H(infinity) tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach

Area of Science:

• Control Theory
• Artificial Intelligence
• Nonlinear Systems

Background:

• Uncertain nonlinear systems present significant control challenges due to unmodeled dynamics, disturbances, and approximation errors.
• Conventional control methods often require precise system models and struggle with robustness.

Purpose of the Study:

• To propose a novel adaptive fuzzy-neural sliding-mode controller (AFNSMC) for uncertain nonlinear systems.
• To achieve robust H(infinity) tracking performance while attenuating the effects of uncertainties.
• To ensure closed-loop stability under relaxed assumptions.

Main Methods:

• Utilizing adaptive fuzzy-neural systems for approximating uncertain nonlinear functions.
• Integrating H(infinity) tracking design techniques for guaranteed performance bounds.
• Incorporating sliding-mode control (SMC) for robustness against uncertainties and disturbances.

Main Results:

• The proposed AFNSMC effectively approximates system nonlinearities.
• Robustness against unmodeled dynamics, disturbances, and approximation errors is demonstrated.
• Closed-loop stability and guaranteed H(infinity) tracking performance are achieved.
• Significant reduction in control input chattering observed.

Conclusions:

• The novel AFNSMC offers superior performance for uncertain nonlinear systems compared to conventional methods.
• The approach relaxes the need for prior knowledge of uncertainty bounds, enhancing practical applicability.
• The controller effectively attenuates lumped uncertainties and reduces control chattering.