Distributed Robust Vehicle State Estimation

Abstract

A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners’ velocity estimates, which are obtained by an unscented Kalman filter. The corners’ estimates from a Kalman observer, which is formed by integrating the model-based and kinematic-based velocity estimation approaches, are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Road tests confirm the robustness of the methods independent of the powertrain configuration on surfaces with various friction conditions in pure and combined-slip maneuvers, which are arduous for the current vehicle state estimators.