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Maintaining both path-tracking accuracy and yaw stability of distributeddrive electric vehicles (DDEVs) under various driving conditions presents asignificant challenge in the field of vehicle control. To address thislimitation, a coordinated control strategy that integrates adaptive modelpredictive control (AMPC) path-tracking control and direct yaw moment control(DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchicalframework, is coordinated by the upper layer of path-tracking control and thelower layer of direct yaw moment control. Based on the linear time-varyingmodel predictive control (LTV MPC) algorithm, the effects of prediction horizonand weight coefficients on the path-tracking accuracy and yaw stability of thevehicle are compared and analyzed first. According to the aforementionedanalysis, an AMPC path-tracking controller with variable prediction horizon andweight coefficients is designed considering the vehicle speed's variation inthe upper layer. The lower layer involves DYC based on the linear quadraticregulator (LQR) technique. Specifically, the intervention rule of DYC isdetermined by the threshold of the yaw rate error and the phase diagram of thesideslip angle. Extensive simulation experiments are conducted to evaluate theproposed coordinated control strategy under different driving conditions. Theresults show that, under variable speed and low adhesion conditions, thevehicle's yaw stability and path-tracking accuracy have been improved by21.58\% and 14.43\%, respectively, compared to AMPC. Similarly, under highspeed and low adhesion conditions, the vehicle's yaw stability andpath-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 theproposed adaptive path-tracking controller is effective across differentspeeds.

Coordinated Control of Path Tracking and Yaw Stability for Distributed Drive Electric Vehicle Based on AMPC and DYC