Decentralized Adaptive Output Feedback Fault Detection and Control for Uncertain Nonlinear Interconnected Systems.

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Fault Detection and Diagnosis

Background:

• Addressing challenges in decentralized adaptive control for uncertain nonlinear interconnected systems.
• Need for robust fault detection and state estimation in complex dynamic systems.
• Mitigating the impact of measurement noise on system performance.

Purpose of the Study:

• To develop a decentralized adaptive output feedback fault detection and control strategy.
• To estimate unmeasured state variables and attenuate measurement noise.
• To ensure the boundedness of all signals in the closed-loop system.

Main Methods:

• Design of K-filters for state estimation.
• Incorporation of noise dampening filters.
• Development of fault detection schemes using residual and threshold signals.
• Application of backstepping design and neural network approximation for control.
• Lyapunov stability theory for rigorous analysis.

Main Results:

• Successful estimation of unmeasured system states.
• Effective attenuation of measurement noise.
• Proposed fault detection scheme with designed residual and threshold signals.
• Development of decentralized switched control strategies.
• Strict proof of closed-loop system signal boundedness via Lyapunov stability theory.

Conclusions:

• The proposed decentralized adaptive output feedback fault detection and control strategy is effective for uncertain nonlinear interconnected systems.
• The methods ensure system stability and performance in the presence of uncertainties and noise.
• Simulation results validate the theoretical findings.