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A fault detection method with parity equations is proposed in this paper. Due to low cost implementation, the velocity of a motor is not measurable in EPB systems. Therefore, residuals are not reliable with a low resolution encoder to estimate the motor velocity. In this paper, we propose a fault detection method with sensorless estimation using current ripples. The method estimates position and velocity of the motor by detecting periodical oscillations of the armature current caused by rotor slots. This method could estimate position and velocity of the motor with less computational effort than a state observer. Moreover, the method is less sensitive to motor parameters than model-based estimation methods. The effectiveness of this method is validated with experimental data. The simulation results show that various faults have their own residual patterns. Therefore, we could detect the fault by monitoring the residual signals.

Lane keeping assistant system (LKAS) is expected to reduce the driver workload with assisting the driver during driving and is regarded as a promising active safety system. For the proposed LKAS which requires cooperative driving between driver and the assistance system, a Model Based Predictive Controller (MBPC) is proposed to minimize the effect of system overshoot caused by the time delay from the vision-based lane detection system. In order to validate the proposed LKAS controller, a HIL (Hardware In the Loop) simulator is built using steering mechanism, single camera, torque motor, sensors, etc. The performance of the proposed system is demonstrated in various roadways.