H-infinity Filtering for Cloud-Aided Semi-active Suspension with Delayed Information
For automotive engineers, this work addresses the challenge of integrating delayed cloud-based road data with onboard sensors for suspension state estimation, though it is an incremental extension of existing H-infinity filtering methods.
The authors propose an H-infinity filtering framework for cloud-aided semi-active suspension systems with time-varying delays, using a quarter-car model and LMI-based filter design. Numerical simulations demonstrate the feasibility of fusing cloud-based and onboard information in a V2C2V implementation.
This chapter presents an H-infinity filtering framework for cloud-aided semiactive suspension system with time-varying delays. In this system, road profile information is downloaded from a cloud database to facilitate onboard estimation of suspension states. Time-varying data transmission delays are considered and assumed to be bounded. A quarter-car linear suspension model is used and an H-infinity filter is designed with both onboard sensor measurements and delayed road profile information from the cloud. The filter design procedure is designed based on linear matrix inequalities (LMIs). Numerical simulation results are reported that illustrates the fusion of cloud-based and on-board information that can be achieved in Vehicleto- Cloud-to-Vehicle (V2C2V) implementation.