Designing A Robust Control Algorithm Against Noise an‎d Modeling Parameters Uncertainties for Electric Power Steering

9/7/2026 12:00:00 AM

In this research, a robust control framework for Electric Power Steering (EPS) systems is proposed, aiming to enhance steering accuracy, system stability, an‎d resilience against noise, external disturbances, an‎d modeling uncertainties. Given the inherent challenges posed by dynamic variations, unmodeled dynamics, an‎d sensor noise in automotive environments, there is a growing deman‎d for intelligent, adaptive, an‎d robust control algorithms. The proposed approach integrates a sliding mode control (SMC) structure with adaptive tuning, augmented by machine learning techniques—specifically, the LsBoost algorithm an‎d regression trees—for real-time adjustment of key control parameters.The control architecture is composed of two main phases: first, offline optimization of control parameters under varying operational conditions using simulated an‎d real-world driving data; second, deployment of the trained machine learning model to predict optimal control parameters online, thereby enabling adaptive SMC behavior in the face of system uncertainties. Simulation-based eva‎luations demonstrate significant improvements in system response, tracking accuracy, an‎d robustness against both structured disturbances an‎d parametric uncertainties.This study introduces a data-driven hybrid methodology for designing resilient control algorithms in EPS systems, offering a practical foundation for future development of intelligent an‎d autonomous vehicle steering systems.

فرمان برقي , كنترل مقاوم , كنترلر حالت لغزشي , عدم قطعيت

electric power steering , robust control , Sliding mode controller , uncertentiy

Sanaei

Dr. Mollajafari