Deterministic Learning-Based Fault Identification for Nonlinear Sampled-Data Systems: Learning Accuracy Analysis.

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Fault Detection and Identification

Background:

• Nonlinear uncertain systems pose challenges for fault identification.
• Existing methods may lack effective performance evaluation metrics.
• Sampled-data systems require specialized identification techniques.

Purpose of the Study:

• To propose a novel sampled-data fault identification (SDFI) scheme for nonlinear uncertain systems.
• To analyze the learning performance of the proposed SDFI algorithm.
• To develop a method for evaluating SDFI performance using measurable signals.

Main Methods:

• Design of a learning-based estimator.
• Modeling the learning system using a sampled-data (SD) linear time-varying (LTV) system.
• Construction of a time-varying symmetric positive definite matrix to derive exponential convergence.
• Establishment of explicit formulas for learning accuracy.

Main Results:

• The exponential convergence property of the SD LTV system was derived.
• Explicit formulas relating learning performance, neural network persistent excitation (PE) level, and system parameters were established.
• The proposed method allows for the evaluation of learning accuracy using measurable parameters.
• Simulations on a robot manipulator and a compressor system demonstrated the method's effectiveness.

Conclusions:

• The developed SDFI scheme provides a robust approach for fault identification in nonlinear uncertain systems.
• The theoretical framework enables practical evaluation of learning performance.
• The method shows significant potential for real-world applications in system monitoring and diagnostics.