Ride Comfort Optimisation via Nonlinear Model Predictive Control: Active Suspension and In-Wheel Motor Integration

Enhancing ride comfort is a key objective in the development of modern vehicles, particularly those featuring advanced electric architectures. This research proposes an innovative control strategy based on Nonlinear Model Predictive Control, designed to operate two actuators: a linear electromechanical actuator for an active suspension system and an in-wheel electric motor. By employing a single predictive model, the proposed solution reduces complexity and computational cost while maintaining high performance. Two implementations are presented: i) a fixed-step prediction horizon controller; ii) a real-time capable variable-step prediction horizon controller. The controllers have been implemented and tested in MATLAB/Simulink using a half-car vehicle model, with tire dynamics simulated through Siemens MF-Swift. A comparative analysis has been carried out against controllers operating exclusively on the suspension actuator or on torque correction. Numerical simulations across various road scenarios demonstrate a substantial improvement in ride comfort, as reflected by significant reductions in several defined Key Performance Indicators.