Work

Enhancing Steering Responsiveness in Four-Wheel Steering Steer-by-Wire Systems Using Machine Learning

Year: 2026

Type: conference-paper

Abstract: Steer-by-wire (SBW) systems in wheel-steering vehicles enhance maneuverability by eliminating mechanical linkages. However, they are susceptible to delays between steering input and pinion response, which can compromise control precision and safety. To mitigate these delays, we developed a machine learning-based compensation method employing a hybrid architecture of convolutional neural networks (CNNs) and gated recurrent units (GRUs) to predict and adjust pinion behavior in real time. The model was trained using experimental data collected from a four-wheel steering test platform, including steering angle inputs, motor signals, and pinion position feedback. By learning the relationship between steering commands and rack force, the model enables dynamic delay correction under both nominal and fault conditions. The system is implemented on an NXP microcontroller and validated through experimental testing, and compared with other hybrid model configurations for performance evaluation. The results demonstrate that the CNN–GRU approach reduces the average steering delay to 3 ms, outperforming conventional PID tuning methods while maintaining high accuracy and system stability.

Source: IEEE International Conference on Knowledge Innovation and Invention (ICKII)

DOI: 10.3390/engproc2025120058

Authors: Amarnathvarma Angani, Teressa Talluri, Myeong-Hwan Hwang, Kyoung-Min Kim, Hyun Rok Cha

Institutions: Korea Institute of Industrial Technology, Kwangju Women's University, Korea University of Science and Technology

Language: English

Topic: Vehicle Dynamics and Control Systems

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funders: Korea Institute of Industrial Technology, Korea Evaluation Institute of Industrial Technology