A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems

Area of Science:

• Control Engineering
• Nonlinear Systems
• Artificial Intelligence

Background:

• Strict-feedback nonlinear systems present significant control challenges.
• Conventional backstepping methods suffer from "explosion of complexity".
• Adaptive control schemes often face controller singularity issues.

Purpose of the Study:

• To develop a robust adaptive tracking control approach for strict-feedback nonlinear systems.
• To overcome limitations of existing control methods, including complexity and singularity.
• To simplify the implementation of adaptive control algorithms.

Main Methods:

• Utilizing radial-basis-function neural networks for uncertainty approximation.
• Combining dynamic surface control and minimal learning parameter techniques.
• Employing input-to-state stability theory and small gain approach for stability analysis.

Main Results:

• The proposed scheme avoids the "explosion of complexity" associated with backstepping.
• The number of online updated parameters per subsystem is reduced to two.
• Controller singularity problems are eliminated, simplifying the adaptive control algorithm.
• All closed-loop system signals are proven to be semiglobal uniformly ultimately bounded.

Conclusions:

• The presented adaptive tracking control is robust and effective for strict-feedback nonlinear systems.
• The method offers a simpler, more practical alternative to conventional techniques.
• Simulation examples validate the effectiveness of the proposed control scheme.