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《Machinery Design & Manufacture》 2015-10
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The Research of State and Parameter Estimation Under Driving Situation Based on Auxiliary Particle Filter Method

BAO Rui-xin;Edoardo Sabbioni;School of Mechanical Engineering,Liaoning SHIHUA University;Department of Mechanical Engineering;  
The estimation of vehicle state and key parameters estimation has important significance for the vehicle active safety control and new structure design. A control algorithm using auxiliary particle filter to estimate the vehicle driving state and parameters was suggested. The algorithm was based on vehicle dynamics system containing constant noise and non-linear model,and Runge-Kuttta method was used to simulate the model. An assigned variable was brought into the algorithm. With two rounds weighted processes,the weights of the samples changed more smoothly. The local state and parameters were identified together. The comparison results demonstrated that the proposed algorithm can estimate vehicle state and key parameters accurately and improve the accuracy of standard particle filter.
【Fund】: 2013年获得国家留学基金委资助 目前在意大利米兰理工大学从事博士后研究工作;; 国家留学基金资助;; 米兰理工大学企业合作基金资助
【CateGory Index】: U463.6
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