Stability analysis of semi-active suspension systems using a data-driven approach

Abstract

The modern vehicles are getting equipped with more and more sensors, which allows the engineers to collect more information about the states of the vehicle and its environment during its operation. This information can be used to increase the capacity and the performances of the control systems. In this paper, a novel data-driven approach is presented to compute the reachability sets of the vehicles, which are equipped with a semi-active suspension system. The dataset, which is used in this paper, is provided by the high fidelity vehicle simulation software, CarSim. Firstly, the dataset is categorized using a stability criterion. Then, a machine-learning algorithm (C4.5 decision tree) is trained, which can categorize a given instance using only the onboard signals of the vehicle. Finally, a possible application of the reachability sets is presented to show the use of the computed sets.