Active Air Suspension Modeling and Control for Roll Stability in Heavy-duty Vehicles

Abstract

Heavy-duty trucks are particularly vulnerable to rollovers during aggressive manoeuvres due to their high centre of gravity and large, often unevenly distributed mass. Traditional passive suspension systems cannot often actively counteract rollovers. This thesis examines the application of active air suspension controlled by a Nonlinear Model Predictive Controller (NMPC) to improve roll stability by leveraging future steering information.

A high-fidelity multibody truck model, based on the Volvo Transport Model (VTM), was extended to include active suspension dynamics. Nonlinear vehicle dynamics were modelled to accurately capture the coupled interactions between longitudinal, lateral, vertical, roll and yaw dynamics for the use in the NMPC. The controller was implemented using CasADi and integrated within a MATLAB/Simulink framework. It utilized the FATROP solver to predict future vehicle states and optimally adjust suspension forces to mitigate roll motion while maintaining the desired speed. The controller was deployed and tested against a baseline controller in various driving scenarios. Simulation results demonstrate that the controller consistently reduces lateral load transfer and roll angle for rollover-prone driving scenarios. The controller has demonstrated the ability to prevent rollovers by actively using the vertical forces in the air-suspension system for realistic driving scenarios. However, the system also showed limitations in handling fast alternating maneuvers, such as lane changes.

The findings suggest that integrating active suspension with predictive control can improve the dynamic stability and safety of heavy-duty trucks.