  Maintaining both path-tracking accuracy and yaw stability of distributed
drive electric vehicles (DDEVs) under various driving conditions presents a
significant challenge in the field of vehicle control. To address this
limitation, a coordinated control strategy that integrates adaptive model
predictive control (AMPC) path-tracking control and direct yaw moment control
(DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical
framework, is coordinated by the upper layer of path-tracking control and the
lower layer of direct yaw moment control. Based on the linear time-varying
model predictive control (LTV MPC) algorithm, the effects of prediction horizon
and weight coefficients on the path-tracking accuracy and yaw stability of the
vehicle are compared and analyzed first. According to the aforementioned
analysis, an AMPC path-tracking controller with variable prediction horizon and
weight coefficients is designed considering the vehicle speed's variation in
the upper layer. The lower layer involves DYC based on the linear quadratic
regulator (LQR) technique. Specifically, the intervention rule of DYC is
determined by the threshold of the yaw rate error and the phase diagram of the
sideslip angle. Extensive simulation experiments are conducted to evaluate the
proposed coordinated control strategy under different driving conditions. The
results show that, under variable speed and low adhesion conditions, the
vehicle's yaw stability and path-tracking accuracy have been improved by
21.58\% and 14.43\%, respectively, compared to AMPC. Similarly, under high
speed and low adhesion conditions, the vehicle's yaw stability and
path-tracking accuracy have been improved by 44.30\% and 14.25\%, respectively,
compared to the coordination of LTV MPC and DYC. The results indicate that the
proposed adaptive path-tracking controller is effective across different
speeds.
