Model-based sensor fault detection and isolation method for a vehicle dynamics control system

Abstract

Conventional vehicle electronic stability control requires one steering-wheel angle sensor, one lateral acceleration sensor and one yaw rate sensor to obtain a good control performance. The control system stops working when a sensor fault is detected, which means that the vehicle runs in an unprotected state. Thus, various sensor fault diagnosis algorithms have been designed to detect and isolate the faulty sensor, but these algorithms also can be used for fault-tolerant control to preserve the safety of the vehicle. However, determining which of the different sensors is faulty is very difficult as the conventional residual comparison algorithm can only find the existence of a sensor fault but cannot locate the faulty sensor, and very few research studies have focused on this problem. In this paper, an ingenious sensor fault diagnosis algorithm is proposed. The sensor fault is detected, located and isolated by cross-checking with three different yaw rate estimates. The steering-wheel angle observer and the lateral acceleration observer are designed to provide corresponding estimated sensor signals which are employed to estimate the different yaw rates by using an extended Kalman filter. A novel decision-making process is carefully designed to locate the faulty sensor based on the different yaw rate residuals. Electronic stability control is not interrupted as the faulty sensor signal is reconfigured by the estimated signal. Experimental tests on a real car show that the proposed algorithm is efficient for detecting the sensor fault and identifying which sensor is faulty. Simulations show that the vehicle stability control strategy based on the proposed sensor fault-tolerant control algorithm has a better performance than the traditional control strategy does.