| Summary: | Wheel loader is off-road vehicle and works on uneven terrain, unexpected banks or steep slopes. In order to improve the ride and stability of the vehicle, this study mainly focuses on how to adjust the parameters of hydropneumatic suspension through the identification of road conditions. Firstly, the multibody model of a wheel loader with hydropneumatic suspension is developed by RecurDyn in a co-simulation with MATLAB/Simulink. Secondly, a method of road level recognition based on learning vector quantization neural network is proposed to accurately identify the level of roads on which the wheel loader travels. Then, the hydropneumatic suspension parameters are optimized by using the particle swarm algorithm. A fuzzy controller is established based on the optimized parameters of the hydropneumatic suspension to realize the active adjustment of the suspension parameters under different road levels and driving speeds. Finally, a virtual prototyping model is used to analyse the influence of the active adjustment of suspension parameters on the vertical vibration under different driving conditions. Results show that the fuzzy controller can reasonably adjust the parameters of hydropneumatic suspension according to the identified road condition and effectively reduce the vertical vibration of the wheel loader.
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