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State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual unscented particle filter Algorithm

Chinese Journal of Mechanical Engineering 2024年第1期37卷 99-113页

作者： Zixu Wang Chaoning Chen Quan Jiang Hongyu Zheng Chuyo Kaku State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China Jiangsu Chaoli Electric Co.Ltd. Zhenjiang 212321China

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

关键词： Drive-by-wire chassis vehicle Vehicle state estimation Dual unscented particle filter Tire force estimation unscented particle filter

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Ground Moving Target Tracking with VS-IMM Using Mean Shift unscented particle filter

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Chinese Journal of Aeronautics 2011年第5期24卷 622-630页

作者： GAO Caicai CHEN Wei School of Electronics and lnformation Engineering Beihang University Beijing 100191 China

In order to track ground moving target, a variable structure interacting multiple model （VS-IMM） using mean shift unscented particle filter （MS-UPF） is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication （GMTI） radar is employed in three common simulation scenarios of ground target： entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error （RMSE） and average normalized estimation error squared （ANEES）, are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.

关键词： ground moving target tracking mean shift unscented particle filter hide model road information variable structure interacting multiple model

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Self-localization Algorithm of Mobile Robot Based on unscented particle filter

Self-localization Algorithm of Mobile Robot Based on Unscent...

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第37届中国控制会议

作者： Dairong Li Qijun Chen Zhiying Zeng Tongji University

Based on Competition of RoboCup Standard Platform League,this paper will do some researches about how to use the data of odometer and the images from camera of Nao robot to realize self-localization.First,this paper defines the world coordinate system and the robot coordinate system.Based on the coordinate systems,this paper defines the state variables,presents the state equations and observation equations of the dynamic system,and describes how to calculate the observation information of the robot pose through recognition information of the camera.Then taking real scene of Robocup into account,it introduces unscented Kalman filter which is put into the particle filter framework to get unscented particle filter（UPF）.The UPF algorithm is used to realize self-localization.Finally,this localization algorithm is implemented on Nao robot through a series of simulation experiments.The experiment shows that the efficiency,accuracy,stability of UPF algorithm is much higher than the particle filter（PF） algorithm,which proves the superiority of unscented particle filter algorithm in self-localization of mobile robot.

关键词： Nao robot State Estimation Self-Localization unscented Kalman filter unscented particle filter

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A new FastSLAM algorithm based on the unscented particle filter

A new FastSLAM algorithm based on the Unscented Particle Fil...

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第30届中国控制与决策会议

作者： Jie Luo Lei Sun Yunhui Jia School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology

With the development of the artificial intelligence technology,mobile robots have been widely applied to various fields,such as human-computer interaction and self-cruise.Simultaneous localization and mapping is a key to realize the intelligent navigation.Based on the previous research fruits,this paper listed a construction method using lidar,and introduced the SLAM algorithm of the unscented particle filter to make the constructed maps more precise.In this method,the lidar is first used to scan the indoor environment,and then the collected data is adopted to fit the indoor map.Finally,the accurate information is shown in the experimental section.

关键词： SLAM unscented particle filter navigation lidar

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An Integrated Method for Lithium-ion Batteries Remaining Useful Life Prediction Based on unscented particle filter and Relevance Vector Machine

An Integrated Method for Lithium-ion Batteries Remaining Use...

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第40届中国控制会议

作者： Meng Jiang Zhenxing Liu Yong Zhang Jie He Yujie Chen School of Information Science and Engineering Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education

Accurate remaining useful life（RUL） prediction for lithium-ion batteries plays a crucial role to prevent accidents from happening. In this paper, we proposed an hybrid method based on unscented particle filter（UPF） and relevance vector machine（RVM）, where the genetic algorithm optimized support vector regression（GA-SVR） was uesd to correct the error generated in the prediction process. RVM is first used to extract feature points and initialize degradation model. In order to implement the RUL prediction, UPF is employed to extrapolate trends of the battery degradation capacity, where the unscented Kalman filter（UKF）is applied to generate a proposal distribution as an importance function for PF. In addition, GA-SVR is uesd to correct prediction error. Finally, to verify the effect of the proposed UPF-GASVR method with higher prediction accuracy, NASA battery dataset was used to carry out the test and compare with some classical methods.

