Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonaffine nonlinear systems present significant control challenges due to their complex structure.
• Existing control methods often struggle with unknown nonlinearities and system uncertainties.

Purpose of the Study:

• To develop an observer-based direct adaptive fuzzy-neural control scheme for nonaffine nonlinear systems.
• To address the challenge of unknown nonlinearities in system structures.
• To improve the tracking performance and stability of control systems.

Main Methods:

• Utilized implicit function theorem and Taylor series expansion for control law derivation.
• Developed a direct adaptive fuzzy-neural controller as an alternative to indirect methods.
• Employed strictly-positive-real (SPR) Lyapunov theory for stability analysis.

Main Results:

• Derived observer-based control and weight update laws for nonaffine nonlinear systems.
• Verified the stability of the closed-loop system using SPR Lyapunov theory.
• Demonstrated bounded signals and asymptotic output trajectory tracking through simulations.

Conclusions:

• The proposed observer-based direct adaptive fuzzy-neural control scheme effectively handles nonaffine nonlinear systems with unknown nonlinearities.
• The method guarantees system stability and achieves precise output tracking.
• Simulation results validate the practical applicability and effectiveness of the developed control strategy.