Constrained nonlinear filter for vehicle sideslip angle estimation withno a priori knowledge of tyre characteristics

Strano, Salvatore; Terzo, Mario

doi:10.1016/j.conengprac.2017.10.004

Cited by 52 publications

(23 citation statements)

References 21 publications

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“…The Constrained Unscented Kalman Filter (CUKF) is used to improve the accuracy of estimation of the UKF, taking into account constraints of state variables. The CUKF is a two-step estimator [9] and uses the unscented transformation to propagate the state and the estimation error covariance of a non-linear system over time. The estimator design model is based on a half body railway vehicle model derived from a more complex full body railway vehicle model that takes into account the fundamental lateral and yaw dynamics and the longitudinal dynamic is neglected.…”

Section: Estimator Design Modelmentioning

confidence: 99%

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A Constrained Nonlinear Approach for Monitoring the Railway Anti-yaw Damping

Kaiser

Strano

Terzo

et al. 2020

Mechanisms and Machine Science

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In railway vehicles, the secondary anti-yaw suspension system is subjected to degradation so that it must undergo maintenance. In order to optimize the maintenance costs and to improve the safety, model-based real-time monitoring techniques are used. This paper presents a constrained nonlinear estimation procedure to monitor the anti-yaw damping through a railway vehicle model with a wheel-rail contact model characterized by a random walk approach. The application of state constraints allows to reduce the parameter variation sensitiveness. This technique considers the constraints applied to the estimated states and is advantageous in terms of unsensitiveness linked to the phenomenon of wheel-rail contact and a no prior knowledge of the track irregularities is required.

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Section: Estimator Design Modelmentioning

confidence: 99%

“…The UKF outperforms the extended Kalman filter (EKF) [8] but it could fail when the system model is inaccurate or when it contemplates random variables. Many approaches have been developed for UKF with constrained problems, also called constrained UKF (CUKF) [9]. The paper is organized as follows: Sect.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Nonlinear Approach for Monitoring the Railway Anti-yaw Damping

Kaiser

Strano

Terzo

et al. 2020

Mechanisms and Machine Science

Self Cite

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“…Considering the effect of model non-linearity, uncertainty, and road friction conditions, an adaptive variable structural UKF (AUKF) was studied in [57] to compensate the model uncertainty for vehicle sideslip angle estimation. The vehicle state estimation with AUKF addressed in [58] was a practical road influence of noise variance and covariance on the estimation accuracy of UKF, whereas the proposed constrained UKF (CUKF) technique in [59] fully took state boundaries, measurement noise, and nonlinearities in to account to prevent unphysical vehicle sideslip angle estimation. To address vehicle system un-modeled dynamics and nonlinearities, EKF and UKF techniques for vehicle sideslip angle and tire–road forces estimation in [60] were proposed and compared, and road results demonstrated that estimation performances of UKF were far better than EKF with respect to road variation, which was tested from an experimental car equipped with noncontact optical correvits.…”

Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

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“…Li et al [9] proposed an unscented KF‐based adaptive variable structural observer with dynamic correction for vehicle body side‐slip angle to tackle down the bias drift problem in the integral. Moreover, the authors of [5, 10, 11] also presented a series of estimation methods based on the modified KF. Besides, numerous model‐based and numerical method‐based systems have been developed to predict the side‐slip angle.…”

Section: Introductionmentioning

confidence: 99%