertainty.
• To integrate dynamic surface control (DSC) with neural network adaptive control.
• To overcome the
• explosion of complexity
• issue in nonlinear adaptive control.

Main Methods:

• Incorporation of dynamic surface control (DSC) into a neural network adaptive control framework.
• Development of a backstepping-based control design for nonlinear systems in strict-feedback form.
• Utilizing neural networks to handle arbitrary system uncertainty.

Main Results:

• A new control design effectively eliminates the
• explosion of complexity
• problem.
• The proposed control law guarantees uniformly ultimate boundedness of the closed-loop system.
• Achieved arbitrarily small tracking errors in nonlinear systems with arbitrary uncertainty.

Conclusions:

• The developed neural network-based adaptive control design offers a robust solution for complex nonlinear systems.
• The method successfully addresses arbitrary uncertainty and ensures system stability.
• This approach advances the field of adaptive control for nonlinear systems.