  Featuring the fast response and flexibility in control allocation, an
electric vehicle with in-wheel motors is a good platform for implementing
advanced vehicle dynamics control. Among many active safety functions of an
in-wheel motor driven vehicle (IMDV), lateral stability control is a key
technology, which can be realized through torque vectoring. To further advance
the lateral stabilization performance of the IMDV, in this paper a novel
data-driven nonlinear model predictive control (NMPC) is proposed based the
recurrent high-order neural network (RHONN) modelling method. First, the new
RHONN model is developed to represent vehicle's nonlinear dynamic behaviors.
Different from the conventional physics-based modelling method, the RHONN model
only needs data and forms high-order polynomials. Based on the RHONN model, the
steady-state responses of vehicle's yaw rate and sideslip angle are iteratively
optimized and set as the control objectives for low-level controller, aiming to
improve the system robustness. Besides, a nonlinear model predictive controller
is designed based on the RHONN, which is expected to improve the prediction
accuracy during the receding horizon control. Further, a constrained
optimization problem is formulated to derive the required yaw moment for
vehicle lateral dynamics stabilization. Finally, the performance of the
developed RHONN-based nonlinear MPC is validated on an IMDV in the
CarSim/Simulink simulation environment. The validation results show that the
developed approach outperforms the conventional method, and further improves
the stable margin of the system. It is able to enhance the lateral
stabilization performance of the IMDV under various driving scenarios,
demonstrating the feasibility and effectiveness of the proposed approach.
