Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

Abstract An active disturbance rejection control (ADRC) has been developed for stabilizing electric vehicle (EV) systems without the need for model identification. The proximal policy optimization (PPO) algorithm, along with actor and critic neural networks, has been used to fine-tune the adjustable parameters of the ADRC controller to achieve optimal performance in a specific case study. The architecture of PPO implements separate neural networks and ameliorates the PPO adaptability to handle continuous action spaces. By maximizing a reward function based on system output, the PPO agent optimally tunes the gains to reduce undesired speed fluctuations of EVs and improve system stability. Performance evaluation under the new European driving cycle and federal test procedure has been conducted to examine the feasibility of the suggested controller. The disturbance rejection capability of the ADRC controller designed by the PPO algorithm has been tested and compared with prevalent control methodologies. Moreover, real-time examinations of the dynamic behavior of EV systems have been made to identify the capability of the suggested controller in real-world hardware. The results show that the suggested controller outperforms other designed controllers in terms of transient behavior and numerical performance metrics.