Robust Fault Detection for Vehicle Lateral Dynamics: A Zonotope-based Set-membership Approach

Abstract : In this work, a model-based fault detection layout for vehicle lateral dynamics system is presented. The major focus in this study is on the handling of model uncertainties and unknown inputs. In fact, the vehicle lateral model is affected by several parameter variations such as longitudinal velocity, cornering stiffnesses coefficients and unknown inputs like wind gust disturbances. Cornering stiffness parameters variation is considered to be unknown but bounded with known compact set. Their effect is addressed by generating intervals for the residuals based on the zonotope representation of all possible values. The developed fault detection procedure has been tested using real driving data acquired from a prototype vehicle.
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Submitted on : Tuesday, December 11, 2018 - 9:13:37 AM
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  • HAL Id : hal-01950541, version 1

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Sara Ifqir, Vicenc Puig, Naïma Ait-Oufroukh, Dalil Ichalal, Saïd Mammar. Robust Fault Detection for Vehicle Lateral Dynamics: A Zonotope-based Set-membership Approach. 21st International Conference on Intelligent Transportation Systems (ITSC 2018), Nov 2018, Maui, Hawaii, United States. ⟨hal-01950541⟩

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