Dimensionless Model-Based System Tracking Via Augmented Kalman Filter for Multiscale Unmanned Ground Vehicles

Abstract

In recent years, many unmanned vehicle designs and autonomous driving functions have been introduced in the automotive industry to increase the safety and versatility of multiple vehicle designs. It is critical to select a plant vehicle model and a compact uncertainty representation regardless of the vehicle scale for vast deployment. This article introduces a dimensionless representation of a dynamic vehicle model that is suitable for generalized dynamic analysis. Demonstrations included in this article showed that the compact uncertainty bounds of the dimensionless parameters could be used to generate an adaptive observer suitable for full-sized and corresponding scaled vehicles. The proposed dimensionless model-based observer can assess motion states, mass, center of gravity displacement, and the tire stiffness online with common onboard inertial measurement unit (IMU). The effectiveness and sensitivity of the proposed technique are highlighted through simulations and experiments on scaled test vehicles.