Supervisor-Based Hierarchical Adaptive MPC for Yaw Stabilization of FWID-EVs Under Extreme Conditions

This work focuses on the yaw stabilization of the four-wheel-independent-drive electric vehicle (FWID-EV) with the constrained active front steering (AFS) and direct yaw-moment control (DYC). First, a modified tire model is employed in the design of the unscented Kalman filter to realize the estimation of the tire-road friction coefficient (TRFC), and a backpropagation neural network is developed to online estimate the tire cornering stiffness; Second, a yaw stabilization supervisor is designed to solve the conflicts between the AFS and DYC systems, and the mode-boundary maps of the tire operating regions are utilized to generate the triggered signals so as to activate the systems; Third, a hierarchical adaptive model predictive control (MPC), including the estimation, activation, compensation, and distribution layers is proposed for yaw stabilization of the FWID-EV under the extreme conditions. Emergency maneuvers under big path curvature, low TRFC, and high vehicle speed are designed. Both software-in-the-loop and hardware-in-the-loop tests are performed to examine the effectiveness and practicability of the proposed methods, respectively.