Active Safety Control of Automated Electric Vehicles at Driving Limits: A Tube-Based MPC Approach

To enhance the active safety performance for automated electric vehicles (AEVs) at driving limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control (DYC) is adopted. To deal with external disturbance and modeling error, tube-based model predictive control (MPC) is applied to the control algorithm design, which takes the improvement of handling stability and path-tracking performance into considerations. Taking the constraints into account, including control vector constraints, lateral stability constraints, rollover prevention constraints, and path-tracking error constraints, the integrated controller is designed and worked out by addressing the optimization issue. To verify the effectiveness and feasibility of the integrated controller, two extreme driving conditions are conducted based on hardware-in-the-loop (HIL) tests. The test results indicate that the integrated controller can improve vehicle's handling stability and path-tracking performance in unison at driving limits. Besides, the integrated controller shows strong robustness in extreme conditions.

Automated summary

This research focuses on the collaborative control of four-wheel steering and direct yaw-moment control to enhance the active safety performance of automated electric vehicles. The study applies tube-based model predictive control to deal with disturbance and modeling error, and successfully designs an integrated controller that improves handling stability and path-tracking performance. Experimental tests prove the effectiveness and robustness of the integrated controller under extreme driving conditions.