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作 者:孙建民 陈昊 杨世虎 栗雷敬 Sun Jianmin;Chen Hao;Yang Shihu;Li Leijing(School of Mechatronics and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Service Performance Assurance of Urban Rail Transit Vehicles,Beijing 100044,China)
机构地区:[1]北京建筑大学机电与车辆工程学院,北京100044 [2]城市轨道交通车辆服役性能保障北京市重点实验室,北京100044
出 处:《电子测量技术》2024年第17期47-53,共7页Electronic Measurement Technology
基 金:国家自然科学基金(51605023);北京市教委科研计划项目(SQKM201810016015);北京建筑大学研究生创新项目(PG2024140)资助。
摘 要:面对插电式汽车电子节气门系统存在非线性、参数不确定性以及进气气流扰动等问题,提出了一种基于粒子群算法(PSO)优化非奇异快速终端滑模控制(NFTSMC)的方法,以此提高节气门响应速度和跟随特性。首先考虑节气门零部件参数不确定性因素以及进气扰动,将系统扰动进行范围估计,并馈入到控制器中。然后利用PSO算法对NFTSMC进行参数整定,并根据李雅普诺夫第二定理进行稳定性证明。设计了插电式汽车阶跃信号和城市道路工况,PSO-NFTSMC相比于PSO-SMC响应时间缩短了82 ms,最大幅值跟随误差由0.32°降低到0.005°,最大误差值由0.5°减小到0.004°。证明了本文所提出的在PSO-NFTSM控制下插电式汽车电子节气门系统响应速度更快,跟随误差更加符合预设效果。该方法的提出与应用为提高插电式汽车发动机的燃烧和排放效率,以及提升车辆动力性能提供了一定参考。In order to improve the throttle response speed and the following characteristics,a particle swarm optimization(PSO)based method was proposed to optimize the non-singular fast terminal sliding mode control(NFTSMC)in plug-in automotive electronic throttle system.Firstly,considering the parameter uncertainty of throttle components and the intake disturbance,the system disturbance is estimated and fed into the controller.Then the parameters of NFTSMC are adjusted by PSO algorithm,and the stability is proved according to Lyapunov′s second theorem.Compared with PSO-SMC,the response time of PSO-NFTSMC is reduced by 82 ms,the maximum value following error is reduced from 0.32°to 0.005°,and the maximum value of error is reduced from 0.5°to 0.004°.It is proved that the proposed plug-in automotive electronic throttle system under the control of PSO-NFTSM has faster response speed and more consistent with the preset error.The proposal and application of this method provide a reference for improving the combustion and emission efficiency of plug-in vehicle engines and improving the dynamic performance of vehicles.
关 键 词:插电式汽车 电子节气门 非线性 非奇异快速终端滑模 粒子群算法
分 类 号:U467.3[机械工程—车辆工程] TP273[交通运输工程—载运工具运用工程] TN47[交通运输工程—道路与铁道工程]
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