Body attitude control strategy based on road level for heavy rescue vehicles

This paper proposes an attitude control strategy based on road level for heavy rescue vehicles. The strategy aims to address the problem of poor ride comfort and stability of heavy rescue vehicles in complex road conditions. Firstly, with the pressure of the suspension hydraulic cylinder chamber without a piston rod as the parameter, Takagi–Sugeno fuzzy controller classification and adaptive network-based fuzzy inference system controller classification are used to recognise the road level. Secondly, particle swarm optimisation is adopted to obtain the optimal parameters of the active suspension system of vehicle body attitude control under different road levels. Lastly, the parameters of the active suspension system are selected in accordance with the road level recognised in the driving process to improve the adaptive adjustment capability of the active suspension system at different road levels. Test results show that the root mean square values of vertical acceleration, pitch angle and roll angle of the vehicle body are reduced by 59.9%, 76.2% and 68.4%, respectively. This reduction improves the ride comfort and stability of heavy rescue vehicles in complex road conditions.