An extended adaptive Kalman filter for real-time state estimation of vehicle handling dynamics

This paper considers a method for estimating vehicle handling dynamic state s in real-time, using a reduced sensor set; the information is essential fo r vehicle handling stability control and is also valuable in chassis design evaluation. An extended (nonlinear) Kalman filter is designed to estimate the rapidly varying handling state vector. This employs a low order (4 DOF) handling model which is augmented to include adaptive states (cornering st iffnesses) to compensate for tyre force nonlinearities. The adaptation is d riven by steer-induced variations in the longitudinal vehicle acceleration. The observer is compared with an equivalent linear, model-invariant Kalman filter. Both filters are designed and tested against data from a high order source model which simulates six degrees of freedom for the vehicle body, and employs a combined-slip Pacejka tyre model. A performance comparison is presented, which shows promising results for the extended filter, given a sensor set comprising three accelerometers only. The study also presents an insight into the effect of correlated error sources in this application, a nd it concludes with a discussion of the new observer's practical viability .