Vehicle dynamics estimation via augmented Extended Kalman Filtering

Reina, Giulio; Messina, Arcangelo

doi:10.1016/j.measurement.2018.10.030

Cited by 99 publications

(64 citation statements)

References 17 publications

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“…and from the fact that ̇ appears in the state vector. This is inspired from [19] and is not common in the literature. In alternative approaches that include the lateral acceleration in the measurement vector, the lateral acceleration explicitly depends on the control input, i.e.…”

Section: A Filter With Linear Tyre Model (Lint)mentioning

confidence: 99%

“…So the models should somehow account for changes in the tyre behaviour. Some interesting attempts in the literature propose estimators based on linear tyre models, with the estimator computing the cornering stiffness of each axle, that is updated in real time or according to rule-based criteria [5,[18][19]]. Yet, it is well known that linear tyre models are accurate only for relatively low values of tyre slip angle.…”

Section: Introductionmentioning

confidence: 99%

“…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. An EKF is preferred over an UKF structure due to the peculiarities of the problem at hand, including the need of ease of tuning and low computational burden [23], with the perspective of real-time application of the estimator for advanced real-time controllers, aimed at enhancing vehicle safety.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Filter With Linear Tyre Model (Lint)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2020

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“…Wind effects are estimated by observers, stochastic or deterministic as ( [24], while [25] presents an estimation strategy to compensate the external wrench exerted on the aerial multi-link robot while estimating external forces. Different state-estimation techniques as sliding-mode based observers [26] are also used to improve the performance of the system, however, due to its performance the Kalman Filter is widely used [27], [28], [29], [30], [31], [32], [33], [34], [35]. In this paper, we consider a multi-link aerial system that is intended to track a time-based trajectory while rejecting parametric and external disturbances during a multiple-load transportation task.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Castillo-Zamora

Escareño

Boussaada

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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The actual paper presents the modeling and control of a multi-link unmanned aerial system whose dynamics is computed by means of the Euler-Lagrange energy-based approach. The aforementioned system is subjected to lumped disturbances which comprise external disturbances and parametric uncertainties. An Augmented-State Extended Kalman Filter intended to estimate endogenous and exogenous uncertainties is conceived and trajectory-tracking controller fulfilling Lyapunov asymptotic stability is synthesized. A simulation stage is conducted to validate the effectiveness of the proposal.

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“…To overcome the drawbacks of the current sensors, many pieces of research have been conducted on the fusion of measurements from multiple heterogeneous sensors. Traditionally, Kalman filtering has been widely employed to fuse multiple sensors in the field of autonomous vehicles and simultaneous localization and mapping (SLAM) [ 17 , 18 , 19 ]. Two data fusion schemes are commonly used—loosely-coupled and tightly-coupled Kalman filters [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Kim

Sohn

2020

Sensors

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In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

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Vehicle dynamics estimation via augmented Extended Kalman Filtering

Cited by 99 publications

References 17 publications

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

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

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

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