Steering Feedback Torque Prediction Based on Sequence-to-Sequence Network With Switcher-Assisted Training Algorithm

The steer-by-wire (SbW) system has gained recognition as the future of intelligent vehicles due to its attributes, such as safety, simplification, and flexibility. However, the elimination of mechanical linkage necessitates the provision of artificial steering feedback torque (SFT), which is crucial for the potential driver to manipulate the vehicle. To enhance the steering feel, this article extends the SFT design range and proposes an SFT prediction scheme based on the sequence-to-sequence (S2S) network with the switcher-assisted (SA) training algorithm. The models of the electric power steering (EPS) and SbW are first established to analyze the input features. The S2S network with gated recurrent units (GRU) is then presented, where the encoder scheme incorporates the squeeze-and-excitation (SE) operation to achieve adaptive feature recalibration. Subsequently, the SA algorithm is proposed to alleviate the exposure bias based on the principle of multitask learning (MTL), wherein online training is regarded as the main task and offline training is treated as the auxiliary task. The weighting coefficients between tasks are optimized using an assisted network named switcher, facilitating a gradual transition from MTL to the single main task, thereby avoiding complex tuning processes. The validation results indicate the proposed scheme outperforms existing methods regarding estimation and prediction. The ablation experiments are further conducted to illustrate the effectiveness of SE blocks and the SA algorithm. Finally, the SFT construction simulation, involving target prediction and torque tracking, is conducted, validating that variable-length prediction can adapt to various conditions and improve tracking performance.