A Robust Fuzzy Model Predictive Control Method for Ensuring Platoon Stability in Autonomous Electric Vehicles Across Various Dynamic Conditions

This paper introduces a new hierarchical control method for autonomous electric vehicle (EV) platoons. The method integrates model predictive control (MPC) with fuzzy logic systems to ensure stability in connected vehicle scenarios. The fuzzy MPC framework was developed to address the nonlinearities and interconnections in vehicle dynamics, including electromechanical actuator delay, rolling resistance, tire‐to‐road interaction, and aerodynamic drag. The multi‐level control methodology consists of a high‐level fuzzy inference system that adjusts parameters on‐the‐fly and a low‐level MPC that optimizes longitudinal control commands to account for powertrain and brake latencies. The effectiveness of the controller was systematically tested across five distinct scenarios (urban traffic, highway travel, bad weather conditions, cornering maneuver, and mixed platoon) to represent the wide variety of real‐world driving situations. Each scenario used an adaptive time‐headway policy (THP) that aims to strengthen the system against uncertainties arising from the environment or communication delays, and to outperform traditional fixed‐headway policies. Simulation results demonstrate that the fuzzy MPC controller maintains a strict standard of stability, maintains an acceptable distance from safety vehicles, and facilitates coordinated platoon operation across various motion scenarios.