Disturbance-observer-based adaptive command filtered control for uncertain nonlinear systems.

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Adaptive Control

Background:

• Uncertain nonlinear systems present significant challenges in control design due to parametric uncertainties and external disturbances.
• Existing control strategies often struggle with simultaneous compensation of matched/mismatched disturbances and parameter variations, leading to performance degradation.
• The 'explosion of complexity' is a common issue in adaptive control for high-order systems, limiting practical applicability.

Purpose of the Study:

• To develop an asymptotic adaptive command filtered control strategy for uncertain nonlinear systems.
• To effectively compensate for parametric uncertainties, matched disturbances, and mismatched disturbances.
• To avoid the 'explosion of complexity' while ensuring asymptotic tracking performance.

Main Methods:

• A disturbance observer (DO) with a single tuning parameter is employed for disturbance compensation.
• Composite updated laws are utilized to address parameter uncertainties.
• A novel command filtered controller integrating DO, adaptive control, and adaptive-gain auxiliary systems is designed.

Main Results:

• The proposed control strategy achieves asymptotic tracking for uncertain nonlinear systems.
• The controller effectively compensates for both parametric uncertainties and disturbances.
• System stability is rigorously proven using Lyapunov functions, and experimental results validate the strategy's effectiveness.

Conclusions:

• The developed asymptotic adaptive command filtered control approach offers a robust solution for uncertain nonlinear systems.
• The integration of disturbance observation and adaptive control with command filtering successfully mitigates complexity and ensures performance.
• The strategy demonstrates superior performance and stability, validated through extensive experimental evidence.