Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance

Area of Science:

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

Background:

• Uncertain nonlinear systems present significant control challenges due to unknown nonlinearities and time-varying disturbances.
• Traditional control methods often struggle with simultaneously addressing both system uncertainty and external disturbances.
• Existing approaches may lack transparency in how intelligent approximation and disturbance estimation are integrated into the control law.

Purpose of the Study:

• To develop a dynamic surface control strategy for uncertain nonlinear systems.
• To integrate composite intelligent learning and a disturbance observer for enhanced estimation and control.
• To ensure transparency in the intelligent approximation and disturbance estimation processes within the control scheme.

Main Methods:

• A serial-parallel estimation model is employed for intelligent approximation and disturbance estimation, generating prediction error.
• A composite law for updating weights is constructed based on the prediction error.
• A nonlinear disturbance observer is designed using intelligent approximation, with stability analyzed via Lyapunov methods.

Main Results:

• The disturbance estimation converges to a bounded set, ensuring reliable disturbance compensation.
• The composite intelligent learning and disturbance observer effectively estimate the impact of system nonlinearity and disturbances.
• The proposed approach demonstrates superior performance and higher accuracy compared to existing methods.

Conclusions:

• The developed dynamic surface control strategy, incorporating composite intelligent learning and a disturbance observer, achieves uniformly ultimate boundedness stability.
• The transparency of the intelligent approximation and disturbance estimation enhances the understanding and reliability of the control system.
• This method offers an effective solution for controlling uncertain nonlinear systems with improved accuracy and performance.