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As the traditional control algorithm is over-dependent on accurate vehicle model in intelligent vehicle steering control, a human-simulated intelligent control method is proposed based on experienced driver steering characteristics. Intelligent vehicle unmanned steering system dynamics model and the driver model are set up. Through experienced drivers’ trial run experiment, the analysis is mainly conducted on the double lanes condition. After the transformation of coordinates on global positioning system (GPS) derivative, the path information of local coordinates is accessed. The ideal driver steering path is obtained through fuzzy C-means clustering algorithm. The human-simulated intelligent controller is designed. Characteristic model is established according to the ideal and practical steering angle deviation and the deviation rate. Besides, the corresponding control rules and control modality set are designed. The joint simulation under CarSim joint/Simulink environment shows that the humanoid steering controller designed in this paper has better tracking performance than the model predictive control. 相似文献
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在车辆电子稳定控制系统(ES P)的设计过程中,一般存在着因设计人员主观因素导致控制器性能下降的问题.基于模糊控制原理设计了ES P模糊控制器,通过遗传算法对模糊控制器的隶属度函数分布、比例系数,以及量化因子进行综合优化.为了提高对车辆稳定性判断的准确性,采用β相平面法判断车辆是否处于稳定状态.将优化前、后的ES P模糊控制器性能进行对比实验,结果表明,相比于未优化的ESP模糊控制器,在变速或匀速工况下,优化后的ESP模糊控制器都能显著提升车辆操纵稳定性,表现在能够良好的追踪车辆理想横摆角速度,保持车辆质心侧偏角在理想范围内;在前轮转向角阶跃工况下,横摆角速度稳态误差减小0.01 rad/s,达到稳态用时减少1 s左右,质心侧偏角稳态误差减小0.001 rad,达到稳态用时减少0.6 s左右,经过遗传优化后的ESP模糊控制器性能有所提升. 相似文献