Vehicle Stability Upper-Level-Controller Based on Parameterized Model Predictive Control

This paper presents an upper-level vehicular stability controller based on parameterized Model Predictive Control (MPC). The proposed system computes the additional moment applied on the vehicle’s yaw axis to improve the lateral stability. In the MPC formulation, the optimization problem is defined as a quadratic programming derived from a linear time-invariant model of vehicle dynamics. The control system is implemented based on a model that considers the rolling movement and on a simpler model that does not consider it, in order to evaluate the effects of using a more representative linear model for more accurate prediction or a simplified model for faster calculation. Constraints are imposed on the optimization problem to deal with the limits in the corrective yaw moment. A parameterized MPC approach is designed to reduce the number of optimization variables, and hence, reducing the computation time required for real-time implementation. Model-in-the-loop simulations are proposed to evaluate the effectiveness of the MPC strategy to avoid steering instability. Simulations are performed for profiling the calculation time, tuning the parameters, and testing algorithm running in an ARM-Cortex A8 on real-time control. Simulation results show that the proposed control strategy is effective in preventing destabilization and demonstrates that even with a longer computation time, the resulting MPC scheme meets the control requirements successfully, even under the presence of model disturbances.