IRI - Robust LPV fault diagnosis using the set-based approach for autonomous ground vehicles

Publication

Robust LPV fault diagnosis using the set-based approach for autonomous ground vehicles

Journal Article (2024)

Journal

IEEE Transactions on Intelligent Transportation Systems

Doc link

https://doi.org/10.1109/TITS.2024.3394518

File

Download the digital copy of the doc pdf document

Authors

Abstract

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.

Author keywords

Fault diagnosis, set invariance approach, set-membership approach, LPV, minimum detectable fault, minimum isolable fault, autonomous ground vehicles

Scientific reference

S. Zhang, V. Puig and S. Ifqir. Robust LPV fault diagnosis using the set-based approach for autonomous ground vehicles. IEEE Transactions on Intelligent Transportation Systems, 2024.