A Reduced Order Steering State Observer for Automated Steering Control Functions

Abstract

State observer design is one of the key technologies in research for autonomous vehicles, specifically the unmanned control of the steering wheel. Currently, estimation algorithms design is one of the most important challenges facing researchers in the field of intelligent transportation systems (ITS). In this paper we present: mathematical model and dynamic response identification of electric power steering column by least square identification experiments; observability analysis of identified models; model simplification via mechanical approach and singular perturbation model reduction; and two reduced order steering Kalman filter syntheses for estimation of steering column states and disturbances. The simulation and experimental results conducted on a steering test bench executed in the FCA Technical Center show that designed Kalman observers have good adaptability for steering wheel position control and safety aims. This can be useful in intelligent vehicle path tracking in outdoor experiments.