A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

Zhang, Yixi; Ma, Jian; Zhao, Xuan; Liu, Xiaodong; Zhang, Kai

doi:10.1155/2021/8847075

Cited by 5 publications

(6 citation statements)

References 46 publications

(57 reference statements)

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“…It is evident from equations ( 22) and (40), if the process noise Q is inaccurate, the predicted state covariance P k and P k+1|k will be biased, and the resulting estimated P k and P k+1|k+1 to be biased. Thus the kalman gain K k and K k+1 will be inaccurate, resulting deteriorating the state estimate obtained from equations (24) and (46). And, Similarly for the measurement noise R. Thus it can be concluded that, without accurate system noise variance, the solution of filters may be deteriorated or divergent.…”

Section: Adaptive Filter Designmentioning

confidence: 99%

“…These sigma points are transformed by the non-linear function twice in each iteration, thus elapsing more computational time than RAEKF. Furthermore, to show the improvement in estimation accuracy, comparisons are also made between the proposed method and estimation methods proposed in the literatures [14,15,23,24]. The comparison is done for three transient steering angle inputs including DLC, Sine, and the Slalom maneuver.…”

Section: Slalom Maneuvermentioning

confidence: 99%

“…The results show an improvement in convergence speed and computational load has decreased. Similarly in [24] ALO based UKF is applied for the stability control of a four in-wheel motor independent drive electric vehicle and performance are found to be improved for the estimation of sideslip angle and yaw rate. In [25], authors have designed an evolutionary algorithms based model predictive control (MPC) for lateral stability, in which the initial states for MPC are assumed to be available, and further a default KFs are exploited to obtain states.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal

Prakash

Dheer

2023

Eng. Res. Express

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The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

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Section: Adaptive Filter Designmentioning

confidence: 99%

Section: Slalom Maneuvermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal

Prakash

Dheer

2023

Eng. Res. Express

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“…However, the posterior mean and covariance may be corrupted in EKF. e UKF [23], which is a derivative-free alternative to EKF, overcomes this problem by using a deterministic sampling approach. e state distribution is represented using a minimal set of carefully chosen sample points called sigma points.…”

Section: Unscented Kalman Filter Estimate Algorithmmentioning

confidence: 99%

A Novel Rollover Warning Approach for Commercial Vehicles Using Unscented Kalman Filter

Hou

Lei

Zhi-jun

et al. 2022

Mathematical Problems in Engineering

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Roll responses of the semitrailer and the tractor provide higher lead time and characterise the roll instability of the commercial vehicles subjected to directional manoeuvres at highway speeds. This paper proposes a novel rollover index based on the synthesized roll angles of the tractor and trailer. Owing to the poor measurability, the unscented Kalman filter (UKF) algorithm is used to estimate the roll angle of the track and trailer, respectively. Meanwhile, different weight coefficients are considered in the rollover index to eliminate the influence of mutual coupling between the tractor and the trailer and improve the accuracy of the warning. For the practical implementation of the algorithm, a two-stage rollover warning method triggered by the video and audio is finally proposed to reduce the possibilities of false warnings. Co-simulation is presented to prove the validity of the proposed rollover warning approach.

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“…Based on fusion of the machine learning model and the vehicle dynamics model, Yu et al proposed a vehicle mass estimation method for intelligent vehicles [6]. Zhang et al proposed a novel modified UKF state estimation methodology for the stability control of an electric vehicle [7]. To improve the safety and stability of land vehicles, Song et al explored the estimation problem for different vehicle states [8].…”

Section: Introductionmentioning

confidence: 99%

Moving horizon estimation of vehicle state and parameters

Liu¹,

Cui²,

Peng³

2022

J. vibroeng.

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For the active safety control of the vehicle, it is extremely important to estimate the vehicle state in real-time and accurately during the driving process. A joint state and parameter estimation method based on QR decomposition and receding horizon estimation (RHE) is proposed. Firstly, by introducing the receding horizon strategy, the authors optimized the state and parameter estimation with a fixed number of variables, which can better deal with the estimation problem of time-varying parameters. Then, based on the principle of forward dynamic programming, the calculation of arrival cost is transformed into a least square equation, which is solved by QR decomposition. At the same time, an update method of arrival cost based on QR decomposition is given. In this way, the whole receding horizon estimation method is based on the optimization, and the feedback mechanism is introduced to improve the estimation accuracy and speed. The simulation results show that the accuracy of receding horizon estimation is obviously better than that of unscented Kalman filter (UKF), and the arrival cost update method based on QR decomposition is more convenient than the traditional arrival cost update method based on error covariance estimation.

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A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

Cited by 5 publications

References 46 publications

Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal

Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal

A Novel Rollover Warning Approach for Commercial Vehicles Using Unscented Kalman Filter

Moving horizon estimation of vehicle state and parameters

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