Design and Experimental Verification of Real-Time Nonlinear Predictive Controller for Improving the Stability of Production Vehicles

Vehicle stability control under extreme conditions is influenced by the coupled nonlinear characteristics of vehicle dynamics, corresponding safety constraints, and rapid response requirements. To address these problems, this brief proposes a real-time nonlinear predictive controller for a distributed drive electric vehicle. First, nonlinear lateral dynamics of the vehicle are applied to develop the stability controller on low friction coefficient surfaces. Second, the requirement for suppressing the sideslip angle is integrated into the objective function to prevent the vehicle from destabilizing due to excessive sideslip angles. Finally, a fast solution algorithm is proposed by solving the transformed two-point boundary value problem, making it possible to apply nonlinear predictive controller to experimental road tests. The experiments with a production vehicle are conducted on the snow-covered dynamic roads of the DongFeng's winter test center. The test results on low friction coefficient roads show that the overall passing speed can be improved from 50-55 to 60-70 km/h with the proposed controller.

Automated summary

This paper proposes a real-time nonlinear predictive controller to improve vehicle stability by addressing the nonlinear dynamics of the vehicle on low friction coefficient surfaces and the requirement to suppress sideslip angles. Experimental results demonstrate that the proposed controller significantly improves the passing speed of vehicles on low friction coefficient roads.