| Title |
Electric Power Steering System Identification Using Artificial Neural Network for Autonomous Vehicles |
| Authors |
(Rodi Hartono) ; (Hyun Rok Cha) ; (Hee Tae Chung) ; (Kyoo Jae Shin) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.3.426 |
| Keywords |
Electric power steering; System identification; Artificial neural network; Backpropagation; Nonlinear behavior |
| Abstract |
The steering system is crucial for autonomous vehicles to accurately convert angle inputs into motion trajectories. Among the types of steering systems, electric power steering (EPS) has gained widespread adoption due to its superior reliability, safety, and efficiency compared to hydraulic systems. Precise steering control necessitates a deep understanding of EPS dynamics, typically achieved through mathematical modeling or system identification (SI). However, traditional modeling methods often fall short in capturing the complex nonlinear behavior of EPS. To address this limitation, we propose an artificial neural network (ANN) model trained using backpropagation (BP) to represent the dynamic characteristics of EPS. The ANN is trained on real-world data collected from a physical EPS system. Extensive testing on diverse datasets demonstrates the exceptional performance of our proposed model, achieving a remarkable fit of over 99.6% with measured data. This significant improvement over conventional methods highlights the potential of the ANN-based BP model as a superior approach for SI in EPS. |