Lane-changing control strategy for distributed drive vehicles considering yaw stability

Abstract

Intelligent vehicles are prone to dangerous issues such as sideslip and instability when changing lanes to avoid obstacles under some extreme conditions. Therefore, to improve safety and stability during the obstacle-avoidance process, this paper proposes a lane-change control method that considers yaw stability based on distributed drive electric vehicles. Fuzzy adaptive model predictive control and proportional integral derivative (PID) control are, respectively, established to compute the optimal front wheel steering angle and vehicle longitudinal torque under lateral and longitudinal decoupling. Additionally, a direct yaw moment controller is constructed based on model predictive control to calculate the additional yaw moment, which is then distributed according to the tire adhesion utilisation rate to optimise yaw stability in lane-changing obstacle-avoidance scenarios. Finally, the proposed control framework is verified in typical obstacle-avoidance scenarios. The results show that, compared to the control method that do not consider yaw stability, the average yaw rate deviation is reduced by 54.0% on high-adhesion road surfaces and by 61.2% on low-adhesion road surfaces, achieving further optimsation in the safety and stability of the obstacle-avoidance process.