Robust Model-Based Fault Detection for a Roll Stability Control System

Fault management is critical for a vehicle active safety system. Since a sensor fault may not always be detectable by a sensor self-test or an electronic monitoring system whose detection often relies on out-of-range signals, a redundancy check is warranted for the detection of an in-range signal fault. In this paper, an in-vehicle roll rate sensor failure detection scheme utilizing analytical redundancy is presented. The vehicle is assumed to be equipped with a steering wheel angle sensor, a yaw rate sensor, a lateral accelerometer, and wheel speed sensors in addition to the roll rate sensor. Due to the wide variation of vehicle dynamics under a vast operating range, such as various and dynamically changing road super-elevations and road grades, the detection of a roll rate signal fault using analytical redundancy is particularly challenging. These challenges, as well as the robustness and performance of the proposed scheme are discussed. The robust performance of the proposed scheme, over model uncertainties and road disturbances, is illustrated analytically and validated through experimental test data. The analytical illustrations include three elements: a robust estimation of the vehicle roll angle, a dynamic compensation of both electrical and kinematics-induced biases in the roll rate signal, and a directionally sensitive design of a robust observer which decouples the model uncertainties and disturbances from the fault. The experimental verifications of no false positive and/or no false negative were taken with a variety of maneuvers and road conditions on several vehicle test platforms