Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear dynamical systems often suffer from unmodeled dynamics, external disturbances, and internal modeling errors.
• Existing control strategies may struggle to maintain stability and performance under such uncertainties.
• Adaptive and robust control techniques are crucial for enhancing the reliability of control systems.

Purpose of the Study:

• To propose a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems.
• To address and mitigate the impacts of unmodeled dynamics, disturbances, and modeling errors.
• To ensure system states remain within specified bounds and prevent parameter drift.

Main Methods:

• A generalized projection update law was developed, integrating projection algorithm modification and switching-sigma adaptive laws.
• A variable structure control method was incorporated into the fuzzy-neural control architecture.
• Adjustable parameters were tuned to prevent parameter drift and confine system states.

Main Results:

• The proposed fuzzy-neural control scheme demonstrated robustness against unmodeled dynamics, disturbances, and modeling errors.
• The generalized projection update law effectively prevented parameter drift.
• System states were successfully confined to specified regions, ensuring stability.
• Simulations and examples confirmed the effectiveness of the developed control strategy.

Conclusions:

• The robust adaptive fuzzy-neural control scheme offers a reliable solution for controlling nonlinear dynamical systems with uncertainties.
• The integration of generalized projection update laws and variable structure control enhances controller robustness and parameter convergence.
• The proposed method provides a significant advancement in adaptive and robust control for complex systems.