Active suspension control strategy of heavy rescue vehicle based on multi-sensor information fusion

This study proposes the active suspension control strategy of heavy rescue vehicles based on multi-sensor information fusion to address the problem of poor ride comfort and stability of heavy rescue vehicles on unstructured pavement. Firstly, an attitude fusion algorithm with acceleration compensation is designed. According to the driving speed, the appropriate cut-off frequency of acceleration filter is selected, and the extended Kalman filter is designed to complete the optimal estimation of position and attitude information. Secondly, the vehicle nine degree of freedom model is established, and the inverse matrix of attitude control is solved to obtain the suspension actuator output. Lastly, an active suspension control strategy based on multi-sensor information fusion is designed. The body attitude information after the information fusion is used for the control of active suspension system to realize the real-time adjustment of body attitude. Test results show that the root mean square values of vertical displacement, pitch angle, and roll angle under active suspension are reduced by 37.01%, 26.96%, and 38.90%, respectively, compared with those under in passive suspension. This reduction improves the ride comfort and stability of the heavy rescue vehicles on unstructured pavement.