摘要
路面的激励作用会使车辆在行驶过程中产生颠簸和振动,严重影响汽车行驶平顺性和乘坐舒适性。建立了1/4汽车主动悬架数学模型,提出一种基于模糊神经网络的控制策略。该方法利用了模糊控制鲁棒性强和神经网络控制收敛速度快的特点,对系统参数进行实时在线调整;同时,以悬架动行程、车轮动载荷以及车身垂直加速度为衡量指标进行仿真分析和测试研究。结果表明,所提出的控制策略可以有效减小汽车在行驶中因路面激励作用而产生的振动,大幅改善了车辆操纵稳定性、汽车行驶平顺性及乘坐舒适性,鲁棒性强,有一定可借鉴意义。
The excitation of road surface causes the vehicle to produce bump and vibration in the traveling process, affects the automobile ride comfort. This study established a 1/4 car active suspension model, proposed a control strategy based on fuzzy neural network. The design made full use of strong robustness of fuzzy control and fast convergence of neural network, could real-time adjust system parameters. Tests and simulation analyses of suspension dynamic travel, wheel dynamic changes in load, and the vertical acceleration of the body research were carried out. The results show that the proposed control strategy can effectively reduce the vibration caused by road excitation and produce, improve vehicle handling stability, ride comfort, strong robust, has certain reference significance.
出处
《实验室研究与探索》
CAS
北大核心
2017年第5期44-47,共4页
Research and Exploration In Laboratory
基金
吉林省科技发展计划项目(201303040NY)
