Reinforcement Learning for Human-Machine Cooperative Control: Enhancing Stability and Antirollover with Commercial Vehicle

Safety issues in commercial vehicles have consistently been a focal point of research, with yaw stability control and anti-rollover control being particularly critical. To enhance the yaw stability and anti-rollover capabilities of commercial vehicles operated by drivers with varying driving styles, this paper proposes a human-machine cooperative control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm within the framework of active front steering (AFS) and differential braking control (DBC) systems. Initially, a model predictive control (MPC) algorithm is utilized to develop an integrated controller for AFS and DBC, aimed at managing yaw stability and preventing rollover. Subsequently, to assess the driving styles of different drivers, driving data from multiple participants were collected and categorized into radical, general, and cautious types based on selected characteristic parameters. The DDPG algorithm is then employed to distribute weights between the driver input and the controller for each of the three driver categories, integrating the constraints of the phase space stability boundaries of sideslip angle, yaw rate, and lateral load transfer rate into the reward function design. Finally, the effectiveness of the proposed cooperative control strategy is confirmed using Matlab/TruckSim co-simulation platform.