Modeling on Steering Feedback Torque Based on Data-Driven Method

赵蕊 , 邓伟文, 任秉韬



摘要—The steering feedback torque (SFT) is a key part of driving simulator and steer-by-wire system, which provides driver with desired road feel and vehicle motion dynamics. Therefore, accurately modeling of SFT is of great signifificance for driver to get better steering feel. Since SFT can be affected by many linear or nonlinear factors, it is appropriate to model SFT using data driven method. In this article, we adopt artifificial neural network (ANN) and Gaussian process regression (GPR) to build the SFT model, and analyze the performance. Considering the fact that the contributing factors for SFT may vary under different driving conditions, we employ K-Means to precluster the training data set to improve the model accuracy. The model training and validation processes are mainly data-driven, and the results show that GPR and ANN can achieve similar prediction accuracy with the mean square error to be around 0.10. Since the GPR model can be trained much faster than ANN model, it is more suitable for real-time application. It is further demonstrated that using preclustered data based on K-Means for model training can signifificantly improve its accuracy without sacrifificing its computational effificiency.

关键词—Artifificial neural network (ANN), data driven modeling, Gaussian process regression (GPR), K-means cluster, steering feedback torque (SFT).