An Adaptive Vehicle Sideslip Estimator for Reliable Estimation in Low and High Excitation Driving

Abstract

This work proposes a novel adaptive estimator for reliable vehicle sideslip angle estimation over the full vehicle operating range, using Kalman filtering. It is shown that the vehicle state estimator with adaptive linear tire model proposed in literature only provides reliable estimation for relatively large sideslip angles. This paper proposes a new method that does not suffer this limitation. It relies on the fact that linear tire behavior is mostly a property of the tire/vehicle and to a much lesser extent of the road condition. The proposed estimator therefore contains both a fixed parameter linear tire model, and an adaptive linear tire model. The former allows for reliable and stable estimation for small sideslip angles and linear tire behavior, while the latter allows tracking of nonlinear tire behavior. A smooth transition between the tire models is obtained by adapting the corresponding model covariances in the Kalman filter according to the operating conditions. For this a measure of degree of nonlinearity in tire behavior is defined. Experimental results are provided to demonstrate the robustness and validity of the proposed approach.