Design of longitudinal vehicle velocity observer using fuzzy logic and Kalman filter

Liang, Chao; Chao, LiBo; Zhang, Yongsheng; Shi, Yanru

doi:10.1109/emeit.2011.6023772

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…The process validity was studied using simulation and test results in slippery road conditions. Chu et al designed an adaptive vehicle longitudinal velocity observer for the electronic stability control system [14]. The observer was designed based on fuzzy logic and the Kalman filter, and the observer's effectiveness was validated in a Carsim environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

et al. 2021

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This article proposes a novel longitudinal vehicle speed estimator for snowy roads in extreme conditions (four-wheel slip) based on low-cost wheel speed encoders and a longitudinal acceleration sensor. The tire rotation factor, η, is introduced to reduce the deviation between the rotation tire radius and the manufacturer’s marked tire radius. The Local Vehicle Speed Estimator is defined to eliminate longitudinal vehicle speed estimation error. It improves the tire slip accuracy of four-wheel slip, even with a high slip rate. The final vehicle speed is estimated using two fuzzy control strategies that use vehicle speed estimates from speed encoders and a longitudinal acceleration sensor. Experimental and simulation results confirm the algorithm’s validity for estimating longitudinal vehicle speed for four-wheel slip in snowy road conditions.

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Section: Introductionmentioning

confidence: 99%

A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

et al. 2021

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“…These algorithms include an adaptive filter [5] or on fuzzy logic connected with some filtration type or estimation method. For this purpose, the connection of the fuzzy logic and a Kalman filter [6], [7] is typically used. In some cases, the incremental encoder could be replaced by estimated velocity by sensorless vector control [8].…”

Section: Wheel Velocity and Train Velocitymentioning

confidence: 99%

Train velocity estimation method based on an adaptive filter with fuzzy logic

Pichlík

Zděnek

2017

Journal of Electrical Engineering

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The train velocity is difficult to determine when the velocity is measured only on the driven or braked locomotive wheelsets. In this case, the calculated train velocity is different from the actual train velocity due to slip velocity or skid velocity respectively. The train velocity is needed for a locomotive controller proper work. For this purpose, an adaptive filter that is tuned by a fuzzy logic is designed and described in the paper. The filter calculates the train longitudinal velocity based on locomotive wheelset velocity. The fuzzy logic is used for the tuning of the filter according to actual wheelset acceleration and wheelset jerk. The simulation results are based on real measured data on a freight train. The results show that the calculated velocity corresponds to the actual train velocity.

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“…Best et al 4 considered a real-time state estimation of vehicle handling dynamics by the extended adaptive KF. Chu et al 5 addressed fuzzy logic and KF for the longitudinal vehicle velocity observer. However, for the online and real-time estimation, KF has trouble in handling the nonlinear system estimation; the extended Kalman filter (EKF) needs first-order linearizaion of the nonlinear system, which would lose vehicle nonlinear dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Huang

Duan

Taheri

et al. 2015

Advances in Mechanical Engineering

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Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

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Design of longitudinal vehicle velocity observer using fuzzy logic and Kalman filter

Cited by 4 publications

References 3 publications

A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions

Train velocity estimation method based on an adaptive filter with fuzzy logic

A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

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