关键词： Remaining useful life prediction unscented particle filter Relevance vector machine Genetic algorithm Support vector regression

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Improved FastSLAM System Using Artificial Fish-Swarm Optimized Distributed unscented particle filter

Improved FastSLAM System Using Artificial Fish-Swarm Optimiz...

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第36届中国控制会议

作者： Fujun Pei Hong Yan Mingju Zhu School of Electronic Information & Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781538629185

particle impoverishment is inevitably induced because of the random particles prediction and resampling applied in particle filter,especially in SLAM problem with a large number of dimensions.To overcome these limitations,an improved FastSLAM system using artificial fish-swarm optimized distributed unscented particle filter（AFSO-DUPF） is developed in this paper.The main contribution of this paper is the derivation of AFSO-DUPF by improving the resampling method and reduce the computation quantity of distributed unscented particle filter（DUPF）.First,the intelligent optimized resampling method basing on artificial fish-swarm algorithm was used to overcome degeneracy and losing particle density simultaneously.Second,to overcome the higher computational complexity problem,the marginalized distribution simplified method was used in sampling particles and calculating weights of DUPF.Compare to DUPF FastSLAM,the AFSO-DUPF FastSLAM can maintain the diversity of particle and avoid inconsistency for long time periods.The simulation experiment results demonstrate that the improved FastSLAM system has higher estimation accuracy and reduce the computational complexity.

关键词： Distributed FastSLAM unscented particle filter Artificial Fish-Swarm Resampling

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Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

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Chinese Journal of Electronics 2021年第1期30卷 26-35页

作者： JIA Jianfang WANG Keke PANG Xiaoqiong SHI Yuanhao WEN Jie ZENG Jianchao School of Electrical and Control Engineering North University of China School of Data Science and Technology North University of China

The prediction of Remaining useful life(RUL) and the estimation of State of health(SOH) are extremely important issues for operating performance of Lithium-ion(Li-ion) batteries in the Battery management system(BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network(WNN) with unscented particle filter(UPF)model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform(DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries’ lifespan.

关键词： Lithium-ion batteries Multi-scale prediction Wavelet neural network unscented particle filter Remaining useful life State of health

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Information Fusion Passive Location filtering Algorithm 25th

Information Fusion Passive Location Filtering Algorithm

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第25届中国控制与决策会议

作者： Mao Lin Yang Da wei Department of Electronic Engineering Heilongjiang University College of Information and Communication Engineering Harbin Engineering University

ISBN: (纸本)9781467355339

In single-station bearings-only passive location system, the localization precision and system stability are always not satisfied with the requirements of the modern electronic warfare because only angle measurement signal is presented. This paper applied to solve the state estimation problem of the passive location system using information fusion technique, improves the estimation precision and increases the system stability without adding any measurement stations. Furthermore, using scalar-weighted information fusion criterion, an information fusion passive location filter is proposed for multi-sensor system with correlated observation noises. And we use the unscented particle filter to calculate the local state estimation values. Finally, a three-sensor simulation example validates the performance of our proposed algorithm.

关键词： Information Fusion Passive Location unscented particle filter

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The Improved Algorithm of UPF in Spacecraft Autonomous Optical Navigation

The Improved Algorithm of UPF in Spacecraft Autonomous Optic...

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第二十七届中国控制会议

作者： Du Junwei 1,Sui Shulin 2,Yao wenlong 2 1.College of Information Science and Technology,Qingdao University of Science & Technology,Qingdao 266042,P.R.China 2.Institute of Autonomous Navigation and Intelligent Control,Qingdao University of Science and Technology,Qingdao 266042,P.R.China

An autonomous optical navigation scheme for cruise phase is presented using the star light data and body edge data which is measured by star sensor and optical navigation camera.And the relative position for probes is acquired from the star light data and body edge data.The observation information is pretreated by wavelet analysis and filter the high frequency noise,then uses the wavelet to recompose the signal to gain smooth parameters value and makes a process of UPF（unscented particle filter） based on these work,so as to reduce the variance of importance weights.The real time orbit for probes is determined by using these.The arithmetic integrates wavelet analysis and UPF to improve the precise and reliability of the autonomous optical navigation system.Simulation result shows that the improved UPF algorithm is not only more accurate but also has higher rate of convergence compared with the UPF.

关键词： Autonomous optical navigation unscented particle filter Kalman filter Wavelet analysis

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