Control of Automotive AirSpring Suspension System Using ZNumber Based Fuzzy System This paper presents the control of an automotive airspring suspension system using a Znumber based fuzzy inference system. One of automotive suspension functions is to isolate vehicle body from road disturbance which is described by transmissibility coefficient. There is a conflict between ride comfort and handling requirements. Lower transmissibility coefficient results in improving passengersxe2x80x99 comfort. In this study, an airspring suspension system is connected to two additional volumes of different sizes via ONOFF valves that are controlled by the proposed control strategy. The stiffness of the suspension system is the control parameter that is controlled by changing the enclosed pressure and the total air volume inside the airspring within one of four chosen levels. The proposed Znumber control scheme is characterized by two components; constraint and reliability. The interpolating reasoning of the Znumber fuzzy is constructed and applied. Genetic Algorithm is used to estimate the optimal control gains and switching levels of the air volumes. The proposed control system is applied to a simulated quarter car model to evaluate system performance. Then, the proposed controller is compared to a conventional fuzzy controller and a fuzzy controller merged with discrete volume control. The results show that the proposed controller improves the air suspension performance compared to the other controllers with a maximum acceleration of 1.1 cms2.

CloudBased Vehicle RideHeight Control We present a novel approach for a cloudbased rideheight control for vehicles equipped with an air suspension. The objective of this approach is to improve both efficiency and comfort, especially on single obstacles, by including vehicletovehicle or vehicletoinfrastructure (V2X) information on the road ahead in the control algorithm. The focus of this paper is the methodology of data processing on a cloud backend and includes three steps: preprocessing, clustering and allocation of streets to the clusters. In the first step, the database is reduced to obstacles relevant for driving comfort. The second step is to find clusters with a high density of obstacles on a road condition map. Finally, the probability of hitting an obstacle is calculated for each road in the area of a cluster, taking the characteristics and the topology of the road network into account. Example data is used to proof the functionality of the method. The proposed method seems to be a suitable approach for big data applications and might improve a vehicle rideheight control with regard to comfort and efficiency.