@article{Essuman2025, 
author = {Jones B. Essuman and Xiangyu Meng and Xun Tang and Michael D. Curry},
title = {Reinforcement Learning-Based Motion Control of Four In-Wheel Motor-Actuated Electric Vehicles},
year = {2025},
journal = {Unmanned Systems},
volume = {13},
number = {6},
pages = {1755-1768},
keywords = {energy efficiency, deep reinforcement learning, electric vehicles, Motion control, PID control, four in-wheel motor},
url = {https://www.sciopen.com/article/10.1142/S230138502543006X},
doi = {10.1142/S230138502543006X},
abstract = {In this paper, we leverage a reinforcement learning approach to address the motion control problem of Four In-Wheel Motor Actuated Vehicles aimed at achieving precise control while optimizing energy efficiency. Our control architecture consists of four adaptive Proportional-Integral-Derivative controllers, each assigned to an independent vehicle wheel. We train these controllers using an actor-critic framework in two standard driving scenarios: acceleration and braking, as well as a double lane-change maneuver. This method eliminates the need for a detailed mathematical model of the complex vehicle dynamics. Moreover, the adaptive mechanism enables controllers to dynamically adapt to varying operating conditions. After training, the resulting controllers are tested in unseen scenarios to validate their robustness and adaptability beyond the training environment. The testing results show that our controllers achieve precise velocity and trajectory tracking while maintaining low energy consumption.}
}