A Parameter Identifying a Kalman Filter Observer for Vehicle Handling Dynamics

Hodgson, Graham; Best, Matt C.

doi:10.1243/09544070d18304

Cited by 18 publications

(9 citation statements)

References 2 publications

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“…Similar approaches have been taken by Shim et al [8] and Hong et al [9]. Hodgson and Best looked at using an EKF as well as an adaptive identifying Kalman Filter (IKF), where the state equations are augmented with road friction, to estimate the tyre forces and slip angles given the lateral acceleration and yaw rate [10]. The IKF outperformed the EKF in manoeuvres that saturated the tyres.…”

Section: Introductionmentioning

confidence: 95%

A high performance pneumatic braking system for heavy vehicles

Miller

Cebon

2010

Vehicle System Dynamics

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Section: Introductionmentioning

confidence: 95%

A high performance pneumatic braking system for heavy vehicles

Miller

Cebon

2010

Vehicle System Dynamics

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“…In [40], a new EKF estimation process was proposed in order to estimate vehicle sideslip angle, lateral tire forces, and TRFC by combining the single-track vehicle model and Burckhardt/Kiencke adaptive tire model that takes into account variations in road friction, evaluating it with two nonadaptive tire-based EKF estimators. An EKF method derived from the Pacejka tire model or modified Pacejka tire model has been proposed to estimate vehicle lateral states, tire-road forces, and TRFC [41,42,43,44]. By equipping additional GPS sensors [45,46,47], an EKF-based fusion methodology integrating in-vehicle sensors and single-frequency double-antenna GPS was developed in [46] to obtain reliable estimation about vehicle state information, such as vehicle sideslip and roll angle, while EKF estimation in [47] considered the vehicle sideslip angle and TRFC by fusing measurements of GPS and IMU.…”

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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“…S is considered null, as in most research papers on the matter 17 and also based on the analysis by Hodgson and Best. 18 The EKF performs a linearisation at each time step, approximating the non-linear model function f and the measurement function h using Jacobian matrices as defined by…”

Section: Structured Ekf For Grey-box Parametric Identificationmentioning

confidence: 99%

A new structure for non-linear black-box system identification using the extended Kalman filter

Bogdanski

Best

2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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The method works iteratively in the time domain using an Extended Kalman Filter. The model retains a state space structure in modal canonical form, which ensures that a minimal number of parameters need to be identified and also produces additional information in terms of system eigenvalues and dominant modes. This structure is completely black-box, requiring no physical understanding of the process for successful identification, and it is possible to easily expand the order and complexity of nonlinearities, whilst ensuring good parameter conditioning. A simple nonlinear example illustrates the method, and identification of a highly nonlinear brake model is also presented. These examples show the method can be applied as a mechanism for model order reduction; it is equally well suited as a tool for nonlinear plant system identification. In both capacities this new method is valuable, particularly as the generation of simplified models for the whole vehicle and its subsystems is an increasingly important aspect of modern vehicle design.

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A Parameter Identifying a Kalman Filter Observer for Vehicle Handling Dynamics

Cited by 18 publications

References 2 publications

A high performance pneumatic braking system for heavy vehicles

A high performance pneumatic braking system for heavy vehicles

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

A new structure for non-linear black-box system identification using the extended Kalman filter

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