The Model Predictive Control of Dual-Chamber Active Air Suspension Based on Road Preview

Adverse weather conditions such as rain and snow, as well as heavy load transportation, can cause varying degrees of damage to road surfaces, and untimely road maintenance often results in potholes. Perception sensors equipped on intelligent vehicles can identify road surface conditions in advance, allowing each wheel’s suspension to actively adjust based on the road information. This paper presents an active suspension control strategy based on road preview information, utilizing a newly designed dual-chamber active air suspension system. It addresses the issue of point cloud stratification caused by vehicle body vibrations in onboard LiDAR data. The point cloud is processed through segmentation, filtering, and registration to extract real-time road roughness information, which serves as preview information for the suspension control system. The MPC algorithm is applied to actively adjust the nonlinear stiffness and damping of the suspension’s dual-chamber air springs, enhancing suspension response speed and accuracy. The effectiveness of the active air suspension MPC method under real-time road sensing is validated through co-simulation using Matlab/Simulink and CarSim. Vehicle ride comfort is used as the evaluation criterion, demonstrating that the integrated sensing and control approach can effectively reduce vehicle.