Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence in Control
• Nonlinear System Dynamics

Background:

• Nonlinear systems with nonstrict-feedback structures present significant control challenges.
• Actuator faults can degrade system performance and stability.
• Existing control methods often struggle with unknown system nonlinearities and state estimation.

Purpose of the Study:

• To develop an output-based adaptive fault-tolerant control strategy for nonlinear systems with nonstrict-feedback forms.
• To address unknown system nonlinearities and unavailable states.
• To ensure robust tracking performance in the presence of actuator faults.

Main Methods:

• Utilizing neural networks for approximating unknown nonlinear system dynamics.
• Employing an observer for state estimation and a fault model for fault description.
• Applying dynamic surface control and adaptive backstepping techniques.
• Designing an output-based adaptive neural tracking control strategy.

Main Results:

• All signals in the closed-loop system are proven to be bounded.
• The system output signal achieves tracking of the reference signal with a small error.
• The proposed control strategy effectively handles actuator faults in nonlinear systems.
• Simulation results validate the control strategy's effectiveness.

Conclusions:

• The developed output-based adaptive neural control strategy is effective for nonlinear systems with nonstrict-feedback and actuator faults.
• The approach ensures system stability and accurate tracking performance.
• Neural networks and adaptive techniques provide a robust solution for complex control problems.