Simultaneous Estimation of Unknown Road Roughness Input and Tire Normal Forces Based on a Long Short-Term Memory Model

Abstract

This paper reports an initial study on the simultaneous estimation of unknown road roughness input and tire normal forces for automotive vehicles using a long short-term memory (LSTM) model. Active safety systems and the improvement of ride comfort using vehicle information have garnered increasing attention in the automotive industry. In particular, active safety systems rely significantly on road roughness data and the normal force of the tires. If these factors can be measured in real-time for a driving vehicle, the measured data can be used for automotive control systems for tasks, such as semi-active and active suspension control, rolling motion control, and torque vectoring. However, it is typically difficult to measure the road roughness and tire normal force directly in real-time by mounting physical sensors on the vehicle. In this study, we explore the simultaneous estimation of these factors using an LSTM model that requires only time-series data of the vehicle body. The LSTM model is implemented by using MATLAB/Simulink and includes data preprocessing, learning, and verification steps. To evaluate the estimation performance of the LSTM model, we compared it with a Kalman filter and used CarSim vehicle simulation software to simulate and interpret the dynamic behavior of vehicles.

Keywords

• Long short-term memory model
• discrete Kalman filter-unknown input
• 7-DOF full-car suspension model
• tire normal forces
• road roughness