Multi-method control approaches for cruise control in intelligent vehicles

With the rapid advancement of intelligent vehicles, the performance of cruise control systems has become an important research topic. This paper adopts a direct longitudinal control architecture to develop a cruise control system framework. Based on fuzzy control and PID control theories, four representative control strategies are designed and comparatively analyzed, including single-input fuzzy control, multi-input fuzzy control, PID control, and fuzzy PID control. A co-simulation platform integrating MATLAB/Simulink and CarSim is established to evaluate the control performance of these methods under various target speed conditions. The results demonstrate that all control strategies can achieve effective speed tracking. Among them, the multi-input fuzzy control method exhibits superior overall performance, as it better aligns with human driving behavior, maintains small steady-state errors, and demonstrates strong robustness under varying conditions, making it well-suited for handling the inherent nonlinear characteristics of vehicle dynamics. In addition, the fuzzy PID control strategy outperforms the PID controller in terms of overall performance.