scholarly journals Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information

2019 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Te Chen ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Coupling Model ◽  
Wheel Speed ◽  
Measurement Information ◽  
Sideslip Angle ◽  
Safety Control ◽  
Coupling Relationship ◽  
Cubature Kalman Filter

Accurate and reliable estimation information of sideslip angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of sideslip angle estimation of an autonomous vehicle, a sideslip angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive sideslip angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

scholarly journals Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Li ◽  
Jiaxu Zhang
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Interacting Multiple Model ◽  
Square Root ◽  
Tire Model ◽  
Multiple Model ◽  
Sideslip Angle ◽  
Safety Control ◽  
Cubature Kalman Filter ◽  
Vehicle Dynamic Model

Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF), which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF), which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM) approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

scholarly journals Design of an Interacting Multiple Model-Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
SuoJun Hou ◽  
Wenbo Xu ◽  
Gang Liu
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Lateral Acceleration ◽  
Accurate Estimation ◽  
Interacting Multiple Model ◽  
Tire Model ◽  
Multiple Model ◽  
Vehicle Model ◽  
Sideslip Angle ◽  
Cubature Kalman Filter

Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

scholarly journals Research on sensorless control method of permanent magnet synchronous motor based on iterated cubature Kalman filter

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989027
Author(s):  
Shi Peicheng ◽  
Wang Chen ◽  
Zhang Rongyun ◽  
Wang Suo
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Method ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Motor Speed ◽  
Rotor Position ◽  
Cubature Kalman Filter

Aiming at the problems of high cost, increased volume, low reliability, and environmental interference caused by sensor installation on permanent magnet synchronous motor, estimation method for motor speed and rotor position is proposed based on iterated cubature Kalman filter algorithm and applied to permanent magnet synchronous motor sensorless control. First, discrete mathematical model of permanent magnet synchronous motor in α-β coordinate system is established. Then, based on cubature Kalman filter and iterated cubature Kalman filter, simulation model of sensorless vector control system with dual closed-loop of permanent magnet synchronous motor speed and current is established. Also, simulation verification of two working conditions with given rotation speed and load is carried out. Finally, hardware experimental verification platform is built based on TMS320F28335 chip. Both simulation analysis and experimental results show that iterated cubature Kalman filter application to sensorless control of permanent magnet synchronous motor demonstrates good anti-load variation interference, stable motor operation, high motor speed and rotor position estimation accuracy, which suits the application with high requirement for precise motor control and mean important reference value and promotion significance.

scholarly journals Randomly Weighted CKF for Multisensor Integrated Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Hua Zong ◽  
Zhaohui Gao ◽  
Wenhui Wei ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Method ◽  
Experimental Results ◽  
Integrated Systems ◽  
System State ◽  
System Error ◽  
Random Weights ◽  
Cubature Kalman Filter ◽  
Dynamic Modification

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

scholarly journals Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

2016 ◽  
Vol 70 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Chien-Hao Tseng ◽  
Sheng-Fuu Lin ◽  
Dah-Jing Jwo
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Measurement Information ◽  
Gps Navigation ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
M Estimation ◽  
Non Gaussian ◽  
Distribution Assumption

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

scholarly journals Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4335 ◽  
Author(s):  
Yuepeng Shi ◽  
Xianfeng Tang ◽  
Xiaoliang Feng ◽  
Dingjun Bian ◽  
Xizhao Zhou
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Measurement Noise ◽  
Variance Estimators ◽  
Unknown Variance ◽  
Weighted Fusion ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
The Stability

This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the variance of the measurement noise. On this basis, a hybrid adaptive cubature Kalman filtering structure is proposed. Furthermore, the information filter of the hybrid adaptive cubature Kalman filter is also studied, and the stability and filtering accuracy of the filter are theoretically discussed. The final simulation examples verify the validity and effectiveness of the hybrid adaptive cubature Kalman filtering methods proposed in this paper.

Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter

2017 ◽  
Vol 232 (16) ◽  
pp. 3024-3033
Author(s):  
Tao Zhang ◽  
Xiang Xu ◽  
Zhicheng Wang
Keyword(s):  
Kalman Filter ◽  
Recursive Algorithm ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Previous Method ◽  
Spacecraft Attitude ◽  
Original Matrix ◽  
Cubature Kalman Filter ◽  
The Matrix ◽  
Gyro Drift

An interlaced matrix Kalman filter, which is based on vector observations and gyro measurements, is proposed for spacecraft attitude estimation in this paper. It combines the matrix Kalman filter and cubature Kalman filter to estimate spacecraft attitude and gyro drift bias, respectively. The defects of the original matrix Kalman filter, which could only estimate the attitude parameters of spacecraft, are addressed by the proposed interlaced matrix Kalman filter. In addition, the dimensions of cubature Kalman filter for conventional attitude estimation method are reduced by the designed recursive algorithm. It is noted that the two filters are not independent with each other. Firstly, the attitude quaternion of spacecraft is estimated by the modified matrix Kalman filter. Then, the estimated quaternion is input for the recursive cubature Kalman filter, which is used to estimate the gyro drift bias. Finally, the estimated gyro drift bias is compensated for the measurements of the gyros. Therefore, the precision of the estimated attitude of spacecraft is improved by the interacting process of the modified matrix Kalman filter and recursive cubature Kalman filter. A simulation test is designed to verify the advantage of the proposed method by comparing with the previous method, and the results indicate that the proposed algorithm has better performance on convergence rate and stability.

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