汽车半主动悬架RBF模糊滑模控制器设计及仿真

Design and simulation analysis of RBF fuzzy sliding mode controller for automotive semi-active suspension

为提高半主动悬架系统控制效率,构建半主动悬架磁流变阻尼器(MRD)的力学模型,表征其控制状态,Sigmoid模型的各项参数悬架系统表现出明显的不确定性变化规律。为增强滑模运动控制性能,构建优化滑模控制,采用模糊控制方式构建精确数学模型。利用天棚阻尼系统建立滑模控制器,选择径向基函数(RBF)神经网络与模糊控制优化控制系统,开展仿真测试。研究结果表明:模糊滑模控制条件下,簧载速度与加速度形成比RBF滑模控制方式更大的峰值,虽然存在一定的偏差,但总体变化趋势一致,对比发现RBF神经网络达到了最优控制效果。RBF滑模控制和模糊滑模控制方式,均比被动悬架的簧载速度及加速度发生了明显减小,悬架振动速度基本相近。该研究可拓展到其他交通运输设备上,具有较好的实际应用价值。

In order to improve the efficiency of the semi-active suspension system control,for the semi-active suspension magneto rheological damper(MRD)mechanics model are established and the control of the state of characterization,identification of Sigmoid model the various parameters of the suspension system showed obvious uncertainty change rule,in order to strengthen the sliding mode motion control performance,build the optimal sliding mode control,fuzzy control method to build accurate mathematical model.The sliding mode controller was established by using the ceiling damping system,and the RBF neural network and fuzzy control optimization control system were selected to carry out the simulation test.The research results show that the sprung velocity and acceleration under fuzzy sliding mode control form a larger peak value than that under RBF sliding mode control.Although there is a certain deviation,the overall change trend is consistent,and the comparison shows that the RBF neural network achieves the optimal control effect.Compared with passive suspension,RBF sliding mode control and fuzzy sliding mode control have significantly reduced sprung velocity and acceleration,while suspension vibration velocity is basically similar.This research can be extended to other transportation equipment and has good practical application value.