Estimation of Road Adhesion Coefficient Based on Tire Aligning Torque Distribution

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Biao Ma ◽  
Chen Lv ◽  
Yahui Liu ◽  
Minghui Zheng ◽  
Yiyong Yang ◽  
...  
Keyword(s):  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Estimation Method ◽  
Iteration Algorithm ◽  
Active Safety ◽  
Torque Distribution ◽  
Safety Control ◽  
Adhesion Coefficient ◽  
Road Friction ◽  
Vehicle Active Safety

Road adhesion coefficient is an important parameter in vehicle active safety control system. Many researchers estimate road adhesion coefficient by total tire self-aligning torque (SAT, also called front-axle aligning torque), which obtains the average road adhesion coefficient of front wheels, thus leading large estimation error. In this paper, a novel estimation of road adhesion coefficient based on single tire SAT, which is obtained by tire aligning torque distribution, is brought forward. Due to the use of SAT, the proposed estimation method is available in steering only condition. The main idea of the proposed method is that road adhesion coefficient is estimated by single tire SAT instead of total tire SAT. The single tire SAT is closer to real tire torque state, and it can be obtained by aligning torque distribution, which makes use of the ratio for the aligning torque of front-left wheel and front-right wheel. Tire sideslip angle used in torque distribution is estimated by unscented Kalman filter (UKF). Two coefficients, including front-left and front-right tire-road friction coefficients, are estimated by iteration algorithm form single tire SAT. The final road adhesion coefficient is determined by a coefficient identification rule, which is designed to determine which tire-road friction coefficient as the final road adhesion coefficient. Both simulations and tests that use gyroscope/lateral accelerometer/global position system (GPS)/strain gauge are conducted, to validate the proposed methodology that can provide accurate road adhesion coefficient to vehicle active safety control.

scholarly journals Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

2020 ◽  
Vol 10 (4) ◽  
pp. 1343 ◽  
Author(s):  
Jianfeng Chen ◽  
Congcong Guo ◽  
Shulin Hu ◽  
Jiantian Sun ◽  
Reza Langari ◽  
...  
Keyword(s):  
Robust Estimation ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Gaussian White Noise ◽  
Active Safety ◽  
Safety Control ◽  
Guaranteed Estimation ◽  
Set Membership ◽  
Vehicle Motion ◽  
Vehicle Active Safety

Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle’s longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

Vehicle Lateral and Longitudinal Velocity Estimation Using Coupled EKF and RLS Methods

2010 ◽  
Vol 29-32 ◽  
pp. 851-856 ◽  
Author(s):  
Liang Chu ◽  
Yong Sheng Zhang ◽  
Yan Ru Shi ◽  
Ming Fa Xu ◽  
Yang Ou
Keyword(s):  
Control Systems ◽  
Control Algorithm ◽  
Longitudinal Velocity ◽  
Velocity Estimation ◽  
Recursive Least Squares ◽  
Active Safety ◽  
Vehicle Model ◽  
Safety Control ◽  
The Cost ◽  
Vehicle Active Safety

In order to meet the cost requirement of lateral and longitudinal velocity measured directly in vehicle active safety control systems, based on 3-DOF vehicle model and the Recursive Least Squares (RLS) which can identify the tire cornering stiffness online, a control algorithm using Extended Kalman Filter(EKF) to estimate lateral and longitudinal velocity is proposed. The estimation values are compared with simulator values from CarSim. The compared results demonstrated that the proposed algorithm could estimate the lateral and longitudinal velocity accurately and robustly.

A review of vehicle active safety control methods: From antilock brakes to semiautonomy

2020 ◽  
pp. 107754632094865
Author(s):  
Arash Hosseinian Ahangarnejad ◽  
Ahmad Radmehr ◽  
Mehdi Ahmadian
Keyword(s):  
Active Safety ◽  
Traction Control ◽  
Real Time Processing ◽  
Safety Control ◽  
Potential Safety ◽  
Yaw Stability ◽  
Active Safety Systems ◽  
Safety Systems ◽  
Vehicle Active Safety ◽  
Antilock Brakes

A comprehensive review of technologies and approaches for active safety systems designed to reduce ground vehicle crashes, as well as the associated severity of injuries and fatalities, is provided. Active safety systems are commonly referred to as systems that can forewarn a driver of a potential safety hazard, or automatically intervene to reduce the likelihood of an accident without requiring driver intervention. The data from naturalistic drivers has shown that such systems are instrumental in improving vehicle safety in various conditions, particularly at higher speeds and under adverse road conditions. The increased integration of sensors, electronics, and real-time processing capabilities has served as one of the critical enabling elements in the widespread integration of active safety systems in modern vehicles. The emphasis is placed on control approaches for active safety systems and their progression over the years from antilock brakes to more advanced technologies that have nearly enabled semiautonomous driving. A review of key active safety control approaches for antilock braking, yaw stability, traction control, roll stability, and various collision avoidance systems is provided.

scholarly journals On co-estimation and validation of vehicle driving states by a UKF-based approach

2021 ◽  
Vol 12 (1) ◽  
pp. 19-30
Author(s):  
Peng Wang ◽  
Hui Pang ◽  
Zijun Xu ◽  
Jiamin Jin
Keyword(s):  
State Estimation ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
Control Plant ◽  
Estimation Method ◽  
Lateral Acceleration ◽  
Accurate Information ◽  
Sideslip Angle ◽  
Safety Control ◽  
Vehicle State Estimation

Abstract. It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.

scholarly journals Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yingjie Liu ◽  
Qijiang Xu ◽  
Jingxia Sun ◽  
Fapeng Shen ◽  
Dawei Cui
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Vehicle Dynamics ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Tire Model ◽  
Dynamics Model ◽  
Safety Control ◽  
Vehicle State Estimation ◽  
Vehicle Active Safety

Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.

scholarly journals Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3123 ◽  
Author(s):  
Quan Sun ◽  
Hong Zhang ◽  
Jianrong Zhang ◽  
Wentao Ma
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Measurement Data ◽  
Estimation Method ◽  
State Of Charge ◽  
Computing Technique ◽  
Soc Estimation ◽  
Lower Estimation ◽  
Non Gaussian

As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

scholarly journals Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257849
Author(s):  
Muhammad Wasim ◽  
Ahsan Ali ◽  
Mohammad Ahmad Choudhry ◽  
Faisal Saleem ◽  
Inam Ul Hasan Shaikh ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Model Uncertainty ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Wind Disturbance ◽  
Model Uncertainties ◽  
Nonlinear Simulation ◽  
Lumped Model ◽  
Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

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