Optimal sliding mode control design using a genetic algorithm for electric power steering control

Abstract

This paper presents the design of an optimal robust algorithm for performance control of an automotive electric power steering system. The proposed controller is formulated based on a Sliding Mode Control (SMC) framework. A Genetic Algorithm (GA) with six stages determines the sliding surface parameters of the control mechanism. The Lyapunov criterion evaluates the stability of the system. The novelty of this study lies in integrating the robustness of SMC with the optimization capability of the GAto automatically tune the sliding surface parameters.Unlike conventionalSMCdesigns that rely on manual parameter adjustment, the proposed framework achieves fast convergence and reduced tracking error without complex gain tuning. Furthermore, it simplifies the controller structure and improves energy efficiency while mitigating the chattering phenomenon that typically affects SMC-based systems. The performance of the proposed controller is validated by numerical simulation. The computational results show that tracking errors are significantly reduced (only about 0.101% for 𝑣1 = 30 km/h and 0.132% for 𝑣2 = 60 km/h) compared to conventional PID control. Furthermore, power consumption is also significantly reduced. In addition, the influence of the chattering phenomenon is largely eliminated. This combination can be applied to the control of automotive mechatronic systems.