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This paper presents a differential braking strategy for vehicle stability control. The controller has been designed using a three-degree-of-freedom (3DOF) yaw plane vehicle model, and a simulation study has been performed using a full non-linear three-dimensional vehicle model. The brake control inputs have been directly derived from the sliding control law based on a 3DOF yaw plane vehicle model with differential braking. The performance of the sliding controller has been compared with that of the direct yaw moment controller (DYC). Simulation results have shown that the proposed controller can provide a vehicle with superior performance with respect to brake actuation and system smoothness and can minimize the acceleration and jerk without compromising stability at high speed and large steering angle input.

SUMMARYAn adaptive method based on the modiÿed bang-bang control algorithm is proposed for the vibration control of structures subjected to unexpected severe seismic loads greater than the design loads. A hydraulic-type active mass damper was made and experiments were carried out in the laboratory using a one-story test structure and a ÿve-story test structure with the active mass damper. Through numerical simulations and experiments it was conÿrmed that the proposed method works well to suppress the vibration of structures subjected to unexpected severe seismic loads greater than the design loads without causing any unstable situations.

This paper describes a method of lane map building and localization for automated driving using 2d laser rangefinder. Today s on-board sensors such as radar or camera do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support these systems. An digital map is used as a powerful additional sensor. So we propose a lane map-based localization using a 2D Laser Rangefinder. The maps are created beforehand using a 2D LiDAR and RTK GPS. A pose estimation of vehicle was derived from a low-cost GPS and an iterative closest point(ICP) match of real-time sensor data to lane map. And the estimated pose was used as an observation inside a Kalman filter framework. The performance of the proposed localization algorithm is verified via vehicle tests in ITS proving ground. It has been shown through vehicle tests that good localization performance can be obtained. The proposed algorithm will be useful in the implementation of automated driving.

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