A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

Gong, Xinle; Suh, Jeong-Se; Lin, Cheng

doi:10.1080/00423114.2019.1566559

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…where z k | k is a linear combination of states, and L k is defined as an identity matrix ( L k = I ) as the estimation errors are assumed to have equal weights (Gong et al, 2020).…”

Section: The H-infinity Extended Kalman Filtermentioning

confidence: 99%

“…Unlike the standard KFs, which require detailed knowledge of the noise, the H-infinity filters require no prior assumption of process and measurement noise (Simon, 2006). As a result, it is a more efficient algorithm than standard KFs (Gong et al, 2020). Multitude types of H-infinity filter solutions have been considered such as game theory (Shen and Deng, 1997), frequency domain (Grimble and El Sayed, 1990), and Krein -space approach in terms of the state-space form (Hassibi et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Yazdkhasti

Sabzevari

Sąsiadek

2022

Transactions of the Institute of Measurement and Control

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The optimal performance of the Kalman filters is highly dependent on the measurement and process noise characteristics, making the whole system unable to achieve the desired estimation in the presence of non-Gaussian mean noise distribution and high initial uncertainties. Recently, the H-infinity filter, as a robust algorithm, has been broadly used, as it is not being dependent on the pre-knowledge of the noise nature; however, making a balance between high robustness and estimation accuracy is a challenging issue. Hence, to overcome this problem, a new adaptive H-infinity extended Kalman filter (AHEKF) was designed in this paper, which benefits from both high robustness and precision. The suggested algorithm contains two adaptive sections to achieve high accuracy as well as controlling the effects of time-varying noise characteristics, high initial uncertainties, and abnormal data that can degrade the accuracy of state estimation in an integrated navigation system. The presented algorithm was used to integrate data from two independent sensors data. The simulation results for an inertial navigation system (INS)/global positioning system (GPS) sensor fusion are presented and compared with the standard H-infinity filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF) to show the effectiveness of the proposed algorithm. Evaluations demonstrate that the AHEKF achieves over 50% higher accuracy and robustness, and over 2.5 times faster convergence of estimation errors than the standard H-infinity filter.

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“…where z k | k is a linear combination of states, and L k is defined as an identity matrix ( L k = I ) as the estimation errors are assumed to have equal weights (Gong et al, 2020).…”

Section: The H-infinity Extended Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Yazdkhasti

Sabzevari

Sąsiadek

2022

Transactions of the Institute of Measurement and Control

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“…Remark 1: In this paper, the state estimators proposed by authors is employed to estimate the position of CG and the vehicle states. 37,38 Single wheel dynamic model…”

Section: Piecewise Affine Lateral Dynamics Modelmentioning

confidence: 99%

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

Lin

Liang

Gong

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve safety and stability of four-wheel independent drive electric vehicles (4WIDEVs) in extreme path tracking maneuvers, a hierarchical yaw stability control scheme based on predictive control is proposed in this work. The upper-level stability control strategy coordinates active front steering (AFS) and direct yaw moment control (DYC), yielding desired front steering angle and longitudinal force of each tire. The control task is formulated into a finite horizon optimal control problem, which is solved by hybrid model predictive control (hMPC) algorithm. The lower-level slip ratio control strategy calculates and tracks the optimal longitudinal slip ratio of each tire for implementation of the longitudinal tire force distributions in a wide range of road friction coefficient. Based on a qualified piecewise affine (PWA) approximation of lateral tire force models, the stable regions of vehicle states under multiple varying inputs are obtained by analyzing trajectories of fixed points. Furthermore, the control modes of the upper-level control strategy with different control inputs and state constraints are determined by judging bifurcation point of system. Finally, the proposed control approach is verified and compared through the dSPACE-based hardware-in-the-loop (HIL) experiments. The results indicate that the proposed yaw stability control strategy can improve yaw stability performance and reduce the tracking error significantly in extreme situations.

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“…Nowadays, modern road vehicles are using an increasing number of active systems to improve vehicle safety, passenger comfort, vehicle performance and energy efficiency. Advanced Driver Assistance Systems (ADAS), as well as Automated Driving (AD) technologies, are being increasingly implemented in vehicles, aiming for improved driving safety and passenger comfort [ 1 , 2 ]. In addition, the autonomous driving test rig is also an important method to test autonomous driving control algorithms (as shown in Figure 1 , it is an autonomous driving test rig proposed by our research group) [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method

et al. 2021

Sensors

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Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

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A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

Cited by 14 publications

References 37 publications

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method

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