Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm

Liu, Yahui; Li, T.; Yang, Yiyong; Ji, Xuewu; Wu, Jian

doi:10.1016/j.ymssp.2016.07.024

Cited by 114 publications

(60 citation statements)

References 38 publications

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“…This is significantly different from commonly adopted Taylor-series method in EKF [8] that require ignoring both second-order and higher-order error terms.…”

Section: Advances In Intelligent Systems Research Volume 134mentioning

confidence: 79%

“…Moving source localization and tracking using TDOAs and FDOAs is a challenging problem because of the high nonlinearity inherent in the measurement equations [3][4][5]. Extended Kalman filter (EKF) is known to be the well-known nonlinear estimator for systems with white target state and measurement noises [7][8][9][10][11][12][13]. Its efficiency and ease of implementation have made this estimator an essential tool for a wide class of applications such as signal processing, target tracking, sonar, navigation, wireless communication, and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

Deng¹,

He²,

Sun³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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“…This is significantly different from commonly adopted Taylor-series method in EKF [8] that require ignoring both second-order and higher-order error terms.…”

Section: Advances In Intelligent Systems Research Volume 134mentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

Deng¹,

He²,

Sun³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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show abstract

“…Among the papers consulted, the vast majority employ an analytical friction modelisation to estimate the coefficient µ max [9,11,17,35,37,39,40,87,88,107,[116][117][118][119][120][121][122][123]. In particular, the Brush tyre model has received greater attention on the works reviewed.…”

Section: Tyre Self-alignment Torquementioning

confidence: 99%

“…The observer exhibits a good performance and is able to infer the friction potential of high and low mu surfaces (µ max = 0.9, µ max = 0.4) for tyre friction levels ranging from µ = 0.6 to µ = 0.4. The Brush tyre model has been also employed by Liu et al in [123]. In particular, authors proposed a modified version of the previous model based on test data.…”

Section: Tyre Self-alignment Torquementioning

confidence: 99%

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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Abstract:In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

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“…The method uses a linear tire model and may become inaccurate when the slip ratio enters its nonlinear range. Estimation of the tireroad friction coefficient based on a combined APF-IEKF and iteration algorithm has been discussed in [9]. The tire-road friction parameter estimation using the extended Kalman filter is the subject of study in [1].…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road

Seyedtabaii

Velayati

2019

Automatika

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In this paper, an adaptive algorithm is developed which senses the road condition change and estimates a (time-varying) optimal braking slip ratio. This is conducted by two on-line simultaneously operating tire-road friction-curve slope calculators: one based on the accelerometer output and the other based on the wheel speed. The required vehicle speed is estimated using a robust sliding-mode observer. Enforcement of the online optimal braking reference is left to an adaptive sliding mode controller to cope with the system strong nonlinearity, time dependency and the speed and friction-coefficient estimation errors. The algorithm is applied to a half model car and the braking performance is examined. The results indicate that the proposed algorithm substantially reduces the stopping time and distance. The performance of the algorithm is verified using different vehicle initial speeds and especially non-uniform road condition where 8% improvement versus the nonadaptive optimal slip ratio algorithm is recorded. ARTICLE HISTORY

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Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm

Cited by 114 publications

References 38 publications

Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

Linear-correction Extended Kalman Filter for Target Tracking Using TDOA and FDOA Measurements

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

Adaptive optimal slip ratio estimator for effective braking on a non-uniform condition road

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