This study involves a layered vehicle dynamics control system, which is composed of an adaptive optimal control allocation method using Lagrangian neural networks for optimal distribution of tyre forces and the sliding mode yaw moment observer for robust control of yaw dynamics. The proposed optimal control allocation method eliminates the requirement of solving optimization problem in every time step and it is a convergent and stability guaranteed solution for the optimal tyre force distribution problem. The aim in the sliding mode yaw moment observer is to force the vehicle to track a reference vehicle dynamic behaviour by estimating the equivalent input extended disturbance, which is the required stabilizing virtual yaw moment. The proposed layered stability control scheme has been tested on a four-wheel drive-four-wheel steer electric Fiat Doblo Van, which is modelled in CarSim. Both the sliding mode disturbance observer and the optimal control allocation methods are the first known applications to the stability control problem of road vehicles.

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According to traffic accident data, the majority of severe road accidents occur at night. It is, therefore, of great importance to use available technology to contribute to road safety by improving the visual conditions provided by vehicle headlights. This paper presents the hardware in the loop simulation of an Adaptive Headlight System for motor vehicles. The Adaptive Headlight System is an active safety system, where the headlamp orientation control system rotates the right and left low beam headlights independently and keeps the beam as parallel to the curved road as possible to provide better night time visibility. In the paper, the real time vehicle and road models used are presented first. The hardware-in-theloop simulation setup proposed for testing the adaptive headlight concept is then given. Real time simulations using this simulator are used to illustrate the approach.