Hybrid State Estimation Combining Artificial Neural Network and Physical Model

Abstract

This article presents a hybrid state estimation using vehicle dynamics as an application. The knowledge about the dynamic states are essential in the vehicle. Ultimately, the built-in control algorithms are using these states to exploit safety, comfort, and performance. In most cases, the states of the vehicle are measured directly. Nevertheless, direct measurement is not profitable or difficult to implement for all states of vehicle dynamics. In this case, state estimators are used. In the past, classical approaches such as modelling of the physical systems have been used for estimation. Due to the continuous developments in the field of computing hardware, methods of machine learning can now also be used in this context. The presented article includes artificial neural networks. With this method, a transfer behavior can be mapped without having knowledge about the system to be estimated. A major problem of such artificial neural networks, however, is the traceability as well as checking the robustness for universal use. Therefore, the artificial neural network is coupled with physical knowledge. This results in a hybrid state estimator based on a Kalman filter. This novel hybrid approach is presented using the example of estimating the roll angle of a vehicle.