Parametric identification of vehicle handling using an extended Kalman filter

This paper considers a novel method for estimating parameters in a vehicle handling dynamic model using a recursive filter. The well known extended Kalman filter – which is widely used for real-time state estimation of vehicle dynamics – is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. In a linear-in-the-parameters example, the results are shown to be comparable to least-squares identification, but the system works equally well for the more general nonlinear handling model examples, and should be well suited to any smoothly nonlinear system. Moreover, it is shown that by simple adjustment of the tuning parameters the filter can operate in a real-time capacity.