This paper explores design methods for a vehicle handling dynamics state estimator based on a linear vehicle model. The state estimator is needed because there are some states of the vehicle that cannot be measured directly, such as sideslip velocity, and also some which are relatively expensive to measure, such as roll and yaw rates. Information about the vehicle states is essential for vehicle handling stability control and is also valuable in chassis design evaluation.
The aim of this study is to compare the performance of a Kalman filter with that of a robust filter, under conditions which would be realistic and viable for a production vehicle. Both filters are thus designed and tested with reference to a higher order source model which incorporates nonlinear saturating tyre force characteristics. Also, both filters rely solely on accelerometer sensors, which are simulated with expected noise characteristics in terms of amplitude and spectra.
As is widely known, the Kalman filter is a stochastic filter whose design depends on the nominal vehicle model and statistical information of process and measurement noises. By contrast, the robust filter is deterministic, formulated in terms of model parameter uncertainties and the expected gain of process and measurement noises. The objective of both filter designs is to minimise the variance of the estimation error. Both filters are designed to compensate the vehicle model non-linearities, parameter uncertainties and other modeling errors, which are represented in terms of process and measurement noise covariances in Kalman filter design and in terms of additive model uncertainties in robust filter design.
The study shows that the robust filter offers higher performance potential. The work concludes with a discussion on the practical realisation of each method, and gives recommendations for further research into a single design methodology which combines the benefits of both approaches.