Adaptive Cruise Control Using Model Reference Adaptive Control with Augmented Optimal Baseline in Electric Vehicles

Adaptive Cruise Control (ACC) enhances safety and efficiency in electric vehicles (EVs), but conventional methods like PID and MPC often struggles with real-time dynamics and model uncertainty. This study introduces a hybrid ACC approach combining Model Reference Adaptive Control (MRAC) with a Linear Quadratic Regulator-Proportional-Integral (LQR-PI) controller to improve robustness and adaptability. Simulations under various scenarios - including speed tracking, stop-and-go, and parameter variations - show that MRAC-LQR-PI maintains a consistent ITAE of around 102.9, while LQR-PI alone shows increased sensitivity, with ITAE rising from 99.510 to 120.368. These results highlight the proposed method's superior performance under uncertainty. Future work includes adding robust controls, validating the system in real time, and implementing it on hardware. © 2025 IEEE.