Virtual Sensor Synthesis for Motorcycle Sideslip Angle Estimation Using Optimal NARX-NN Model

The sideslip angle is crucial for vehicle stability, especially for single-track vehicles. As it is difficult to measure this quantity directly, the use of virtual sensors (observers) is common practice in this field.Neural network-based virtual sensors are becoming increasingly popular due to their ability to handle nonlinear conditions and noise. This paper presents a rigorous methodological approach to selecting measured quantities and determining the appropriate sampling frequency for stable sideslip reconstruction using a nonlinear autoregressive neural network with exogenous inputs (NARX-NN) model. A literature review suggests that most studies on sideslip angle estimation focus solely on achieving superior accuracy, with little extensive discussion of the selected quantities or sampling conditions required for effective estimation. This paper uses an information theory approach combined with a qualitative approach to select suitable model input quantities, the optimal number of look-ahead steps (‘embedding’), and the optimal sampling frequency to maximise the ratio between the latent information provided to the model for state reconstruction and the reduced computational burden on the electronic control unit (ECU). The results show that the sampling and computing frequency can be reduced by up to 20 times compared to the common baseline. This enables the use of less powerful hardware for the same model, resulting in better resource utilisation.