Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

Liu, Yingjie; Cui, Dawei

doi:10.1155/2022/7355110

Cited by 6 publications

(9 citation statements)

References 15 publications

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“…From the table it can be seen that the proposed method has performed well and the difference between both the results are marginal. The yaw rate comparison between the simulation results obtained and the experimental results are shown in table 15. The difference between the two results is small.…”

Section: Slalom Maneuvermentioning

confidence: 89%

“…The output variables are considered as the longitudinal velocity, the lateral acceleration, and the yaw rate are measured by (15) expressed as:…”

Section: Tire Modelmentioning

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%

“…Authors of [15] have proposed a fading unscented kalman filter for vehicle state estimation utilising a 7 DOF model by considering only the process noise but not measurement noise, thus resulting in better accuracy and convergence speed. An adaptive hybrid fusion estimation strategy using low-cost sensors to estimate vehicle sideslip angle in a wide driving-maneuver range is proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

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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: Slalom Maneuvermentioning

confidence: 89%

“…The output variables are considered as the longitudinal velocity, the lateral acceleration, and the yaw rate are measured by (15) expressed as:…”

Section: Tire Modelmentioning

confidence: 99%

Section: Slalom Maneuvermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…An extended state observer is used to estimate the model error caused by linearization while reducing the calculation amount (Qu et al, 2020). S Lu et al (2020) propose a vehicle state estimation method with an unscented Kalman filter to obtain vehicle active safety control variables in real time accurately. However, these observers are greatly affected by nonlinearity; this paper constructs a novel fuzzy observation method based on the T-S and the EKF methods to achieve high-efficiency and high-precision online observation.…”

Section: Introductionmentioning

confidence: 99%

Integration sliding mode control for vehicle yaw and rollover stability based on nonlinear observation

Sun

Deng

et al. 2022

Transactions of the Institute of Measurement and Control

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An active rear steering and direct yaw moment (ARS-DYC) coordination control is conducted on the basis of nonlinear fuzzy observation to improve yaw and roll stability control of vehicles under extreme conditions. First, a Takagi–Sugeno (T-S) model of yaw and roll motion is established, and tire nonlinearity at emergency steering is approximated with sector domain. A nonlinear fuzzy observation model is established on the basis of the extended Kalman filter (EKF) to observe the sideslip angle, yaw rate, and roll state accurately under extreme conditions in real time. Second, an improved ideal yaw reference model under the T-S framework is constructed in accordance with the effect of yaw rate on roll stability, and the feedforward control of roll stability is achieved through yaw motion tracking. A fuzzy sliding mode controller is designed on the basis of nonlinear fuzzy observation considering tire sideslip stiffness and actuator constraints, and the coordinated control strategy of layered sliding mode surface for yaw and roll is constructed. Finally, the parameters of sub-sliding mode surface are adjusted with the 4-wire phase plane stability domain, the main sliding mode surface parameters are adjusted following the roll stability index, and the stability of the fuzzy sliding mode controller is proven in the Lyapunov framework. A hardware-in-loop simulation test is established with Carsim-Labview software, and results show that the proposed controller significantly enhances the yaw and roll stability of vehicles under the extreme steering process, due to the accuracy of nonlinear dynamic observation and the flexibility of layered sliding surface coordinated control.

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Optimal Lane Changing Problem of Vehicle Handling Inverse Dynamics Based on Mesh Refinement Method

Liu,

Cui,

Peng

2023

IEEE Access

Self Cite

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This paper proposes a new optimal control method to solve traffic accidents such as vehicle collisions during driver's conscious lane changing under the dangerous state of highway driving and meet the requirements of safety and comfort during vehicle lane changes. First, by directly analyzing the interpolation error and slope of discrete nodes, a new mesh refinement method was used to solve the vehicle optimal lane-changing problem. Then, a simulation of vehicle emergency left and right lane changes was established to verify the proposed method based on a nonlinear 7-DOF vehicle model. Finally, a virtual test using the CarSim software was conducted to verify the feasibility of the simulated results. The simulation results show that the mesh generated by the method in this study is smaller and requires fewer grid iterations, which can quickly and accurately solve the vehicle optimal lane-changing problem with constant components of the optimal control variables and fewer mesh iteration times and node numbers with high accuracy. And also, compared with the traditional Gaussian pseudospectral method(GPM), the solution accuracy of the mesh refinement method is higher than that of the traditional Gaussian pseudospectral method, indicating the superiority of the proposed method.INDEX TERMS Optimal control, vehicle lane changing, minimum time, mesh refinement.

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Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter

Cited by 6 publications

References 15 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

Integration sliding mode control for vehicle yaw and rollover stability based on nonlinear observation

Optimal Lane Changing Problem of Vehicle Handling Inverse Dynamics Based on Mesh Refinement Method

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