Sideslip Angle Estimation Using Linear Kalman Filter with Neural Network-Based Covariance Adaptation

A methodology for estimating the sideslip angle using a linear Kalman Filter with a model adaptation is presented. In this adaptation, neural networks are trained to estimate the model mismatch and standard deviations, which furthermore enable the calculation of the process and measurement covariance matrices. A particularly suitable mathematical formulation of the tire model is used for this purpose. The algorithm was trained and validated with measurements including different maneuvers and various road conditions. The results show that the algorithm is able to detect the sideslip angle on different road surfaces without knowledge of the current tire-road coefficient of friction. Under high excitations on ice, neural networks may occasionally fail, likely due to insufficient training data on ice. For this and similar situations, a fallback strategy is implemented and its effectiveness is demonstrated with measurements.