Robust LPV Fault Diagnosis Using the Set-Based Approach for Autonomous Ground Vehicles

This paper proposes a robust fault diagnosis method for Autonomous Ground Vehicles (AGVs) modeled as a Linear Parameter Varying (LPV) system with bounded uncertainties. The proposed approach combines the zonotope-based Set-Membership Approach (SMA) and Set Invariance Approach (SIA). Firstly, an onli...

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Detalles Bibliográficos
Autores: Zhang, Shuang, Puig, Vicenç, Ifqir, Sara
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/377665
Acceso en línea:http://hdl.handle.net/10261/377665
https://api.elsevier.com/content/abstract/scopus_id/85192744858
Access Level:acceso abierto
Palabra clave:Autonomous ground vehicles
Fault diagnosis
LPV
Minimum detectable fault
Minimum isolable fault
Set invariance approach
Set-membership approach
Descripción
Sumario:This paper proposes a robust fault diagnosis method for Autonomous Ground Vehicles (AGVs) modeled as a Linear Parameter Varying (LPV) system with bounded uncertainties. The proposed approach combines the zonotope-based Set-Membership Approach (SMA) and Set Invariance Approach (SIA). Firstly, an online fault detection strategy based on zonotopic set-membership state estimation is introduced, where the optimal observer gain is calculated offline by solving LMI optimization problems. To characterize the Minimum Detectable Fault (MDF) and Minimum Isolable Fault (MIF), the invariant residual sets are first obtained for the system operated in healthy and faulty modes. The proposed method relies on the propagation of the zonotopic state estimation error in steady state based on SIA. Then, MDF and MIF are characterized for several types of faults by solving optimization problems subject to set separation conditions. Finally, experiment validations using a prototype vehicle are performed to illustrate the effectiveness of the proposed approach.