Optimized Fuzzy Supertwist‐Based Control for Boosted Stability in Electric Vehicles

ABSTRACT This article investigates an advanced control method called the fuzzy adaptive super‐twist integral sliding mode (FASISM) controller. It combines adaptive sliding mode control with fuzzy logic to improve stability and effectively handle external disturbances. The uncertainties in the system are integrated into a unified framework, enabling them to be effectively addressed using an adaptive control approach. This controller aims to achieve stabilization and optimal performance in electric vehicles by addressing system uncertainties and mitigating the impact of disturbances, thereby ensuring reliable and efficient operation under varying conditions. To realize efficient control, the EV's battery voltage is utilized as the control input, while the EV speed serves as the system output; both variables are restricted to maintain optimal performance and stability. The proposed strategy combines the Takagi‐Sugeno (TS) fuzzy model with a parallel distributed compensation fuzzy controller, incorporating an LMI‐based optimal super‐twist SMC adaptive scheme. The closed‐loop system is proven to attain uniform ultimate boundedness with the implementation of the proposed sliding mode controller. Simulation results in MATLAB demonstrate the robust performance of the FASISM controller, achieving rapid stabilization of EV speed even with the existence of uncertainties and disturbances.