Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion stat...

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Autores principales:	Wan Wenkang, Feng Jingan, Song Bao, Li Xinxin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	distributed drive interacting multiple model square root cubature Kalman filter state estimation vehicle system Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/191db2bbedfc4b8f858bd24b2056eb6c
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id	oai:doaj.org-article:191db2bbedfc4b8f858bd24b2056eb6c
record_format	dspace
spelling	oai:doaj.org-article:191db2bbedfc4b8f858bd24b2056eb6c2021-11-25T16:37:46ZVehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter10.3390/app1122107722076-3417https://doaj.org/article/191db2bbedfc4b8f858bd24b2056eb6c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10772https://doaj.org/toc/2076-3417The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.Wan WenkangFeng JinganSong BaoLi XinxinMDPI AGarticledistributed driveinteracting multiple modelsquare root cubature Kalman filterstate estimationvehicle systemTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10772, p 10772 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	distributed drive interacting multiple model square root cubature Kalman filter state estimation vehicle system Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	distributed drive interacting multiple model square root cubature Kalman filter state estimation vehicle system Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Wan Wenkang Feng Jingan Song Bao Li Xinxin Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
description	The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.
format	article
author	Wan Wenkang Feng Jingan Song Bao Li Xinxin
author_facet	Wan Wenkang Feng Jingan Song Bao Li Xinxin
author_sort	Wan Wenkang
title	Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
title_short	Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
title_full	Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
title_fullStr	Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
title_full_unstemmed	Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter
title_sort	vehicle state estimation using interacting multiple model based on square root cubature kalman filter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/191db2bbedfc4b8f858bd24b2056eb6c
work_keys_str_mv	AT wanwenkang vehiclestateestimationusinginteractingmultiplemodelbasedonsquarerootcubaturekalmanfilter AT fengjingan vehiclestateestimationusinginteractingmultiplemodelbasedonsquarerootcubaturekalmanfilter AT songbao vehiclestateestimationusinginteractingmultiplemodelbasedonsquarerootcubaturekalmanfilter AT lixinxin vehiclestateestimationusinginteractingmultiplemodelbasedonsquarerootcubaturekalmanfilter
_version_	1718413064084127744

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

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