Differential Drive Based Yaw Stabilization Using MPC for Distributed-Drive Articulated Heavy Vehicle

This paper presents a differential drive approach for distributed-drive articulated heavy vehicles (DAHVs) with wheel-side motors. The objective is to keep the yaw stability for DAHVs with direct yaw moment in driving process when the external disturbance acts on one of the vehicle parts. Three contributions are made in this paper: 1) An disturbance observer based Extended Kalman Filter (EKF) strategy is implemented to deal with the unknown mismatched disturbances in strong nonlinear vehicle system for DAHVs; 2) The differential drive approach is developed with the guidance of model predictive controller (MPC) to cope with the vehicle instability with the decoupling dynamic analyses for DAHVs under the observed external disturbance and the specific drive limitation of wheel-side motor for the first time; 3) The novel verification techniques with co-simulation model combining with the ADAMS, Simulink, and AMESim software for DAHVs is employed to verify this vehicle stability controller, which is more reasonable than the traditional methods with simple virtual model. The results demonstrate that the proposed approaches can reduce the oscillation amplitude and period of vehicle yaw motion for about 40% and 80%, respectively. Moreover, the MPC strategy is more efficient comparing with the LQR strategy especially on boundaries issue treatment, which can improve the vehicle stability greatly.