Road Pothole Detection Based on Rack Force Estimation of Electric Power Steering Systems

초록

Road hazard detection such as potholes has been actively attempted so far, usually focusing on methodology using vision systems (e.g., cameras, LiDAR) and vertical vibration signals measured by accelerometers mounted on suspension systems, because significantly uneven road surfaces (e.g., potholes) generate the vertical and lateral force on the contact patch of the tire during driving maneuvers and they are transmitted to the vehicle steering system. It usually acts as an unknown disturbance input, causing control performance degradation and it often leads to the failure of tires and automotive components such as suspension systems. Therefore, the research objective is to explore the new research on road hazard detection such as potholes based on rack force estimation of electric power steering (EPS) system (or motor-driven power steering, MDPS). This study explores a robust pothole detection algorithm based on Kalman filtering of EPS to estimate the unknown disturbance input (i.e., rack force in EPS). The Kalman filter (KF) has been recently adapted for the discrete-time domain, which enables the implementation of real-time embedded control systems. The improved KF-UI (Kalman filter with unknown input) algorithm, which combines the existing Kalman filter with disturbance observer (DOB), is designed based on a torque angle sensor (TAS) information and rack displacement sensor of the steering system. The estimated rack force is then used to detect potholes using signal processing and high-pass filtering. MATLAB/SIMULINK (estimated) and CARSIM software (assumed to be true) will be used to evaluate the estimation performance of rack force and detection accuracy of potholes.