Longitudinal Tracking Control of Vehicle Platooning Using DDPG-based PID
Junru Yang, Xingliang Liu, Shidong Liu, Duanfeng Chu, Liping Lu, Chaozhong Wu
Abstract
Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. In this paper, a learning control method combined Deep Deterministic Policy Gradient and Proportional-Integral-Derivative (DDPG-PID) controller is proposed. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. Based on the Hardware-in-the-Loop (HIL) simulation platform, the DDPG-PID controller is compared with the conventional PID controller under the test condition. Experiment results indicate that on 38.95% stability time in vehicular platooning system is decreased by utilizing the proposed method. The performance of maximum distance error is also improved efficiently, which is reduced by 60.94%. The research in this paper is a further development of learning control method and provides a new idea for the practical application of DRL algorithm in industrial field.