Cloud-based adaptive semi-active suspension control for improving driving comfort and road holding

Abstract

The improvement of driving comfort and vehicle stability performance is essential for the vehicles, which can be actualized by adaptive semi-active suspension control. Cloud computing allows several features for autonomous vehicles. Implementing the adaptive suspension control using historical road data gathered in the cloud database is one of these features. This paper deals with the adaptive semi-active suspension control from the perspective of a Vehicle-to-Cloud-to-Vehicle integration. Measured and historical performance(vertical acceleration and tire deformation) and velocity data in different locations and road irregularities from other vehicles have been stored in the cloud database and used to design the dedicated scheduling variable. The novelty of this paper is developing the adaptive semi-active suspension control method with different scheduling parameter design approaches based on cloud application for the road adaptation capabilities of the suspension system. The control architecture is founded on the Linear Parameter-Varying framework, where the scheduling variable allows the trade-off between driving comfort and vehicle stability. The real data simulation demonstrates the operation of the introduced method in the TruckSim simulation environment and Matlab/Simulink. The results show that both vehicle stability and driving comfort has been improved.