Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Boada, María Jesús López; Díaz, V.

doi:10.1016/j.ymssp.2015.11.003

Cited by 123 publications

(87 citation statements)

References 30 publications

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“…An effective solution to reduce the computational burden while keeping a high accuracy in the VSA estimation is the use of the unscented Kalman filter (UKF) instead of the EKF [33][34][35][36][37][38][39]. As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

“…As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

“…Other observer-based methods are mainly based on hybridising the KF with various artificial intelligence (AI) strategies [16,37,46,47] to improve the observation model determination. The authors in [37] and [47] use ANFIS (Adaptive Network Fuzzy Inference System) respectively with UKF and EKF, while in [46] and [16], the modelling ability of a simple fuzzy logic inference system has been used to improve EKF estimation performances.…”

Section: Kalman Filtermentioning

confidence: 99%

“…The authors in [37] and [47] use ANFIS (Adaptive Network Fuzzy Inference System) respectively with UKF and EKF, while in [46] and [16], the modelling ability of a simple fuzzy logic inference system has been used to improve EKF estimation performances. In particular, Cheli et al [16] proposed a combined approach using a kinematic model in transient conditions, and a dynamic model in steady-state conditions.…”

Section: Kalman Filtermentioning

confidence: 99%

See 3 more Smart Citations

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: Kalman Filtermentioning

confidence: 99%

“…As a matter of fact, many recent observer-based studies on VSA estimation mostly focused on UKF [33,35,37,38].…”

Section: Kalman Filtermentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

See 2 more Smart Citations

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

2018

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“…What is more, uncertainties, such as δh out,k and δA out,k , are affecting the calculated values ofX k/k−1 ,Ẑ k/k−1 , andX k via Equations (15), (16) and (20), making standard UKF unsuitable for systems given by Equations (2) and (4) involving model mismatches.…”

Section: Problem Description Of the Filter For Systems Involving Modementioning

confidence: 99%

Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

et al. 2017

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Abstract:In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission.

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An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

Huang

Lei

Han

et al. 2019

Adaptive Control & Signal

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Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in-wheel-motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady-state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single-chip microcomputer verifies the strong real-time performance and easy-to-implement characteristics of the proposed algorithm. KEYWORDSadaptive adjustment, electric vehicle, in-wheeled motored, state estimation, unscented Kalman filter 1676

show abstract

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Cited by 123 publications

References 30 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

Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

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