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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.

Wheel-slip control systems are able to control the braking force more accurately and can be adapted to different vehicles more easily than conventional ABS systems. But, in order to achieve the superior braking performance, real-time information about the vehicle status variables such as wheel slip ratio, tire force, etc is required. In this paper, a wheel slip controller based on the estimated braking force is developed for brake-by-wire systems. The proposed wheel slip control system is composed of the braking force monitor and robust slip controller. In the brake force monitor, the tire braking forces as well as the brake disk-pad friction coefficient are estimated. The robust wheel slip controller using the estimated tire braking force is designed based on the sliding mode control technique. This system determines the braking pressure as the control input and maintains the wheel slip at any given target slip.

This paper proposes a control and fault diagnosis method for a pressure sensor based brake control system. The proposed wheel brake pressure control method consists of feedforward and feedback controller, respectively. The main purpose of the feedforward controller is to set the operating point of the feedback control, and the purpose of feedback controller is to improve the control response and the steady state error characteristic. Also, the proposed fault diagnosis method consists of three processes: a fault detection process, a fault isolation process and a fault identification process. In the fault detection process, a fault is detected by the difference between the estimated signal and the measured signal. Then, in the fault isolation process, the location of the fault is determined. Finally, in the identification process, the size and effect of the fault are evaluated.

The introduction of inexpensive cylinder pressure sensors provides new opportunities for precise engine control. This paper presents a control strategy of spark advance and air-fuel ratio based upon cylinder pressure for spark ignition engines. In order to extend the cylinder pressure based engine control to a wide range of engine speeds, the appropriate choice of control parameters is important as well as essential. For this control scheme, peak pressure and its location for each cylinder during every engine cycle are the major parameters for controlling the air-fuel ratio and spark timing. However, the conventional method requires the measurement of cylinder pressure at every crank angle degree to determine the peak pressure and its location. In this study, the peak pressure and its location were estimated, using a multi-layer feedforward neural network, which needs only five cylinder pressure samples at -40°, -20°, 0°, 20°, and 40° after TDC.

This paper presents a longitudinal control algorithm for an adaptive cruise control (ACC) with collision avoidance (CA) in multiple vehicle traffic situations. The proposed algorithm consists of a multi-target tracking filter, a primary target selection algorithm and an integrated ACC/CA system. The multi-target tracking filter is used to smooth the sensor signal, and makes it possible to apply to a control system. The primary target selection algorithm decides an in-lane target and provides the information to an integrated ACC/CA system in order to drive a subject vehicle smoothly and improve safety in complex traffic situations. Finally, the integrated ACC/CA system computes the desired acceleration. The performance and safety benefits of the multi-vehicle ACC/CA system is investigated via simulations using real data on driving. Simulation results show that the response of multi-vehicle ACC/CA system is more smooth and safer at a change of traffic situations.