Adap-tyre: DEKF filtering for vehicle state estimation based on tyre parameter adaptation

Ahangarnejad, Arash Hosseinian; Başlamışlı, S. Çağlar

doi:10.1504/ijvd.2016.078769

Cited by 14 publications

(19 citation statements)

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“…In the literature, there are several approaches, e.g., the use of linear models, Pacejka models (also known as Magic Formula models), alternative tyre models (e.g., rational tyre model [122], Dugoff model [123], Burckhardt model [124]). The use of a dynamic model can lead to a good VSA estimation, yet results are accurate only if the tyre model truly reflects the actual conditions.…”

Section: Vehicle Modelsmentioning

confidence: 99%

“…Unmodeled effects such as road conditions and tyre wear can dramatically worsen the reliability of the estimation [125]. Attempts to address this issue include algorithms providing an online update of tyre parameters (e.g., Pacejka coefficients, cornering stiffness, and rational tyre model coefficients, respectively [16,120,122]). …”

Section: Kalman Filtermentioning

confidence: 99%

“…Results showed an error under 5% in every tested manoeuvre, as well as benefits in terms of computational burden, hence its suitability for real-time applications. Ahangarnejad et al in [122] proposed a dual extended Kalman filter (DEKF) approach, where two EKFs are used in parallel. The first EKF is used to estimate the vehicles states, including VSA.…”

Section: Kalman Filtermentioning

confidence: 99%

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On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

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Section: Vehicle Modelsmentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

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On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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“…As standard KF, traditional EKF is also an infinitely increasing memory filter. When making the optimal estimation at the moment k, all the data before the moment k should be used [26]. It means that, with the increase of k, the proportion of old data in filter will become larger, while that of new data is smaller.…”

Section: Ekf Algorithm Of Limited Memorymentioning

confidence: 99%

Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

Huang

et al. 2019

Mathematical Problems in Engineering

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In this paper, a limited-memory adaptive extended Kalman Filter (LM-AEKF) to estimate tire-road friction coefficient is proposed. By combining extended Kalman filter (EKF) with the limited-memory filter, this algorithm can reduce the effects of old measurement data on filtering and improve the estimation accuracy. Self-adaptive regulatory factors were introduced to weigh covariance matrix of evaluated error. Meanwhile, measured noise covariance matrix was adjusted dynamically by fuzzy inference to accurately track the breaking status of system. Therefore, problems, including large filter error and divergence caused by incorrect model, can be solved. Joint simulation was conducted for the proposed algorithm with Carsim and Matlab/Simulink. Under the different road conditions, real-vehicle road tests were conducted in various working conditions for contrast with traditional EKF results. Simulation and real-vehicle road tests show that this algorithm can enhance the filter stability, improve the estimation accuracy of algorithm, and increase algorithm robustness.

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“…For example, [20] investigates articulated heavy-duty vehicles in conditions of limit of adhesion. This paper proposes a novel vehicle sideslip angle estimator, consisting of a simple single-stage EKF approach (differently from more complex approaches such as [22]) with the following main novelties: -the Rational tyre model [21][22] is adopted and its parameters are estimated and updated in real time, for a better accuracy of the estimator with respect to parametervarying estimators based on linear tyre models and estimators based on fixed tyre parameters; -the EKF performance is assessed on experimental data collected on a heavy-duty vehicle equipped with a sideslip angle sensor. The performance of the proposed algorithm is also compared to a similar approach using a linear tyre model (inspired to the recent paper [19]) and to a Rational modelbased filter with no tyre parameter update.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

2020

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Vehicle sideslip angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle safety systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle sideslip angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle sideslip angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle sideslip angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters. INDEX TERMS Kalman filter; sideslip angle; state estimation; Rational tyre model; vehicle dynamics. List of symbols Symbol Unit Quantity various Dynamic matrix m Vehicle semi-wheelbase m/s 2 Lateral acceleration various Matrix used to work out various Matrix used to work out N/rad Front cornering stiffness N/rad Rear cornering stiffness various Control input vector rad 2 Rational tyre model parameter N/rad Rational tyre model parameter

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Adap-tyre: DEKF filtering for vehicle state estimation based on tyre parameter adaptation

Cited by 14 publications

References 0 publications

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

Calculation Algorithm of Tire‐Road Friction Coefficient Based on Limited‐Memory Adaptive Extended Kalman Filter

Vehicle Sideslip Angle Estimation for a Heavy-Duty Vehicle via Extended Kalman Filter Using a Rational Tyre Model

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