Stabilization of nonlinear nonminimum phase systems: adaptive parallel approach using recurrent fuzzy neural network

Area of Science:

• Control Engineering
• Artificial Intelligence
• Nonlinear System Dynamics

Background:

• Nonminimum phase nonlinear systems present significant control challenges due to inherent instability.
• Traditional control methods often struggle with on-line adaptation and performance optimization in dynamic environments.

Purpose of the Study:

• To propose a novel adaptive parallel control architecture for stabilizing nonminimum phase nonlinear systems.
• To enhance controller performance, disturbance rejection, and the domain of attraction using intelligent tuning mechanisms.

Main Methods:

• Design of a main nonfuzzy controller using backstepping and feedback linearization for nominal systems.
• Integration of a recurrent fuzzy neural network (RFNN) identifier for system sensitivity analysis.
• Incorporation of an RFNN compensator for adaptive fine-tuning of the main controller.

Main Results:

• The proposed architecture demonstrates improved system performance and disturbance rejection capabilities.
• Rigorous Lyapunov-based stability proofs confirm the closed-loop stability of the control system.
• Computer simulations validate the effectiveness and applicability of the adaptive parallel controller.

Conclusions:

• The adaptive parallel control architecture effectively stabilizes nonminimum phase nonlinear systems.
• Recurrent fuzzy neural networks significantly enhance control performance and robustness.
• The proposed method offers a promising approach for advanced control applications.