Abstract
<div class="section abstract"><div class="htmlview paragraph">A redundant system refers to a system that operates identical unit systems simultaneously to enhance robustness to fault. In particular, considering system complexity, a redundant system consisting of two identical unit systems is widely used. However, dual-system redundancy can detect the presence of malfunction when the outputs of the two unit systems differ, but it is challenging to identify the normally functioning unit system. Therefore, the functionality can degrade or be interrupted even when a normally operating unit system is present. Hence, research is actively ongoing to address the challenge of identifying the normally functioning unit system. This study proposes an algorithm to identify the normally operating sensor in the event of a steering angle sensor fault in a redundant Electronic Power Steering (EPS) system. In this paper, an Extended Kalman Filter is designed based on the Bicycle model of vehicle dynamics to estimate the steering angle of the steering wheel. Real-time driving data for estimation is acquired through CAN communication inside the vehicle. By comparing estimated values with actual sensor outputs, the algorithm discern sensor faults from normal operation and maintain the steering assist function when a normally functioning sensor is in present. The proposed steering angle estimation algorithm and failure determination algorithm were verified with driving data obtained from an actual vehicle. To evaluate the algorithm, a disturbance was applied to one sensor to simulate a failure. The experimental results demonstrated that the steering assistance function is maintained even when one sensor malfunctions.</div></div>