二维码（扫一下试试看！） | | 汽车电控液压制动系统动力学建模及性能研究 | | Dynamics Modeling and Performance Analysis for Electro Hydraulic Braking System | | 投稿时间：2018-06-15 | | DOI：10.15918/j.tbit1001-0645.2018.增刊1.026 | | 中文关键词:电控液压制动系统无迹卡尔曼滤波估计粒子群优化时变系统 | | English Keywords:electro hydraulic braking systemunscented Kalman filter estimationparticle swarm optimizationtime-varying system | | 基金项目: | | | 摘要点击次数:697 | | 全文下载次数:294 | | 中文摘要: | | 为提高估计电控液压制动系统的压力估计精度，针对其工作特性提出一种基于粒子群优化的UKF算法的汽车电控液压制动系统动力学建模方法.该算法根据液压制动系统的工作特性将压力估计问题转化为多维参数优化的问题，应用UKF对汽车电控液压制动系统进行压力估计，根据该液压系统的强非线性及时变系统特性引入液压系统指数参数，压力变化参数差，及测量轮缸压力作为状态量，引入粒子群算法根据目标函数，对UKF中的参数及观测噪声，过程噪声进行迭代寻优.实验数据对比，验证该算法参数估计的精确性及实时性.研究结果对液压制动系统以及整个液压系统的研究都具有指导意义. | | English Summary: | | To improving the estimate accuracy of pressure of electro-hydraulic braking (EHB) system, a novel dynamics modeling method was proposed based on particle swarm optimization and unscented Kalman filter (UKF) arithmetic. In the arithmetic, according to the work characteristic of EHB system, the pressure estimation was translated into optimizing multidimensional parameter. The nonlinear differential equations were derived in terms of dynamic characteristics of electro-hydraulic braking system. Then, the UKF was applied to estimate the time-varying parameters of the model with an objective function. To obtain accurately the time-varying parameters, the measurement noise, process noise, and parameters in the unscented Kalman filter were optimized with the particle swarm algorithm. Comparing with the experimental data in several braking cases, the results validate the effectiveness and accuracy of the novel method. Therefore, the proposed model can not only provide a theoretical basis for the design of a hydraulic braking system but also help realize the chassis integrated control of electric vehicles. | | 查看全文查看/发表评论下载PDF阅读器 | |
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