基于G1-ICS算法的磁流变半主动悬架最优控制  

Optimal control of magnetorheological semi-active suspension based on G1-ICS

作　　者：胡启国[1] 苟中华 HU Qi-guo;GOU Zhong-hua(School of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074

出　　处：《磁性材料及器件》2022年第6期59-66,共8页Journal of Magnetic Materials and Devices

基　　金：国家自然科学基金资助项目(51375519);重庆市基础科学与前沿技术研究专项项目(cstc2015jcyjBX0133)。

摘　　要：根据车辆行驶动力学特性,建立了包含座椅的三自由度1/4半主动悬架模型,采用BP神经网络建立了磁流变阻尼器逆向模型。针对磁流变半主动悬架各项性能指标,以座椅性能为主要优化目标,应用最优控制理论设计了线性二次型最优控制器。分别采用序关系分析法(G1法)以及与改进布谷鸟搜索算法(G1-ICS)相结合的方法确定了各性能指标加权系数,并在Matlab/Simulink中对磁流变半主动、被动悬架模型进行了仿真验证。结果表明:与被动悬架相比,两种算法均能在一定程度上改善悬架系统的性能,尤其是悬架系统座椅性能;同时,采用两种方法相结合的方式能更进一步提高磁流变半主动悬架性能。According to the dynamic characteristics of the vehicle,a three-degree-of-freedom 1/4 semi-active suspension model with seats was established,and the inverse model of magnetorheological(MR)damper was established by BP neural network.According to the performance indexes of the MR semi-active suspension,the seat performance was the main optimization objective,and the linear quadratic optimal controller was designed by using the optimal control theory.The weighted coefficients of each performance index were determined by using the method of order relation analysis(G1 method)and the method combined with the improved cuckoo algorithm(G1-ICS),and the simulation verification of the MR semi-active and passive suspension models was carried out in Matlab/Simulink.The results show that compared with the passive suspension,both algorithms can improve the performance of the suspension system to a certain extent,especially the seat performance of the suspension system.At the same time,the performance of MR semi-active suspension can be further improved by combining the two methods.

关 键 词：磁流变阻尼器 半主动悬架 G1-ICS BP神经网络 逆向模型 最优控制 

分 类 号：U463.33[机械工程—车辆工程]