Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

Area of Science:

• Control Theory
• Nonlinear Systems
• Neural Networks

Background:

• Perturbed strict feedback nonlinear systems present significant control challenges due to unknown parameters.
• Existing robust adaptive control methods often require prior knowledge of control coefficients or specific nonlinearity structures.

Purpose of the Study:

• To develop a robust adaptive neural control design for nonlinear systems with unknown virtual control coefficients and nonlinearities.
• To extend the applicability of robust adaptive control to a broader class of nonlinear systems.

Main Methods:

• Utilized iterative Lyapunov design combined with neural network approximations.
• Incorporated leakage terms in adaptive laws to mitigate neural network approximation errors.
• Addressed nonlinearities satisfying the triangularity condition and those with parametric uncertainties and known bounding functions.

Main Results:

• The proposed design guarantees uniform ultimate boundedness of closed-loop system signals.
• The control scheme effectively handles completely unknown virtual control coefficients and nonlinearities.
• Demonstrated improved robustness and performance through simulation studies.

Conclusions:

• The developed robust adaptive neural control is effective for perturbed nonlinear systems.
• The method enhances control design possibilities by not requiring knowledge of coefficient signs.
• The approach offers reliable performance and stability guarantees for complex systems.