Variable weight MPC active suspension control based on visual road surface recognition

This study proposes a variable weight model prediction control strategy based on road surface recognition (VW-MPC-RSR), aiming to address the problems of road recognition timeliness and model prediction control (MPC) weight adjustment under multi-working conditions. The road surface recognition is achieved by utilizing the VIT-B/16 network model. A test set and a continuous frame video are used to verify the effectiveness of the road recognition strategy. At the same time, a 2-DOF suspension dynamics model is established, and the variable weight MPC control strategy based on road surface recognition is designed. The optimal weights of six road types are determined using a cross-comparison method, and the corresponding weight-switching strategy is formulated. The performance of VW-MPC-RSR active suspension, MPC active suspension, and passive suspension is compared by simulation. The results show that the road surface recognition strategy based on the VIT network can recognize various road states efficiently and accurately. In addition, the suspension control strategy designed in this paper can adjust the control parameters in real time according to road conditions and significantly improve the overall performance of suspensions.