Vehicle Sideslip Angle and Road Friction Estimation Using Online Gradient Descent Algorithm

Chen, Wuwei; Tan, Dan; Zhao, Lian

doi:10.1109/tvt.2018.2875459

Cited by 54 publications

(26 citation statements)

References 19 publications

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“…Research on the stability region of a vehicle's sideslip angle phase plane trajectory has shown that a self‐stable region exists in the phase plane [25, 26]. However, studies have indicated that the speed of a vehicle, its road friction, and its front‐wheel angle affect its lateral stability [6, 10, 27]. Moreover, due to the high complexity and coupling of the vehicle dynamics system, the lateral stability of the vehicle is often the result of a combination of factors, rather than a single factor.…”

Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

“…The non‐linear 7‐DOF vehicle model (Fig. 1 b ) was used to analyse the stable region of the phase plane of the coach [6]. To determine the boundary value parameters of the stable region under the coupling conditions of the influencing factors, a BPNN model was established based on the boundary value parameter database obtained by influencing the multiple factors of the stable region.…”

Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

“…Significant research has been done on this [2–5]. In the work by Chen et al [6], an online gradient descent algorithm was proposed to estimate the vehicle sideslip angle and road friction. An unknown input observer was used to estimate the lateral force of the rear wheels.…”

Section: Introductionmentioning

confidence: 99%

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Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Zhang

Guo

et al. 2020

IET intell. transp. syst

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This study aimed to develop a coach state estimation and prediction system to enhance driving safety. Different from existing vehicle stability estimation studies, the authors propose a hybrid method to estimate and predict the state of a coach in real time. First, the vehicle sideslip angle and yaw rate are estimated by a three‐degrees‐of‐freedom vehicle model combined with an extended Kalman filter (EKF) estimation algorithm. Then, a steering system is established that replaces the front‐wheel angle with the steering wheel input torque. Next, a seven‐degrees‐of‐freedom vehicle model analyses the effects of various driving influencing factors on the vehicle sideslip angle and the boundary of the stable region of the phase plane of the vehicle sideslip angle rate, and a boundary value parameter database is obtained. A back propagation neural network (BPNN) model is then established to obtain the boundary function parameter values under multifactor coupling conditions. Furthermore, an online prediction of the steering wheel input torque in a time series is done, and the prediction value is input to the steering system and neural network model. The effectiveness of the proposed method was evaluated via simulations based on MATLAB/Simulink and TruckSim software.

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Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

Section: Phase‐plane Stable‐region Determination Of Sideslip Anglementioning

confidence: 99%

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Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Zhang

Guo

et al. 2020

IET intell. transp. syst

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“…According to the dynamic equation, the longitudinal acceleration a x and lateral acceleration a y can be calculated by the following formula [29], [30]:…”

Section: A Estimation Of Sideslip Anglementioning

confidence: 99%

Stability Research of Distributed Drive Electric Vehicle by Adaptive Direct Yaw Moment Control

et al. 2019

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As one of the active safety technologies, stability control of vehicles has recently received great attention. In order to improve the handling stability of distributed drive electric vehicles under various extreme conditions, a direct yaw moment control (DYC) method based on a novel fuzzy sliding mode control (FSMC) is proposed. First, a linear 2DOF reference vehicle model as ideal value reference, a 7DOF vehicle model used for sideslip angle estimation, an electric-driving wheel model used to provide tire motion parameters based on CarSim platform are established. Then, FSMC is designed as the core decision-making layer of the control method to calculate the required additional yaw moment on the premise of estimating the sideslip angle. Four hub motors are allocated by the distribution method based on axle load proportion. Finally, under two typical working conditions, the four hub motors are allocated. Compared with traditional sliding mode control (SMC), the results show that FSMC can not only maintain vehicle stability more effectively under different working conditions, but also greatly reduce the occurrence of buffeting phenomenon, which has practical significance for engineering applications. INDEX TERMS Direct yaw moment control, fuzzy sliding mode control, lateral stability, active safety control strategy, distributed drive electric vehicle.

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“…The study employed relatively severe double-lane change (DLC) and slalom steering inputs. Chen et al 27 proposed a novel online gradient descent algorithm for vehicle side-slip angle and road friction estimation, though the effectiveness has not yet been verified by experiments in real vehicle. Ahn et al 7 proposed an adaptive state and parameter observer to estimate friction coefficient and tire side-slip angle using the lateral acceleration and tire-aligning moment responses to a 0.25 Hz sinusoidal steering input, assuming negligible longitudinal tire force.…”

Section: Introductionmentioning

confidence: 99%

Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics

Rakheja

Zhang

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This study proposes a two-stage framework for real-time estimation of tire–road friction coefficient of a vehicle on the basis of lateral dynamics of the vehicle. The estimation framework employs a new cascade structure consisting of an extended Kalman filter and two unscented Kalman filters to reduce the computational burden. In the first stage, extended Kalman filter is utilized to estimate lateral velocity of the vehicle and thereby both the front and rear tires’ side-slip angles. In the second stage, a two–unscented Kalman filters sub-framework is formulated in sequence to observe both the front- and rear-axle tire forces, and to subsequently identify their respective tire–road friction coefficient, regarded as two unknown states. All the measured signals required in the study could be realized from the conventional on-board sensors. Typical double-lane change and single-lane change maneuvers were designed and the developed algorithm was verified through CarSim–MATLAB/Simulink software platform considering high-, mid-, and low-friction road conditions. The simulation results show that the proposed method can yield accurate and rapid estimations of the tire–road friction coefficient for mid- and low-friction road conditions even under a single-lane change maneuver, although double-lane change maneuver is needed to accurately estimate the tire–road friction coefficient for high-friction road condition.

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Vehicle Sideslip Angle and Road Friction Estimation Using Online Gradient Descent Algorithm

Cited by 54 publications

References 19 publications

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Real‐time estimation and prediction of lateral stability of coaches: a hybrid approach based on EKF, BPNN, and online autoregressive integrated moving average algorithm

Stability Research of Distributed Drive Electric Vehicle by Adaptive Direct Yaw Moment Control

Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics

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