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Electric power steering (EPS) systems utilize an electric motor to assist the driver’s steering efforts. Recently, EPS is drawing attention as a mechanism for improving vehicle stability and maneuverability. This paper proposes a control method using EPS to improve steering maneuverability when driving on rutted roads. The disturbance torque generated by the grooves in rutted roads affects maneuverability. Conventional EPS cannot discriminate between torque originating from driver input and aligning torque generated by the road surface. Thus, it is difficult for conventional EPS to reduce the unstable effect caused by ruts without affecting the driver’s steering feel. The method proposed here detects the amount of disturbance torque caused by the ruts in the road, and only when disturbance torque is detected, the EPS system compensates accordingly. As a result, steering pull is reduced without interfering with the driver’s steering feel.

In this paper, we suggest a novel algorithm to distinguish semi-steady states from various steering patterns and to estimate the stability factor. The algorithm also estimates each stability factor in left and right turns because there could be a case where they differ based on uneven tire wear and so on. The stability factor, which is the turning characteristic of a vehicle, has been treated as constant for most vehicle control systems. However, in fact, it may change in some situations, for example when a vehicle is overloaded. So there is a chance that a driver may be aware of an unusual sensation when vehicle control is designed based on a constant stability factor. We have succeeded in developing an algorithm to estimate the stability factor accurately enough to be able to compensate for it and have confirmed the effectiveness of the algorithm by simulation and vehicle testing as well.

In this paper the vehicle body side slip angle (VBSSA) is determined by means of non-linear state space observers. First, an adaptive non-linear double track model is presented. Validation with real measurement data shows that the model accuracy is sufficient for observer design. On basis of this model two observers are derived. One observer is based on a linearization of the vehicle model around the currently estimated state vector. The other observer adapts the dynamics of the non-linear estimation error to the one of a linear reference model. As this observer is restricted to systems of a specific structure, the adaptive non-linear double track model has to be restructured accordingly. The presented observers are validated with real measurement data. They provide an accurate estimation of the VBSSA up to the stability limit of the vehicle.

The reproduction of the vehicle motion is a crucial element of accident reconstruction. Apart from the position of the center of gravity in an inertial coordinate system, the vehicle heading plays an important role. The heading is the sum of the yaw angle and the vehicle body side slip angle. In standard vehicles, the yaw angle can be determined using the yaw rate sensor and the wheel speeds. However, the yaw rate sensor is often subject to temperature drift. The wheel speed signals are forged at low speeds or due to slip. These errors result in significant deviations of reconstructed and real vehicle heading. Therefore, an intelligent combination of these signals is required. This paper describes a fuzzy system which is capable to increase the accuracy of yaw angle calculation by means of fuzzy logic. Before the data is applied to the fuzzy system, it is preprocessed to ensure the accuracy of the fuzzy system inputs.