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<p>This article presents a novel hybrid control method for improving the performance of an automotive electric power steering (EPS) system. The proposed controller synthesizes two control signals: one from a sliding mode control (SMC) and the other from a backstepping control (BSC), each designed to control a different object of the EPS. Specifically, the SMC governs the steering column angle to ensure robustness against external disturbances, while the BSC controls the steering motor angle to enhance responsiveness. A fuzzy system adjusts the ideal reference input of the BSC based on the steering motor angle and speed errors to improve the system’s adaptability. The fuzzy output is then scaled by a gain factor optimized using a genetic algorithm, enabling the system to adapt effectively to changing conditions. An extended state observer is also integrated to estimate unmeasured states and total disturbances, contributing to disturbance rejection and reduced sensor noise dependence. Simulation results show that the combined control signals allow the system output to follow the desired trajectory with negligible tracking error. The proposed method eliminates phase lag and chattering, maintaining estimation errors below 5.85% for disturbances and 1.70% for state variables. Moreover, the controller ensures energy-efficient performance by keeping power consumption within acceptable limits. This hybrid control framework, combining robust nonlinear techniques with intelligent adaptation and optimization, constitutes a novel contribution to EPS control that has not been addressed in previous publications. These results confirm the method’s robustness, adaptability, and practical viability under various operating scenarios.</p>

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An integrated robust control strategy based on observed states for electric power steering

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Tuan Anh Nguyen

摘要

This article presents a novel hybrid control method for improving the performance of an automotive electric power steering (EPS) system. The proposed controller synthesizes two control signals: one from a sliding mode control (SMC) and the other from a backstepping control (BSC), each designed to control a different object of the EPS. Specifically, the SMC governs the steering column angle to ensure robustness against external disturbances, while the BSC controls the steering motor angle to enhance responsiveness. A fuzzy system adjusts the ideal reference input of the BSC based on the steering motor angle and speed errors to improve the system’s adaptability. The fuzzy output is then scaled by a gain factor optimized using a genetic algorithm, enabling the system to adapt effectively to changing conditions. An extended state observer is also integrated to estimate unmeasured states and total disturbances, contributing to disturbance rejection and reduced sensor noise dependence. Simulation results show that the combined control signals allow the system output to follow the desired trajectory with negligible tracking error. The proposed method eliminates phase lag and chattering, maintaining estimation errors below 5.85% for disturbances and 1.70% for state variables. Moreover, the controller ensures energy-efficient performance by keeping power consumption within acceptable limits. This hybrid control framework, combining robust nonlinear techniques with intelligent adaptation and optimization, constitutes a novel contribution to EPS control that has not been addressed in previous publications. These results confirm the method’s robustness, adaptability, and practical viability under various operating scenarios.