Data-Driven Kalman Filtering in Nonlinear Systems with Actuator and Sensor Fault Diagnosis Based on Lyapunov Stability
This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f7112f50248744bc86042a8e5cbd84af |
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| Sumario: | This study proposes a data-driven adaptive filtering method for the fault diagnosis (DDAF-FD) of discrete-time nonlinear systems and provides a simultaneous online estimation of actuator and sensor faults. First, dynamic linearization was adopted to transform the nonlinear system into a quasi-linear model, which facilitated accurate modeling of the nonlinear system. Second, a data-driven adaptive fault diagnosis method was designed under the framework of data-driven filtering and the recursive least-squares algorithm using system I/O data only, and accurate real-time estimation of two fault factors was achieved. In addition, the simulation results demonstrate the effectiveness of the proposed method. The stability was verified via the Lyapunov method. |
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