Predefined-time adaptive learning control of nonlinear strict-feedback systems via dynamic regressor extension and...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Adaptive Control Theory

Background:

• Nonlinear strict-feedback systems often require persistent excitation for parameter identification.
• Traditional backstepping methods can suffer from singular terms, complicating control design.
• Achieving control objectives within a predefined time is a significant challenge in adaptive control.

Purpose of the Study:

• To develop a parameter identification algorithm for nonlinear systems under model uncertainties.
• To propose a novel adaptive tracking control strategy using predefined-time convergence.
• To address limitations of conventional methods, including persistent excitation conditions and singular terms.

Main Methods:

• A three-layer transformation-based parameter estimation method with predefined-time convergence.
• Utilization of a hyperbolic tangent function to design new control laws and filters, avoiding singular terms.
• A composite learning control approach integrating parameter identification with adaptive dynamic surface control.

Main Results:

• The proposed parameter estimation method relaxes the strict persistent excitation condition.
• Control laws and filters are designed to circumvent singular terms inherent in backstepping.
• The composite learning approach ensures error convergence within a practical predefined time.
• Lyapunov analysis confirms semi-global uniformly predefined-time boundedness of the closed-loop system.

Conclusions:

• The developed control scheme is effective for nonlinear strict-feedback systems with model uncertainties.
• The approach achieves predefined-time convergence for tracking errors and system states.
• Numerical simulations validate the performance and robustness of the proposed control strategy.