Unbiased-estimation-based and computation-efficient adaptive MPC for four-wheel-independently-actuated electric vehicles

In this paper, an adaptive model predictive control (AMPC) scheme with high computational efficiency is developed to improve the yaw stability for four-wheel-independently-actuated electric vehicles (FWIA EVs). A novel vehicle model is first established based on an autoregressive with exogenous input (ARX) model, which is independent of vehicle parameters and road conditions. The time-varying model parameters are identified by an unbiased estimation system via an instrumental variable (IV) method. The AMPC scheme is proposed based on the ARX vehicle model for direct yaw moment control (DYC). Then, a multi-objective optimization method is proposed to optimize torque allocation for yaw stability enhancement. Finally, the performance of the proposed scheme is verified under the double lane change and slalom maneuvers in Carsim. Simulation results show that the ARX-model-based unbiased estimation can effectively follow the reference while filtering out measurement noises. The yaw rate signal is smoother and the computational time is reduced by half under the proposed AMPC scheme in comparison to that under conventional dynamics-model-based MPC. In the meantime, the vehicle slip angle and the steering wheel angle are reduced, which indicates improved vehicle stability.