Enhancing torque performance in electric four-wheel drive systems using fuzzy GPC

Abstract

This paper presents a robust supervisory control strategy for speed regulation in a four-wheel-drive electric vehicle (EV) equipped with in-wheel induction motors. A hybrid control architecture is developed by combining fuzzy logic control (FLC) and generalized predictive control (GPC), with an intelligent switching mechanism that dynamically allocates control authority based on real-time operating conditions. FLC is employed to manage transient phases such as acceleration and deceleration, while GPC ensures optimal performance during steady-state operation. The proposed control system is modeled and validated in the MATLAB/Simulink environment. Simulation results demonstrate that the hybrid controller achieves a 27% improvement in transient response, a 15% reduction in steady-state speed fluctuations, and a 19% decrease in energy consumption under urban driving conditions. Furthermore, the controller maintains reliable performance under parameter variations of up to 25% and road gradients of up to 15%. Compared to standalone FLC and GPC controllers, the hybrid approach improves transient speed recovery by 35% and reduces steady-state error by 22%. Overall, this hybrid FLC-GPC strategy effectively addresses key challenges in EV control, such as system nonlinearity, parameter uncertainty, and external disturbances, while ensuring high dynamic responsiveness, steady-state precision, and energy efficiency. These results highlight the potential of the proposed method for future intelligent and autonomous electric mobility systems.