Adaptive angle fault-tolerance estimation for steer-by-wire system using hybridizing physical and data-driven model

The accurate steering angle is the foundation of steering closed-loop control of steer-by-wire (SBW) system and vehicle driving safety. Since the SBW system relies on sensor to obtain the steering angle signal, the high-performance angle fault-tolerance estimation is crucial when the sensor occurs offset or failure faults. However, the sensor faults signal and the system nonlinear characteristics that difficult to model will reduce the angle estimation performance in the form of noises. This leads the accurate and stable angle fault-tolerant estimation to remaining challenging. To solve this issue, this paper proposes an adaptive angle fault-tolerance estimation for SBW system using hybridizing physical and data-driven model. Firstly, a deep neural network is designed and trained to capture the vehicle system nonlinear characteristic and establish data-driven model, as well as obtain additional angle information of SBW system. Secondly, an adaptive unscented Kalman filter (UKF) combining a confidence level mechanism is constructed. Based on the density of training data, the confidence level mechanism can dynamically adjust the covariance of UKF to balance the degree of dependence between the system physical model and data-driven model, which can improve the interpretability of the data-driven model and the accuracy of angle estimation. Then, to enhance the reliability of angle estimation, an expansion fault-tolerant mechanism is constructed to suppress the influence of abnormal measurement values when the angle sensor fails. Finally, the proposed method is verified using a vehicle equipped with SBW system. The experiment results demonstrate that the proposed method can accurately estimate steering angle and improve the fault-tolerant performance. The angle estimation application experiment further verifies the feasibility of the proposed method in the steering closed-loop control of SBW system.