基于模糊神经网络的二自由度主动悬架滑模控制系统设计

主动悬架滑模控制系统受到行驶路面影响,车体震动加速度功率谱密度与实际密度不一致,导致系统控制效果不佳,提出基于模糊神经网络的二自由度主动悬架滑模控制系统设计;基于二自由度硬件结构,在主动悬架液压伺服系统中安装蓄能器,减小空间内存;采用磁流变阻尼器,调整电流大小控制阻尼;设计自适应减振座椅悬架并计算阻尼值,实现了主动悬架的控制;考虑路面不规则度,构建二自由度主动悬架滑模控制模型,依据牛顿定律计算悬架弹性元件受力;采用模糊控制规则校正控制误差,采用模糊神经网络设计主动悬架滑膜控制方案,经迭代处理得到满足优化要求的解,实现滑膜控制;由试验结果可知,车体震动加速度功率谱密度与实际密度一致,从最初的0 ms^(-2)到最终的1.5 ms^(-2),具有良好控制效果,确保乘车的舒适性。

Design of Sliding Mode Control System for 2-DOF Active Suspension Based on Fuzzy Neural Network

The active suspension sliding mode control system is affected by the driving road, and the vehicle body vibration acceleration power spectrum density is inconsistent with the actual density, resulting in poor system control effect. A two-degree-of-freedom active suspension sliding mode control system design based on fuzzy neural network is proposed. Based on the two-degree-of-freedom hardware structure, an accumulator is installed in the hydraulic servo system of the active suspension to reduce the space memory. The magnetorheological damper is used to adjust the current to control the damping. Design an adaptive damping seat suspension and calculate the damping value to realize the control of the active suspension. Considering the irregularity of the road surface, a two-degree-of-freedom active suspension sliding mode control model is constructed, and the force of the suspension elastic elements is calculated according to Newton''s law. The fuzzy control rules are used to correct the control error, and the fuzzy neural network is used to design the active suspension sliding film control scheme. After iterative processing, the solution that meets the optimization requirements is obtained, and the sliding film control is realized. It can be seen from the test results that the power spectral density of the vehicle body vibration acceleration is consistent with the actual density. From the initial 0ms-2 to the final 1.5ms-2, it has a good control effect and ensures the comfort of the car.